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Review On The Attacks And Security Protocols for Wireless
Sensor Networks
Ibrihich Ouafaa*1, Krit Salah-ddine2, Laassiri Jalal3 and El Hajji Said4 1Department of Mathematic Informatics, University Mohammed V-Agdal, BP 1040, Morocco
2Polydisciplinary Faculty of Ouarzazate, University Ibn Zohr, BP/638, Morocco 3Department of informatics, Faculty of Sciences, University Ibn Tofail, BP 33, Morocco
4Department of Mathematic Informatics, University Mohammed V-Agdal, BP 1040, Morocco
*[email protected];[email protected];[email protected];[email protected]
Tel: 1+212-78818946, 2+212-662685324, 3+212-661925021, 4+212-661495499
Abstract
Wireless Sensor Network has opened several research criteria related to social
security, data management, networking models, distributed system, agricultural aspects,
military supervision etc. With the increasing number of applications, an increment in
sensor network vulnerabilities has also become noticeably higher. Thus securing the WSN
has become a great challenge for the researchers. In this paper we explore the security
issues and challenges regarding WSN by classifying security attacks such as Sybil,
HELLO, Wormhole and Sinkhole attacks, reviewing proposed security mechanisms and
clarifying essential security requirements for specific security schemes. At last, a
comparison table is presented which illustrates the various properties held by these security
protocols, including authentication characteristics.
Keywords: WSN, Security protocols, attacks, Data aggregation, Key management.
1. Introduction WSN are composed of a large set (hundreds to a few thousand) of homogeneous nodes with
extreme resource constraints. Each sensor node has wireless communication capability plus some level
of intelligence for signal processing and data networking. These nodes are usually scattered over the
area to be monitored to collect data, process it, and forward it to a central node for further processing.
Military sensor networks might detect and gather information about enemy movements of people and
equipment, or other phenomena of interest such as the presence of chemical, biological, nuclear,
radiological, explosive materials. WSNs can support a myriad of uses including military, commercial,
environmental, and medical applications. Natural environments such as remote ecosystems, disaster
sites, endangered species, agriculture conditions, and forest fires can also be monitored with sensor
networks.
Several application specific sensor network data gathering protocols have been proposed in
research literatures. However, most of the proposed algorithms have given little attention to the related
security issues. In this paper we have explored general security threats in wireless sensor network and
made an extensive study to categorize available data gathering protocols and analyze possible security
threats on them.
2. Possible attacks against WSN Attacks against wireless sensor networks are categorized as invasive or non-invasive. Non- invasive
attacks generally consist of side channel attacks such as power, timing or frequency based attacks.
There is not much work published about side channel attacks that target WSN specifically, but many of
the problems found with other embedded systems, such as timing attacks against MAC generation or
encryption, could be used against sensor nodes. Invasive attacks are much more common and the more
important of these are described in the following sections. Several attacks on sensor networks are listed
as follows:
a. Denial-of-Service (DoS) attack:
In the denial-of-Service (DoS) attack, the hacker’s objective is to render target machines inaccessible
by legitimate users. There are two types of DoS attacks:
Passive attack: Selfish nodes use the network but do not cooperate, saving battery life for their own
communications, they do not intend to directly damage other nodes.
Active attack: Malicious nodes damage other nodes by causing network outage by partitioning while
saving battery life is not a priority. Dos attacks can happen in multiple WSN protocols layers. At
physical layer, the DoS attack could be jamming and tempering, at link layer, collision, exhaustion,
unfairness, at network layer, neglect and greed, homing, misdirection, black holes and at transport
layer, this attack could be performed by malicious flooding and de-synchronization. The mechanisms
to prevent DoS attacks include payment for network resources, pushback, strong authentication and
identification of traffic.
b. Attacks on Information in Transit:
The most common attacks against WSNs are on information in transit between nodes. Information in
transit is vulnerable to eavesdropping, modification, injection that can be prevented using well
established confidentiality, authentication, integrity and replay protection protocols. Traffic analysis
can potentially be a big problem in WSNs allowing an attacker to map the routing layout of a network,
enabling very tightly targeted attacks to disrupt chosen portions of a network for greatest effect.
c. Node Replication Attack:
A node replication attack involves an attacker inserting a new node into a network which has been
cloned from an existing node, such cloning being a relatively simple task with current sensor node
hardware. This new node can act exactly like the old node or it can have some extra behavior, such as
transmitting information of interest directly to the attacker. A node replication attack is serious when
the base station is cloned. However, as for many deployments, the base station is both in a secure
location and much more powerful than the rest of the sensor nodes, so cloning it is much more difficult.
d. Routing attack:
As with almost all networks there are a number of attacks that target the routing protocol of WSNs, all
of which are necessarily insider attacks. Some are as follows:
i. Selective forwarding:
Selective forwarding is a way to influence the network traffic by believing that all the participating
nodes in network are reliable to forward the message. In selective forwarding attack, malicious nodes
simply drop certain messages instead of forwarding every message. Malicious or attacking nodes can
refuse to route certain messages and drop them. If they drop all the packets through them, then it is
called a black hole attack. However, if they selectively forward the packets, then it is called selective
forwarding. Effectiveness of this attack depends on two factors. First the location of the malicious
node, the closer it is to the base station the more traffic it will attract. Second is the percentage of
messages it drops. When selective forwarder drops more messages and forwards less, it retains its
energy level thus remaining powerful to trick the neighboring nodes.
ii. Sinkhole attacks:
In sinkhole attacks, adversary attracts the traffic to a compromised node. The simplest way of creating
sinkhole is to place a malicious node where it can attract most of the traffic, possibly closer to the base
station or malicious node itself deceiving as a base station. One reason for sinkhole attacks is to make
selective forwarding possible to attract the traffic towards a compromised node. The nature of sensor
networks where all the traffic flows towards one base station makes this type of attacks more
susceptible.
