Attacks In Wireless Sensor Networks Research Articles

Sinkhole Attacks in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of a large number of nodes, communicating sensor readings to the base stations through other nodes. Due to their energy limitations and positioning in hostile environments, WSNs are vulnerable to various routing attacks. From a security point of view in WSN, data authenticity, confidentiality, Integrity, and availability are the important security goals. It is in common practice that a security protocol used to be created by focusing a particular attack in WSN. Most renowned attacks in WSN are Sybil attack, Denial of Service attack, wormhole attack, selective attack, HELLO Flooding attack, Sinkhole attack etc. This survey focuses on one of the most challenging routing attacks, called Sinkhole attack. A Sinkhole attack is one of the sternest routing attacks because it attracts surrounding nodes with misleading routing path information and performs data forging or selective forwarding of data passing through it. It can cause an energy drain on surrounding nodes resulting in energy holes in WSNs and it can cause inappropriate and potentially dangerous responses based on false measurements. Researchers had presented several ways to detect and identify sinkhole attacks. This survey reviews related work on Sinkhole attack detection, prevention strategies, and attack techniques and also highlights open challenges in dealing with such attacks. Among many discussed techniques, fuzzy logic-based systems are considered to be good in performance in intruder detection system (IDS).

Using Improved Hilbert–Huang Transformation Method to Detect Routing-Layer Reduce of Quality Attack in Wireless Sensor Network

Denial of Service (DoS) attack is a serious threat to the security of Wireless Sensor Netwokr (WSN). Moreover, Reduce of Quality (RoQ) attack is a special DoS attack type. The RoQ attack is a potential DoS attack, it is simulator to normal traffic,so it is difficult to be detected by traditional detection method. Hilbert–Huang Transform (HHT) time–frequency analysis method can be used to analyze the non-linear signal generated by RoQ attack. However, mode mixing and false component are the challenge problems to analyze the RoQ attack by HHT method. Ensemble empirical mode decomposition (EEMD) method is used to eliminate the mode mixing problem. Correlated coefficient method is used to recognize the false components. In this paper, EEMD and correlated coefficient methods are combined to detect the weak signal generated by RoQ attack. Ad Hoc On-demand Multi-path Distance Vector routing protocol and random Routing REQuest flooding attack are simulated to implement RoQ attack in wireless sensor network by Network Simulator. Suitable white noises are added to original signal to detect the weak RoQ attack signal. Experiment results show that the improved HHT methods are highly effective to detect RoQ attack, when the standard deviation of white noise is 0.01 and the number of ensemble is 80, the detection results is best. When the correlated coefficient of intrinsic mode functions (IMFs) is greater than 0.2, the IMFs can be used to analyze and detect RoQ attack. To our knowledge, this is the first quantitative experiment and method research on the routing-layer RoQ attack detection in WSN.

TECHNIQUE FOR DETECTING SINGLE AND MULTIPLE BLACKHOLE ATTACK ON WIRELESS SENSER NETWORKS

Security is the fundamental issue in wireless sensor networks and attackers are effortlessly altered the Actual behaviour and performance of network. In this research work we give the security plot against single and multiple black hole attack in wireless sensor networks. in black hole attack malicious node behave like a normal node and show that they are part of our network but when the data packets arrives they drops all the packets and reply false value to other nodes. in this proposed scheme we have identified the attackers node that caputers the data packets and nor forwareded to the destination. in wireless sensor network the malicious nodes are only nodes which are not farwareded data packects to goal node. in our proposed scheme we not only detect single black hole but also capable to detect multiple blackhole attack. black hole attack is veryharmful attack, the proposed method is surely identify the malicious node from the dynamic network and increase the network efficiancy.