Fig.1: Sinkhole attack
iii. Sybil attacks:
In Sybil attack, a single node presents multiple identities to all other nodes in the WSN. This may
mislead other nodes, and hence routes believed to be disjoint w.r.t node can have the same adversary
node. Sybil attacks can be used against routing algorithms and topology maintenance; it reduces the
effectiveness of fault tolerant schemes such as distributed storage and dispersity. Another malicious
factor is geographic routing where a Sybil node can appear at more than one place simultaneously.
iv. Wormholes:
In wormhole attacks, an adversary positioned closer to the base station can completely disrupt the
traffic by tunneling messages over a low latency link. Here an adversary convinces the nodes which are
multi hop away that they are closer to the base station. This creates a sinkhole because adversary on the
other side of the sinkhole provides a better route to the base station.
Fig.2: Normal Network (left), Wormhole Attack (right)
v. Flooding:
Sometime, the malicious node can cause immense traffic of useless messages on the network. This is
known as the flooding. Sometimes, malicious nodes replay some actual broadcast messages, and hence
generating useless traffic on the network. This can cause congestion, and may eventually lead to the
exhaustion of complete nodes. This is a form of Denial of Service attack. Security in wireless sensor
networks is a critical issue keeping in view limitations and application domains of sensor networks. In
sensor networks there is need to maintain a delicate balance between security and network operations.
The techniques such as Link Layer encryption and authentication, multipath routing, identity
verification and authenticated broadcast seem to be good solution for security in WSN. However
attacks such as Sinkhole and Wormholes pose lot of challenges to secure routing protocol design.
Geographical Routing Protocols is one example of routing protocols which are able to withstand most
of the WSN routing based attacks, as the legitimate nodes are able to estimate the location of the
adversary nodes. Hence attacks such as Sybil are effective. Effective and Efficient countermeasures are
still lacking against these attacks, which can be applied after the design of these routing protocols has
completed. So there exist a severe need to design such routing protocols in which these attacks are
ineffective.
vi. HELLO flood attack:
Many protocols require nodes to broadcast HELLO packets for neighbor discovery, and a node
receiving such a packet may assume that it is within (normal) radio range of the sender. A laptop-class
attacker with large transmission power could convince every node in the network that the adversary is
its neighbor, so that all the nodes will respond to the HELLO message and waste their energy. The
result of a HELLO flood is that every node thinks the attacker is within one-hop radio communication
range. If the attacker subsequently advertises low-cost routes, nodes will attempt to forward their
messages to the attacker. Protocols which depend on localized information exchange between
neighboring nodes for topology maintenance or flow control are also subject to this attack. HELLO
floods can also be thought of as one-way, broadcast wormholes. We can prevent this attack by
verifying the bi-directionality of local links before using them is effective if the attacker possesses the
same reception capabilities as the sensor devices. Another way by using Authenticated broadcast
protocols.
Fig.3: HELLO flood attack
vii. Black-hole attack:
The black hole attack positions a node in range of the sink and attracts the entire traffic to be routed
through it by advertising itself as the shortest route. The adversary drops packets coming from specific
sources in the network. This attack can isolate certain nodes from the base station and creates a
discontinuity in network connectivity. This attack is easier to detect than sinkhole attack. This attack
generally targets the flooding based protocols. Another interesting type of attack is homing. In a
homing attack, the attacker looks at network traffic to deduce the geographic location of critical nodes,
such as cluster heads or neighbors of the base station. The attacker can then physically disable these
nodes. This leads to another type of black hole attack. This attack aims to block the traffic to the sink
and to provide a better ground for lunching other attacks like data integrity or sniffing. This attack can
be prevented if we can restrict malicious node to join the network. Network setup phase should be
carried out in a secure way [1].
Fig.4: Black-hole attack
3. Secure Data Aggregation Since WSNs are energy constrained and bandwidth limited, reducing communications between sensors
and base stations has a significant effect on power conservation and bandwidth utilization. Aggregated
sensor networks serve this purpose. Data aggregation (or data fusion) is a process in which
intermediary nodes called “aggregators” collect the raw sensed information form sensor nodes, process
it locally, and forward only the result to the end-user. This important operation essentially reduces the
amount of transmitted data on the network and thus prolongs its overall lifetime [2]. This operation
cannot be efficiently done without being secured. Because of deployment environment, the physical
compromise of aggregators and some of the sensor nodes is possible. An active adversary can forge
[3], the home server to accept false aggregation results (Stealthy attacks), which are very much
different from the actual results determined by the measured values. The first line of defense against
threats is cryptographic mechanisms: integrity and confidentiality can be achieved using cryptographic
schemes.
1. Classification of Existing Secure Data Aggregation Schemes This section classifies the proposed secure data aggregation schemes into two models: the one
aggregator model and the multiple aggregator model. Under each model, each scheme is examined to
see whether it has a verification phase or not.
Fig.5: Classification of Existing Secure Data Aggregation Schemes
1.1. Single Aggregator Model
In this model, the aggregation process takes place once between the sensing nodes and the base station
or the external user. In other words, all individual collected data in the WSN travels to only one
aggregator point in the network before reaching the querier. This aggregator node should be powerful
enough to perform the expected high computation and communication. The main role of the data
aggregation might not be satisfied fully since redundant data will still travel in the network for a while
until they reach the aggregator as in Fig5. This model is useful when the network is small or when the
querier is not in the same network. However, large networks are not suitable places to implement this
model especially when data redundancy at the lower levels is high. The data aggregation schemes that
fit in this model can be divided into two categories: whether they have a verification phase or not. a. Verification Phase
Informs on the secure data aggregation schemes that aggregate data once in its way to the querier. This
phase enhances the querier’s ability to distinguish between the valid and invalid aggregated readings. RA: Resilient aggregation in sensor networks (2004)
Wagner (2004) proposed a mathematical framework (RA) for evaluating the security of several
resilient aggregation techniques. The paper measures how much damage an adversary can cause by
compromising a number of nodes and then using them to inject erroneous data. Wagner described a
number of better methods for securing the data aggregation such as how the median function is a good
way to summaries statistics. Furthermore, Wagner claimed that trimming and truncation can be used to
strengthen the security of many aggregation primitives by eliminating possible outliers. However, this
work only focused on examining the received aggregated data (at the base station) without studying
how these data are aggregated. Thus, when the network size increases, the communication cost will be
very high for the transmission of all the sensor readings to the base station. Moreover, eliminating
abnormal data with no further reasoning is impractical especially for applications such as monitoring
bush-fire. SIA: Secure Information Aggregation in WSNs (Przydatek et al.) 2003
Przydatek et al. (2003) proposed a secure information aggregation (SIA) framework for WSNs called
aggregate-commit-prove. This framework provides resistance against a special type of attack called
stealthy attacks aggregate manipulation where the attacker’s goal is to make the user accept false
aggregation results without revealing its presence to the user. It consists of three node categories: a
home server, a base station, and sensor nodes. SIA assumes that each sensor has a unique identifier and
shares a separate secret cryptographic key with both the home server and the aggregator. The keys
enable message authentication and encryption if data confidentiality is required. Moreover, it assumes
that the home server and base station can use a mechanism, such as μTESLA (Perrig et al. 2002), to
broadcast authentic messages. SIA consists of three parts: collecting data from sensors and locally
computing the aggregation result, committing to the collected data, and reporting the aggregation result
while proving the correctness of the result. SIA offers data integrity, authentication, data freshness, and
confidentiality (if required). WDA: Secure a witness-based approach for data fusion assurance in wireless sensor network (2003)
A witness based data aggregation (WDA) scheme for the WSN is being proposed by Du et al. (2003)
to assure the validation of the data sent from an aggregator node to the base station. In order to prove
the validity of the aggregated result, the aggregator node has to provide proofs from several witnesses.
A witness is one who also performs data aggregation like the aggregator node, but does not forward its
result to the base station. Instead, each witness computes the message authentication code MAC) of the
result and then sends it to the aggregator node which must forward the proofs to the base station. WDA
offers only integrity property to the data aggregation security and this is required to send multiple
copies similar to the original aggregated result, to the aggregator point. Thus, the aggregator point must
forward these reports as well as the aggregated result to the base station. Since the aggregator point is
fixed and responsible to handle so much traffic, the aggregator resources will not last long. b. No Verification Phase
Informs on the secure data aggregation scheme that does not contain a verification phase because data
integrity has not been considered by the scheme’s designers. In other words, the type of expecting
adversary is honest but has some interest in knowing about sensitive information while the one in the
previous phase is not honest and can inject false readings. SecureDAV: A secure Data Aggregation and Verification Protocol for Wireless Sensor Networks (2004)
SecureDAV (Mahimkar & Rappaport 2004) improved the data integrity vulnerability in SDA and ESA
by signing the aggregated data. In SecureDAV, each sensor within a cluster will have its share of its
secret cluster key and then it will be able to generate a partial signature on the aggregated data. Once
an aggregator receives sensor readings in the same cluster, it aggregates them and broadcasts the
average value of the readings. Each sensor in the cluster compares its reading with the average value
received from the aggregator. Then, it partially signs the average value only and only if the difference
between the received average value and its reading is less than a certain value (threshold). Then, the
aggregator (cluster-head) combines partial signatures to form a full signature of the aggregated results
and sends it to the base station. SecureDAV provides data confidentiality, data integrity, and
authentication. The drawbacks of this scheme are: it requires high communication costs on data
validation, and supports only the AVG aggregation function.
1.2. Multiple Aggregator Model
In this model, collected data in the WSN are aggregated more than one time before reaching the last
destination (querier). This model achieves greater reduction in the number of bits transmitted within
the network especially in the large WSNs, as illustrated in Fig5. The importance of this model appears
as the network size is getting bigger especially when data redundancy at the lower levels is high. The
data aggregation schemes that fit in this model can be divided into two categories: whether they have a
verification phase or not. a. Verification Phase
Secure data aggregation scheme that contains a verification phase to enhance the querier ability in
distinguishing between the valid and invalid aggregated readings. This phase is more complicated than
the same phase in the single aggregator model since the data is aggregated many times at different
aggregation points. The querier is interested to know whether the final aggregated result is altered or
not by one of these points [4]. SDAP: Secure hop-by-hop Data Aggregation Protocol for sensor network (2006)
Yang et al. (2006) proposed a secure hop-by-hop data aggregation protocol (SDAP) that can tolerate
more than one compromised node. SDAP is based on two principles: divide-and-conquer and commit-
and-attest. In order to reduce the damage caused by compromising an aggregator at a high level in the
per-hop aggregation scheme, SDAP uses the divide-and-conquer principle to divide the network tree
into multiple logical sub trees which increases the number of aggregators and reduces the number of
nodes in each sub tree. Consequently, the damage caused by compromising an aggregator of a sub tree
is reduced. The other principle, that is commit-and-attest, enhances the ordinary hop-by-hop
aggregation scheme by adding a commitment property, and helps the base station to prove the
correctness of the aggregated data. Once an aggregator of a logical sub tree commits its aggregation
result, it cannot deny it later on. This scheme needs to send much data to ensure reasonable level of
security. SHDA: Secure Hierarchical in-network Aggregation in sensor networks (2006)
Furthermore, Chan et al. (2006) extended the work in SIA by applying the aggregate-commit-
prove framework in fully a distributed network instead of single aggregator model. In general, this
scheme (SHDA) offers exactly what the SIA does data integrity, authentication, and confidentiality.