Sensor Networks Attacks Classifications and Mitigation

Wireless Sensor Networks (WSNs) are exposed to many security attacks, and it can be easily compromised. One of the main reasons for these vulnerabilities is the deployment nature, where sensor nodes are deployed without physical guarding duty. That makes the network susceptible to physical attacks. The communication nature between sensor nodes is another reason, where intruders can easily send/receive information if they are located in the network communication range. In this paper, most of the possible WSN attacks are discussed, different security services expected in WSN are explained, and trust-based solutions proposed in the literature are listed. Moreover, the state-of-the-art of the attacks’ mitigation and avoidance techniques are presented. Besides, this paper is enriched with a new classification of the WSNs attacks regarding attacks’ characteristics. It will be beneficial to researchers in the field of WSNs security if they can distinguish between different attacks that have common characteristics.

Security Issues and Attacks in Wireless Sensor Networks: Some Case Studies

Security Issues and Attacks in Wireless Sensor Networks: Some Case Studies

An Efficient Routing Approach for Detection of Syn Flooding Attacks in Wireless Sensor Networks

In wireless environment researches on security issues in various layering level of the networks are focused recent times. One of the major issue is denial of service attacks. This paper mainly deals with the detection of syn flooding attacks which is one form of denial of service attacks in wireles

A cognitive chronometry strategy associated with a revised cloud model to deal with the dishonest recommendations attacks in wireless sensor networks

Wireless sensor networks (WSNs) face many security issues. When external attacks can be prevented with traditional cryptographic mechanisms; internal attacks remain difficult to be eliminated. Trust and reputation have been recently suggested by many researches as a powerful tool for guaranteeing an effective security mechanism. They enable the detection and the isolation of both faulty and malicious nodes. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies especially in the case of dishonest recommendations attacks, i.e. badmouthing, ballot-stuffing and collusion attacks. In this paper, we propose a novel bio inspired trust model for WSNs namely Bee-Trust Scheme (BTS) based on the use of both a modified cloud model and a cognitive chronometry parameter. The objective of the scheme is to achieve both a higher detection rate and a lower false positive rate of dishonest recommendations attacks by allowing the distinction between erroneous recommendations and dishonest ones which has thus far been overlooked by most research work. Simulation results demonstrate that the proposed scheme is both effective and lightweight even when the number of dishonest recommenders is large.

Stealth Jamming Attack in WSNs: Effects and Countermeasure

This paper explores a novel type of attack in wireless sensor networks- Stealth Jamming (SJAM) , in which malicious nodes periodically generate bogus traffic in order to trigger false route breakages. More precisely, they target a relatively unexplored weakness inherent in the route maintenance mechanism of ad hoc protocols used by trust-aware routing schemes. Missing a certain number of transmission attempts at the data link layer is typically considered as the link, and consequently route breakage. In this attack, malicious nodes silently listen to the wireless channel, and if the busy airtime (indicative of wireless congestion) exceeds a certain threshold, it starts injecting gratuitous fake broadcast messages for a brief duration, thereby leading to excessive collisions for a transient period and leading to failed transmissions and route breakages, significantly degrading network performance. SJAM is difficult to detect, because the malicious nodes are active only if congestion exceeds a threshold and then only periodically. It is difficult to discern these attacks from genuine route breakages, which are also quite common. In this paper, we explore this type of attack and propose an intelligent cross-layered scheme for mitigating these attacks, thereby improving route stability, throughput, and delay performance. We discuss how enhanced route maintenance in existing trust-aware routing solutions ensures wide applicability of the solution. Simulation results demonstrate the improved network performance when compared with the state-of-the-art solutions.