Each parent sensor performs an aggregation function whenever it has heard from its child nodes. In
addition, it has to create a commitment to the set of the input used to compute the aggregated result by
using a Merkle hash tree. Then, it forwards the aggregated data and the commitment to its parent until
it reaches the base station. Once the base station received the final commitment values, it rebroadcasts
them into the rest of the network in an authenticated broadcast. Each node is responsible for checking
whether its contribution was added to the aggregated data or not. Once its readings are added, it sends
an authentication code to the base station where the authentication code for node R is MACKR
(NkOK). For communication efficiency, the authentication codes are aggregated along the way to the
base station. However, missing one authentication code for any reason leads the base station to reject
the aggregated result. Furthermore, noticeable delay, too much transmission and computation will be
added as consequences of adding security to the scheme. SDA: Secure data aggregation (2003)
The first secure data aggregation (SDA) was proposed by Hu & Evans (2003) who studied the
problem of data aggregation once one node is compromised. This protocol achieves resilience against a
node compromise by delaying the aggregation and authentication at the upper levels. Therefore,
sensors measurements are forwarded unchanged and then aggregated at the second hop instead of
aggregating them at the immediate next hop. Thus, the sensor needs to buffer the data to authenticate it
once the shared key is revealed by the base station. Moreover, the proposed scheme only offers data
integrity, freshness and authentication. Even though it increases the confidence in the sensor readings
integrity the data can be altered once a parent and child in the hierarchy are compromised. Once a
compromised node is detected, no practical action is taken to reduce the damage caused by this
compromise which affects the data availability in the network. Much worse, once a grandfather node
detects a node compromise, it could not decide whether the cheating node is the child or the
grandchild. In addition, SDA scheme is improved in ESA by Jadia & Mathuria (2004). Instead of using
μTESLA to authenticate the base stations broadcast in the validation process to reveal the shared key
with sensors, the authors used one-hop pairwise keys (to encrypt data between a node and its parent)
and two-hop pairwise keys (to encrypt data between a node and its grandparent). This will improve the
secure aggregation scheme by adding data confidentiality and reducing the memory overhead since
data does not need to be stored until the key is revealed. However, the system will still break as soon as
two consecutive nodes in the hierarchy are compromised. b. No Verification Phase
Informs on the secure data aggregation scheme that does not contain a verification phase because data
integrity has not been considered by the scheme’s designers. ESPDA: Energy Efficient and Secure Pattern-based data aggregation for wireless sensor networks (2003)
Çam et al. proposed an energy-efficient secure pattern-based data aggregation (ESPDA)
protocol for wireless sensor networks. ESPDA is applicable for hierarchy-based sensor networks. In
ESPDA, a cluster head first requests sensor nodes to send the corresponding pattern code for the
sensed data. If multiple sensor nodes send the same pattern code to the cluster head, only one of them
is permitted to send the data to the cluster head. ESPDA is secure because it does not require encrypted
data to be decrypted by cluster-heads in order to perform data aggregation. SRDA: Secure Reference-based Data Aggregation protocol for wireless sensor networks (2004)
Sanli et al. (2004) developed a new data aggregation technique called the Secure Reference-
Based Data Aggregation scheme (SRDA) that sends only the difference between sensed data and the
reference value (called differential value) instead of raw data. Deference value is taken as the average
value of previous sensor readings. In SRDA scheme, each sensor computes the differential data (sensed
data -reference value), encrypts it, and then sends it to the cluster-head. The authors claim that the
security level of the network should be gradually increased as the data is traveled to higher level
cluster-heads. Therefore, they suggest using a cryptographic algorithm (RC6) with adjustable
parameters such as the number of rounds, to achieve different level of security in the WSN. Increasing
or decreasing the number of rounds changes the security strength of the RC6 that can be measured by
the security margin. The security margin is the deviation of the actual number of rounds from the
minimum number of rounds for which the algorithm is considered to be secured. The SRDA uses a
higher security margin at higher level cluster-heads compared to low level cluster-heads. CDA: Concealed Data Aggregation for reverse multicast traffic wireless sensor networks (2005)
Concealed data aggregation (CDA) [5], [6] is based on the symmetric additive privacy
homomorphism proposed by Domingo-Ferrer [7]. In this approach, every sensor node shares a same
key with the base station. So it does not guarantee privacy of individually sensed data from other
sensor nodes. Because one compromised sensor leads to the decryption of every sensor data. In this
approach ,each sensor node splits its data into ‘d’ parts (d ≥ 2) and encrypt them by using common key
shared with the base station and send to aggregator .Aggregator aggregate the encrypted sensor data
with other sensors encrypted data because of privacy homomorphism property and finally send the
aggregated result to the sink. At the sink, aggregated data is decrypted using the same key used for the
encryption. Disadvantages of this technique are vulnerability to reply attack and malicious aggregation,
size grow, and efficiency and also this technique do not address the problem of non-response ID. SELDA: Secure and reliable data aggregation for wireless sensor networks (2007)
In SELDA [8], to develop trustworthiness for environments and neighboring nodes, action of
the neighboring nodes are observed by the sensor nodes. Aggregators consider sensor node’s reading
received using the web of trust to enhance the reliability of aggregated data. If any aggregator is under
the denial-of- service attack, then it can be detected using the monitoring mechanism. It ensures data
integrity and source authentication but it does not provide data confidentiality. CPDA: Concealed data aggregation in heterogeneous sensor networks using privacy homomorphism (2007)
The basic idea of CPDA is to introduce noise to the raw data sensed by the sensor nodes in a
WSN, such that an aggregator can obtain accurate aggregated information but not individual sensor
data (He et al., 2007). This is similar to the data perturbation approach extensively used in privacy-
preserving data mining. However, unlike in privacy-preserving data mining, where noises are
independently generated (at random) leading to imprecise aggregated results, the noises in CPDA are
carefully designed to leverage the cooperation between different sensor nodes, such that the precise
aggregated values can be obtained by the aggregator. The CPDA protocol classifies sensor nodes into
two types: cluster leaders and cluster members. There is a one-to-many mapping between the cluster
leaders and cluster members. The cluster leaders are responsible for aggregating data received from the
cluster members. For security, the messages communicated between the cluster leaders and the cluster
members are encrypted using different symmetric keys for each pair of nodes. RSDA: Reputation-based Secure Data Aggregation in wireless sensor networks (2008)
In SRDA [9], sensors send differential sensing data instead of raw sensed data by comparing
raw data sensed by sensor to the reference data. So it reduces the number of bits transmitted from
sensor node to cluster head. So it improves energy consumption. To increase security levels by going
from lower level to higher level, SRDA uses one algorithm with security margin as adjustable
parameter. Security is calculated based on number of hops from the base station. First step is the
transmission of raw sensed packet in a session to cluster head by a node (leaf/cluster head) reporting to
higher level cluster head. Then cluster head create reference entry for that node. Sensor node sends
differential data to cluster head for subsequent readings. Finally when the session ends for a sensor
node, cluster head removes the reference entry for the node from the cluster head. This method is
independent of clustering scheme so this method can be applied on any level. When the reference value
is greater than differential value, then the efficiency of the scheme will increase.
Table 1: Comparison between different secure data aggregation schemes
4. Key management protocols The Sensor nodes cannot practically use a third party trusted server because of the high communication
cost and deployment cost. The Public Key protocols involve high computation cost. Hence the
Symmetric Key Cryptography involving is considered to be the better method of cryptography system
in WSN. Sensor network dynamic structure, easy node compromise and self-organization property
increase the difficulty of key management and bring a broad research issues in this area. Due to the
importance and difficulty of key management in WSNs, there are a large number of approaches
focused on this area. Based on the main technique that these proposals used or the special structure of
WSNs, we classify the current proposals as key pre-distribution schemes, hybrid cryptography
schemes, one way hash schemes, key infection schemes, and key management in hierarchy networks,
though some schemes combine several techniques [10].
Fig.6: Key management protocols in WSNs: a taxonomy
1. Key management based on network structure
1.1. Centralized key scheme
In this type of key management, some use the physical hierarchical structure of networks, while others
implement their hierarchy key management logically in physical flat structure sensor networks[11],
which only include a base station and sensors. For example, LKHW (Logical Key Hierarchy for
Wireless sensor networks), proposed by Pietro [12], integrates directed diffusion and LKH (Logical
Key Hierarchy) where keys are logically distributed in a tree rooted at the key distribution center
(KDC). A key distribution center maintains a key tree that will be used for group key updates and
distribution, and every sensor only stores its keys on its key path, i.e. the path from the leaf node up to
the root. In order to efficiently achieve confidential and authentication, they apply LKHW: directed
diffusion sources are treated as multicast group members, whereas the sink is treated as the KDC.
1.2. Key pre-distribution schemes
In the key pre-distribution schemes, sensor nodes store some initial keys before they are deployed.
After deployed, the sensor nodes can use the initial keys to setup secure communication. This method
can ease key management especially for sensor nodes that have limited resource. Two types of key pre-
distribution schemes suited for WSNs have been developed: random key pre-distribution and
deterministic key pre-distribution.
a. Random Key Pre-distribution
The Random Key Pre-Distribution (RPK) [13] guarantees secure authentication among nodes
through the three-step process of random key pre-distribution, shared key discovery, and at key
establishment. Because connection weight is determined probabilistically in RPK, the entire graph
representing WSN may not be connected completely and this problem is even more serious if sensor
nodes are deployed irregularly or there are physical obstacles to communication in the environment. In
particular, the size of key ring to be stored in each node has to be enlarged in order to increase network
connection weight, and this enables a malicious attacker to get more keys through node compromise.
In order to solve this problem, a method that utilizes information on sensor node deployment was
proposed, but it still has the problem that a malicious attacker can use a key obtained from node
compromise in other areas of the sensor network [14]. In addition, this method does not consider
security analysis, through which compromised nodes can tap or hide compromise efficiently through
mutual cooperation. However, the key pre-distribution scheme is advantageous in that when mobility
such as insertion of new nodes or cluster change of existing nodes has been assigned it can form a
cluster for secure communication using the shared key owned by each node. b. BROadcast Session Key (BROSK) Negotiation Protocol
BROSK is a new protocol: each node can negotiate a session key with its neighbors by
broadcasting the key negotiation message. BROSK uses a fully ad-hoc scheme to negotiate the session
key and can perform this key negotiating process efficiently. Moreover the scalability of BROSK is
significant especially when applied to large scale sensor networks. 2. Key management on probability of key sharing
The key management protocols for WSNs may be classified on the probability of key sharing between
a pair of sensor nodes. Depending of this probability the key management schemes may be either
deterministic or probabilistic [15].
2.1. Deterministic key distribution schemes a. LEAP: Localized Encryption and Authentication Protocol (2003)
The localized encryption and authentication protocol (LEAP) proposed by Zhu et al [16] is a key
management protocol for WSNs based on symmetric key algorithms. It uses different keying
mechanisms for different packets depending on their security requirements. Four types of keys are
established for each node:
i. an individual key shared with the base station (pre-distributed),
ii. A group of key shared by all the nodes in the network (pre-distributed),
iii. Pair-wise key shared with immediate neighbor nodes,
iv. A cluster key shared with multiple neighbor nodes. The pair-wise keys shared with immediate
neighbor nodes are used to protect peer-to-peer communication and the cluster key is used
for local broadcast. b. PIKE: Peer Intermediaries for Key Establishment
Peer Intermediaries for Key Establishment (PIKE) (proposed by Chan and Perrig) is a deterministic
key establishment scheme that uses peer sensor nodes as trusted intermediaries for key establishment.
It is designed to address several shortcomings of the existing symmetric-key distribution schemes.
PIKE can establish keys between nodes regardless of network topology or node density. This scheme is
designed to incur sub-linear overheads in memory per node and focused communication load per node
while retaining the property of resilience against the compromise of a fraction of the network. c. EPKEM: Efficient Pairwise Key Establishment and Management Scheme
Cheng et al. proposed an efficient pairwise key establishment and management scheme (EPKEM) in
[17]. In this scheme, a two-dimensional key matrix is constructed to pre-distribute symmetric keys into
sensors. Each sensor stores a row and a column from the key matrix. EPKEM guarantees every two
nodes share at least two common keys after the deployment. Combined with the identities of the
communicating parties, EPKEM can establish a distinct pairwise key for each pair of sensors.