Identifying localization attacks in wireless sensor networks using deep learning

Identifying localization attacks in wireless sensor networks using deep learning

Swarm-based defense technique for tampering and cheating attack in WSN using CPHS

Nowadays, Wireless Sensor Networks (WSN) have been seen as one of the most essential technologies which play a vital role in several important areas, such as healthcare, military, critical infrastructure monitoring, environment monitoring, and manufacturing. The desirable nature of WSN introduces many security challenges which have necessitated the introduction of several classical security methodologies over the last few years for various security attacks. A defense technique using Swarm Based Trusted Node for Tampering and Cheating Attack (SBTN-TC) model has been proposed to identify the physical layer attacks in a WSN. This model, first selects a trusted node (TN) within the network for identifying the tampered or cheating nodes using swarm intelligence. The trusted node monitors and processes the nodes session receipts involved in the transmission session to extract the contextual information such as broken links, frequent packet droppers, and fair and cheating sessions. Based on the session receipts, the trusted node renews the nodes certificate and ignores the tampered node or cheating node. In order to maintain a secure communication of contextual information between node and trusted node, a Cryptographic Puzzle Hiding Scheme (CPHS) is used for submitting the session receipts to trusted node. The effectiveness of the proposed method has been proved from the simulation results based on packet delivery ratio, packet drop, residual energy, and overhead.

Security Attacks in Wireless Sensor Networks

In today’s world, Security is the prime concern for each application. Network security and Computer protection are the serious issues. A broad range of Wireless Sensor Networks (WSNs) applications[1][15] are widely used in military, researches etc. The key subject of computing is to provide network security several types of attacks are increasing day by day. The attacks present in different layers are produced due to the susceptibilities present in the layers. The Seven layers in OSI model are formulated to convey the information from highest layer to lowest layer. Broadcast of data gets modified according to the layer in which it lies. Individually every layer is weak to different types of attacks. This paper models about the different attacks present in each layer of OSI.

A Statistical Approach to Detect Jamming Attacks in Wireless Sensor Networks.

Wireless Sensor Networks (WSNs), in recent times, have become one of the most promising network solutions with a wide variety of applications in the areas of agriculture, environment, healthcare and the military. Notwithstanding these promising applications, sensor nodes in WSNs are vulnerable to different security attacks due to their deployment in hostile and unattended areas and their resource constraints. One of such attacks is the DoS jamming attack that interferes and disrupts the normal functions of sensor nodes in a WSN by emitting radio frequency signals to jam legitimate signals to cause a denial of service. In this work we propose a step-wise approach using a statistical process control technique to detect these attacks. We deploy an exponentially weighted moving average (EWMA) to detect anomalous changes in the intensity of a jamming attack event by using the packet inter-arrival feature of the received packets from the sensor nodes. Results obtained from a trace-driven simulation show that the proposed solution can efficiently and accurately detect jamming attacks in WSNs with little or no overhead.

Security Techniques for Wormhole Attack in Wireless Sensor Networks

The wireless sensor network has group of sensors which can sense the data and route this data to base station. As there is no physical connection between sensor and base station the important data can be routed without wires. The broadcast nature of wireless sensor network makes it prone to security threat to the valuable data. The attacker node can detect the data by creating their own data aggregation and routing mechanism .The number of attacks can be possible on the network layer. Out of these attacks wormhole is one of the major attack which can change the routing method of the whole wireless sensor network. In this attack,the attacker node can control the packet transmission of whole network and route it to the tunnel of nodes. The major drawback of this attack is to increase the packet drop and disturbing the routing mechanism. A number of security techniques are developed by the researcher to reduce the packet drop ratio and secure the routing mechanism of the network. Out of all thesetechniquesfew related to packet drop ratio are discussed in this paper. TheLightweight countermeasure for the wormhole attack (LITEWORP) based on Dynamic Source routing (DSR) protocol security technique,Delay Per Hop Indication (Delphi) based on AODV(Avoidance Routing Protocol) Protocol security technique and MOBIWORP based on DSRprotocol security technique reduce the packet loss percentage 40%,43% and 35% respectively.