Although Cheng et al.’s scheme can provide better network resilience than previous schemes; it still
has some limitations when used for large-scale WSNs. The communication overhead is still high,
sensors need to store too many keys in the network initialization phase.
d. Energy Efficient Session Key Establishment (EESK):
In EESK only polynomial shares are pre-loaded in CHs. Any two CHs need to setup a unique session
key between them before they exchange the sensitive information. There is no group key exists in
EESK, any communication between CHs need to be encrypted by the intended session key. Therefore,
any CH’s compromise does not affect the communication between non compromised CHs. According
to security property of t -degree bivariate polynomial, EESK can guarantee the network’s security
when there is no more than t CHs are compromised. Furthermore, in our network model, the CHs have
considerably high energy and memory storage. By setting t > m (where m is the number of cluster head
nodes in a network), we can guarantee that even all the CHs are compromised, the coefficients of the
polynomial are still keep secret to the adversary. e. LEKM:
In LEKM [18], all secret keys are pre-loaded in cluster heads (CHs) on the network initialization
phase, and each CH stores (n /m) keys in its memory. Once a CH is captured in this phase, all its stored
keys could be compromised. Furthermore, a group key is used in LEKM to secure the communication
among CHs, which also could lead to the single-point failure attack in WSNs. Any single CH’s capture
could compromise all the communication between non-compromised CHs. If this case happened in the
initialization phase, a malicious node can track all the exchanged key information between CHs, and
break the entire network lately.
Fig.7: Fraction of compromised keys in non-captured sensor nodes vs. number of compromised sensor nodes
Fig.8: Communication overhead vs. Sensor node addition
Fig.7, 8 shows that that EPKEM has the lowest communication overhead since the new nodes only
need to exchange key information with their one-hop neighbors. Random key pre-distribution schemes
have the highest overhead, since the new nodes have to exchange key information with all the
neighbors to establish a secure link. LEKM and EESK have lower communication overhead than
random key pre-distribution schemes since the new nodes only need to exchange key information with
their cluster heads. EESK can reduce 25%communication overhead than LEKM since there is no key
re-broadcast procedure involved.
2.2. Probabilistic key distribution schemes
The mechanism has three phases: key pre-distribution, shared key discovery, and path key
establishment. In the key pre-distribution phase, each sensor is equipped with a key ring stored in its
memory. The key ring consists of k keys which are randomly drawn from a large pool of P keys. The
association information of the key identifiers in the key ring and sensor identifier is also stored at the
base station. Each sensor node shares a pair-wise key with the base station. In the shared key discovery
phase, each sensor discovers its neighbors with which it shares keys. The authors have suggested two
methods for this purpose. The simplest method is for each node to broadcast a list of identifiers of the
keys in their key rings in plaintext allowing neighboring nodes to check whether they share a key.
However, the adversary may observe the key-sharing patterns among sensors in this way. The second
method uses the challenge-response technique to hide key-sharing patterns among nodes from an
adversary. Finally, in the path key establishment phase, a path key is assigned for those sensor nodes
within the communication range and not sharing a key, but connected by two or more links at the end
of the second phase. If a node is compromised, the base station can send a message to all other sensors
to revoke the compromised node’s key ring. Re-keying follows the same procedure as revocation. The
messages from the base station are signed by the pair-wise key shared by the base station and sensor
nodes, thus ensuring that no adversary can forge a station. If a node is compromised, the attacker has a
probability of approximately k/P to attack any link successfully. Because k << P, it only affects a small
number of sensor nodes. a. Q-Composite Key:
Q-Composite key scheme offers greater resilience against node capture when the number of nodes
captured is small. When a large number of nodes are compromised q-composite schemes tend to reveal
larger fractions of the network to the intruder. A small scale attacks will not have any effect as the
amount of additional information revealed (with such an attack) about the rest of the network is
minimal. A drawback of this scheme is that, it offers no resistance against node replication because
there is no limit on the number of times each key can be used and node degree is not considered.
However, this scheme supports node revocation via a trusted base station similar to the approach used
in the basic scheme.
5. Secure Localization In a WSN, sensors can be randomly distributed in order to collect data from a site. Knowledge of the
position of the sensing nodes in a WSN is an essential part of many sensor network operations and
applications. Sensors reporting monitored data need to also report the location where the information is
sensed, and hence, sensors need to be aware of their position. In addition, many network protocols such
as routing require location information in order to provide the specific protocol service. Localization
systems can be divided into three distinct components as Distance/angle estimation, Position
computation and Localization algorithm and attacks on these three different areas are discussed in [19].
Currently, most of current proposals are suitable for static WSNs. Secure location algorithms for
mobile WSNs in different environments need to be investigated.
6. Secure Routing Secure routing is vital to the acceptance and use of sensor networks for many applications, but many
sensor network routing protocols have been proposed, but none of them have been designed with
security as a goal. WSNs use multi-hop routing and wireless communication to transfer data, thus incur
more routing attacks. Security attributes are the mechanisms that allow the routing protocols to defend
against the possible threats in the whole network. These attributes consist of identity verification, bi-
directionality confirmation, topology structure restriction, base station decentralization and braided and
multi-path transmission. a. SPINS: Security Protocol for Sensor Networks
Perrig et al. (2002) proposed Security Protocols for Sensor Networks, SPINS, a suite of security
protocols optimized for sensor networks. It consists of two secure building blocks SNEP and µTESLA,
which run on top of TinyOS, a small, event driven operating system for sensor nodes. Secure Network
Encryption Protocol, SNEP, is used to provide confidentiality through encryption and authentication,
in addition to integrity, using a message authentication code (MAC).
There are a number of unique advantages with SNEP. It has a very low communication
overhead, adding only 8 bytes per message. SNEP achieves semantic security (a property which
prevents an adversary from learning even partial information about a transmitted message), which is an
important security property, as it prevents eavesdroppers from inferring the message content from the
encrypted message; achieved as the counter value is incremented after each message, implying that the
message is encrypted differently each time. The counter value is sufficiently long enough never to
repeat within the lifetime of the node. Finally, it also provides data authentication, replay protection
and weak message freshness. To achieve data authentication, the same block cipher is used as in CBC-
MAC mode.
µTESLA is the “micro” version of TESLA (Timed Efficient Stream Loss-tolerant
Authentication) proposed by Perrig et al in 2002. It emulates asymmetry through a delayed disclosure
of symmetric keys and serves as the broadcast authentication service of SNEP. µTESLA relies solely
on this delayed disclosure, unlike its predecessor, which authenticates the initial packet using the
digital signature. It has been argued that while symmetric key techniques are attractive, due to their
energy efficiency, limitations have been exhibited in the flexibility of these symmetric key exchange
protocols. µTESLA requires that the base station and the nodes be loosely time synchronized, and that
each node knows an upper bound on the maximum synchronization error. For an authenticated packet
to be sent, the base station computes a MAC on the packet with the key that is secret at that point in
time. When a node gets a packet, it can confirm that the base station did not yet disclose the
corresponding MAC key, using its loosely synchronized clock, maximum synchronization error and
the time at which the keys are to be disclosed. The node stores the packet in a buffer, aware that the
MAC key is only known to the base station, and that no adversary could have altered the packet during
transmission. When the keys are to be disclosed, the base station broadcasts the key to all receivers.
The receiver can then verify the correctness of the key and use it to authenticate the packet stored in the
buffer. Each MAC key is a member of a key chain, which has been generated by a one-way function F.
In order to generate this chain, the sender chooses the last key K of the chain randomly, and applies F
repeatedly to compute all other keys: nKi = F(Ki+1).
Applying the SNEP building block, each node can easily perform time synchronization and
retrieve an authenticated key from the chain for the “commitment in a secure and authenticated
manner”.
Schemes, like µTESLA, based on delayed key disclosure, can suffer from denial of service
attacks DOS. In the subsequent interval when the message is in the buffer and the receiver waits on the
disclosure time, an attacker can flood the network with arbitrary messages, claiming that they belong to
the current time interval. Only in the next time interval can the nodes determine that these messages are
not authentic. This type of attack can lead to buffers overflowing in the nodes and battery exhaustion as
all messages are forwarded to the nodes. The use of public key cryptography would eliminate the need
for such complicated protocols, increasing the security of the system, and only requiring the public key
of the base station to be embedded into all of the nodes. b. ZigBee Security
ZigBee uses all of the basic security elements of the IEEE 802.15.4 standard. In addition, the
ZigBee security specification employs a simpler and unified mode of operation of CCM (this modes in
an amalgamation of both the encryption and authentication suites listed above), defines key types
(Master, Link, Network) and describes key setup and maintenance (Commercial, Residential).
Additionally, ZigBee provides freshness through the use of freshness checks. These checks
prevent replay attacks, as ZigBee devices maintain incoming and outgoing freshness counters.
Whenever a new key is created, the counters are reset. It is postulated that devices that communicate
once per second will not overflow their freshness counters for 136 years. Under the ZigBee
specification, authentication is defined to provide assurance about the originator of a message. This
prevents an attacker from mimicking the operation of another device in any attempt to compromise the
network.
Authentication is possible at both the network level and the device level. At the network level,
authentication is achieved using a common network key, thus preventing outside attacks whilst adding
very little in memory cost. Device level authentication is achieved by using unique link keys between
pairs of devices. Insider and outsider attacks are now preventable, but there is a higher memory cost
involved. c. Tinysec: The First Fully Implemented Protocol For Link-Layer Cryptography In Sensor Networks
Chris Karlof et al. [20] introduced TinySec, the first fully-implemented protocol for link-layer
cryptography in sensor networks. They explored some of the tradeoffs between performance,
transparency, and cryptographic security, and proposed a design that meets the needs of applications in
the sensor network space. They measured the bandwidth, latency, and energy costs of implementation
of TinySec and showed that they were minimal for sensor network applications. This demonstrates for
the first time that it is feasible to implement acceptable cryptographic protection for sensor networks
entirely in software. TinySec is a research platform that is easily extensible and has been incorporated
into higher-level protocols.
TinySec supports two different security options: authenticated encryption (TinySec-AE) and
authentication only (TinySec-Auth). With authenticated encryption, TinySec encrypts the data payload
and authenticates the packet with a MAC. The MAC is computed over the encrypted data and the
packet header. In authentication only mode, TinySec authenticates the entire packet with a MAC, but
the data payload is not encrypted [21].
Table 2: Security architecture comparison table
The discussion of the aforementioned security protocols and authentication mechanisms allow for the
construction of a comparison table (Table 2), where they can be compared under similar headings. It
can be seen, from this flavor of authentication mechanisms, that the trend has moved from pre-
deployed keying mechanisms, to symmetric keying agreements (SKA) to Elliptical Curve
Cryptography (ECC) based algorithms to perform authentication in wireless sensor networking. d. INSENS
Enforcing security in existing routing protocols through public key cryptographic mechanisms would
either make them more complex or would consume the resources of tiny sensor devices. According to
these constraints, many secure routing protocols implement symmetric key cryptographic mechanisms
to provide security. But this security is not complete because they consider only few of the design
principles. For instance, SPINS and TinySec focus only on Prevention principle. They provide
inadequate security in the presence of compromised nodes. As a preventive measure Secure Implicit
Geographic Forwarding (SIGF) protocol chooses next hop dynamically and non-deterministically
rather than maintaining routing tables. On the other hands, Intrusion-Tolerant Routing protocol for
Wireless Sensor Networks (INSENS) protocol uses multipath technique in order to make the network
resilient to attacks. Moreover [22], none of the proposed symmetric key based routing protocols
incorporate all the three main design principles. These principles are Prevention, Detection or
Recovery and Resilience. So to design and build a new protocol needs to consider all the discussed
requirements. Parno et al. has designed ’Secure Sensor Network Routing Protocol with a new
asymmetric key based routing protocol and also security and efficiency as the central design
parameters .The overhead and complexity of cryptographic mechanisms has been observed to be
within acceptable limits. e. LISP: A Lightweight Security Protocol For Wireless Sensor Networks
Taejoon Park et al. [23] proposed a lightweight security protocol (LiSP) that is equipped with key
renew ability and makes a tradeoff between security and resource consumption. The heart of LiSP is a
novel rekeying protocol that (1) periodically renews the shared key to solve the key stream-reuse
problem and maximize scalability/energy efficiency. And (2) supports reliable key distribution.