DDoS attacks in WSNs: detection and countermeasures

Wireless sensor networks (WSNs) have been widely applied in many areas for real-time event detection. They are designed using both mobile and static sensor nodes (SNs) for different applications such as smart parking, environmental monitoring, health care systems, automotive industries, sports, open space surveillance, and so on. WSNs communicate through wireless mediums and are accessible to anyone, which make SNs susceptible to different types of attacks. Distributed denial of service (DDoS) is one such attack. It wastes the limited energy of SNs and causes loss of data packets within a network. A DDoS attack launches a coordinated attack by flooding the target nodes with bogus requests, thus exhausting their resources, and forcing them to deny service to legitimate member nodes. In this study, the authors propose a message analyser scheme for WSNs. The method is capable of detecting compromised SNs vulnerable to a DDoS attack. In addition, it is able to detect all compromised messages transmitted by the attackers to the base station through the sender nodes. The proposed method is compared with other related protocols. The results show that their method can effectively detect and defend against DDoS attacks in WSNs.

Security DV‐hop localisation algorithm against wormhole attack in wireless sensor network

Distance vector-hop (DV-hop) is a localisation algorithm based on distance vector routing, which often suffers the wormhole attack. To solve this problem, a security DV-hop localisation algorithm against wormhole attack (AWDV-hop) is proposed. First, the algorithm establishes the neighbour node relationship list (NNRL) by broadcast flooding. All the nodes get the ID numbers of their neighbour nodes through NNRL. The suspect beacon nodes can be found by comparing the theoretical and actual number of neighbour nodes. Then, the suspect beacon nodes calculate the distances to other beacon nodes in their NNRL to find the actual attacked beacon nodes. The attacked beacon nodes in the different areas of wormhole attack are marked with 1 or 2. Finally, the unknown nodes mark themselves with 1 or 2 according to the beacon nodes marked before. In the next round of localisation, the nodes marked with 1 and the nodes marked with 2 disconnected from each other. The simulation results show that the localisation error of the proposed AWDV-hop algorithm is reduced by about 80% than that of DV-hop algorithm suffering from the wormhole attack. Compared to label-based DV-HOP (LBDV-hop) and secure neighbour discovery based DV-HOP (NDDV-hop) algorithm, the localisation error is reduced by about 7 and 34%, respectively.

An approach towards preventing iot based sybil attack based on contiki framework through cooja simulator

In this paper we propagate the Sybil attack in WSN (Wireless sensor network), by the researchers many attacks have been recognized in WSN till now and there are many attacks which can attack over through internet, Internet of thing means all devices is interconnected to each other M2M over internet and can be attacked by any of the attacker on any devices. Sybil attack is the detrimental attack against sensor network where several counterfeit identities and legitimate identities are used to get prohibited pass in a network. This is major attack which results an information loss and misinterpretation in the network, and it also minimizes the routing disturbance, trustworthiness and dropping sensitivity packets into a network. In this instance node can trust the imaginary node and sharing of information starts, owed to this security of node is get affected and information is lost. In this paper, a survey of CONTIKI OS-2.7, stimulation tool COOJA and the Sybil attack and proposed the defense mechanisms and CAM (Compare and Match) approach to verify the Sybil attack position and prevent it. This Sybil attack can be stimulated on the stimulation tool COOJA which helps to identify the attacker position node, whereas these attacks outcome in uni-casting as well as multicasting and in this paper specifically given the secure security for Wireless sensor network.

Detection and prevention of black hole and selective forwarding attack in clustered WSN with Active Trust

In the recent era, security is the major problem in sensor networks. Wireless sensor networks (WSNs) are mostly used for various real-world applications. However, WSNs face a lot of insider and outsider attacks, and it is complex to identify and protect towards insider attacks. Generally, an insider attack, in which the intruders choose several received data packets to drop, threatens the clustered WSNs. This situation has occurred because of the unattended clustered environments in the network. To overcome this problem, this paper proposes a trustable and secure routing scheme using two-stage security mechanism, and dual assurance scheme, for selecting the node and securing the data packet for WSNs. Both schemes are based on Active Trust to protect several kinds of attacks, such as black hole attack, and selective forwarding attack, during routing. Therefore, this paper identifies the trusted path and provides the secure routing paths using trust and Cuckoo search algorithm. Energy is the performance parameter utilized in the proposed scheme. The experimental result proves that proposed system provides the assurance to prolong the network lifespan and the probability of secure routing path in the network.

M optimal routes hops strategy: detecting sinkhole attacks in wireless sensor networks

Sinkhole malicious nodes attacks are quite hard to handle in wireless sensor networks, due to the reason that the results of the attacks may cut off the communications by spreading false hops, absorbing data packets. Those terrible attacks are formed from the unique destroying method, which will preferentially destroy the nodes with higher communication function and shorter distance from the Sink node (Base station). In this paper, we propose a secure and energy-efficient detection scheme which can detect the malicious nodes more precisely than the traditional methods. In particularly, a new measure method is introduced in this paper, which is the frequency of each node by establishing M routes with optimal hops from per node to the Sink node. Using the proposed measure and dynamic programming, the malicious nodes can easily tell apart from the network according to the various satisfaction levels and hop difference. Compared with the traditional algorithms, simulation results show that our scheme can significantly promote the detection rate and the false positive rate. The detection rate increased about by 6–30%, the false positive rate decreased about by 5–25%. We also obtain a high energy-saving results in wireless sensor networks.

Analysis of AODV protocol under sinkhole attack in wireless sensor network

A Wireless Sensor Network (WSNs) is a collection of number of sensor nodes which are left open in an unsecured environment. Sensor nodes work and communicate together to attain the desired goals. They are placed at the locations where monitoring is otherwise impossible. Wireless Sensor Networks are resource constrained which may be computational power, memory capacity, battery power etc. As Wireless Sensor Networks are implemented in the unattended environment, they are prone to discrete type of security attacks. Because of their limitations these networks are easily targeted by intruders. Sinkhole attack is one of the security attacks which try to disturb the ongoing communication in wireless sensor network. In sinkhole attack, the intruder or the malicious node try to attract the network traffic towards itself, that sensor nodes will pass data packets through this compromised node thereby manipulating messages which sensor nodes are transferring to the base station. In this paper we analyze the impact of Sinkhole attack on AODV protocol under various conditions. We analyzed the impact of Sinkhole attack on AODV protocol with varying number of attacker nodes.

Whac-A-Mole: Smart node positioning in clone attack in wireless sensor networks

Wireless sensor networks are often deployed in unattended environments and, thus, an adversary can physically capture some of the sensors, build clones with the same identity as the captured sensors, and place these clones at strategic positions in the network for further malicious activities. Such attacks, called clone attacks, are a very serious threat against the usefulness of wireless networks. Researchers proposed different techniques to detect such attacks. The most promising detection techniques are the distributed ones that scale for large networks and distribute the task of detecting the presence of clones among all sensors, thus, making it hard for a smart attacker to position the clones in such a way as to disrupt the detection process. However, even when the distributed algorithms work normally, their ability to discover an attack may vary greatly with the position of the clones. We believe this aspect has been greatly underestimated in the literature. In this paper, we present a thorough and novel study of the relation between the position of clones and the probability that the clones are detected. To the best of our knowledge, this is the first such study. In particular, we consider four algorithms that are representatives of the distributed approach. We evaluate for them whether their capability of detecting clone attacks is influenced by the positions of the clones. Since wireless sensor networks may be deployed in different situations, our study considers several possible scenarios: a uniform scenario in which the sensors are deployed uniformly, and also not uniform scenarios, in which there are one or more large areas with no sensor (we call such areas “holes”) that force communications to flow around these areas. We show that the different scenarios greatly influence the performance of the algorithms. For instance, we show that, when holes are present, there are some clone positions that make the attacks much harder to be detected. We believe that our work is key to better understand the actual security risk of the clone attack in the presence of a smart adversary and also with respect to different deployment scenarios. Moreover, our work suggests, for the different scenarios, effective clone detection solutions even when a smart adversary is part of the game.