LiSP aims to offer a lightweight security solution for a large-scale network of resource-limited sensor
devices. For scalability to a large number of sensors, LiSP decomposes the entire network into clusters
and/or sensing groups and selects a Gh (group head) for each of them. f. GEOSENS: Geo-Based Sensor Network Secure Communication Protocol
For robust group communication in sensor networks, because of the necessity and difficulty of doing
multiple node revocation, no protocols work efficiently enough for sensor networks. [24] Protocols of
other traditional approaches such as group key distribution or broadcast encryption protocols are
usually not suitable for sensor networks either, due to the limited resources of sensor network. For pair
wise communication (unicasts), Eschenauer and Gligor [25] designed a key pre-distribution scheme
using the theory of random graphs. Before deployment, each sensor node receives a random subset of
keys from a large key pool. To agree on a key for pair wise communication, two nodes find one
common key within their subsets and use that key as their shared secret key. Mike Chen et al. [26]
generalized it using the idea of q-composite. This generalized scheme increases the security of key
setup such that an attacker has to compromise many more nodes to achieve a high probability of
compromising communication. It is shown that, by increasing the value q, the network resilience
against node capture is improved. Liu and Ning [27] designed two schemes for secure pair wise
communication in sensor networks: polynomial-based and grid-based key distribution protocols.
Polynomial-based protocol further extended the idea of Eschenauer and Chan’s works. The basic idea
of GeoSENS is using the zone information to save unnecessary transmissions while retaining the
robustness against node capture. In this paper, Scott C.-H. Huang et al. proposed GeoSENS, a random
key pre-distribution scheme for pair wise communication in sensor networks and demonstrated that in
the case where the wireless communication range of sensor nodes is very limited, previous schemes
will not work well. In this case, sensors will be too busy relaying packets to really communicate with
each other. GeoSENS is a scheme that makes use the zone information of the network, so the overhead
of relaying can be substantially reduced. They also showed that overhead can be further reduced from
4k to 3k if the region can be triangulated. As a conclusion, it becomes a tradeoff between the number
of keys stored at a sensor node and the communication efficiency. [28] g. MINISEC
MiniSec is a secure network layer protocol that claims to have lower energy consumption than TinySec
while achieving a level of security which matches that of Zigbee. A major feature of MiniSec is that it
uses offset codebook (OCB) mode as its block cipher mode of operation, which offers authenticated
encryption with only one pass over the message data. Normally two passes are required for both
secrecy and authentication. Another major benefit of using OCB mode is that the cipher text is the
same length as the plaintext, disregarding the additional fixed length tag, four bytes in MiniSec’s case,
so padding or cipher text stealing is not necessary. Another primary feature MiniSec has over the other
security suites mentioned here is strong replay protection without the transmission overhead of sending
a large counter with each packet or the problems associated with synchronized counters if packets are
dropped. To achieve this MiniSec has two modes of operation, one for unicast packets MiniSec-U, and
one for broadcast packets.
7. Conclusion New technologies expanded in last few years have advanced the architecture on the WSN with more
vivacity and exuberance which eventually caused a noticeable increment in the applications of wireless
sensor networks. On the other hand, the salient features of WSNs make it very challenging to design
strong security protocols while still maintaining low overheads. This paper we have studied diverse
types of security vulnerabilities and proposed security solutions against them for existing wireless
sensor networks (WSN) and showed comparisons among them.
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Accepted on 17 December 2004.
Salah-ddine Krit received the B.S. and Ph.D degrees in Microectronics Engineering from Sidi Mohammed Ben
Abdellah University, Fez, Morroco. Institute in 2004 and 2009, respectively. During 2002-2008, he is also an engineer
Team leader in audio and power management Integrated Circuits (ICs) Research. Design, simulation and layout of analog and digital blocks dedicated for mobile phone and satellite communication systems using CMOS technology. He
is currently a professor of informatics with Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University, Agadir,
Morroco. His research interests include wireless sensor Networks (Software and Hardware), computer engineering and wireless communications.
Said El Hajji, Professor of Higher Education at Mohammed V - Agdal University, chief of Laboratory MIA, Faculty of
Sciences, Rabat, Morocco.
http://www.fsr.ac.ma/mia/elhajji.htm
Jalal Laassiri received his Bachelor’s degree (License es Sciences) in Mathematics and Informatics in 2001and his
Master’s degree (DESA) in computer sciences and engineering from the faculty of sciences, university Mohammed V, Rabat, Morocco, in 2005, and he developed He received his Ph.D. degree in computer sciences and engineering from
University of Mohammed V, Rabat, Morocco, in Juin, 2010. He was a visiting scientific with the Imperial College
London, in London, U.K. He is Member of the International Association of Engineers (IAENG), He joined the Faculty of Sciences of Kenitra, Department of Computer Sciences, Ibn Tofail University, Morocco, as an Professor in October
2010, His current research interests include Software and SystemsEngineering, UML-OCL,B-Method,datamining,
http://sites.google.com/site/laassirijalal/
Ouafaa Ibrihich received the Bachelor’s degree (Licence es sciences) in Mathematics and Informatics in 2008 and the
specialized Master’s degree in systems and networks from the Faculty of Sciences of Kenitra, Department of Computer Sciences, Ibn Tofail University, Morrocco, in 2010. Since 2011, she is currently an administrator of informatics with
Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University, Agadir, Morroco. Her research interests include Security
Protocols for Wireless Sensor Networks.
