Attacks In Sensor Networks Research Articles

Mitigating jamming attacks in underwater sensor networks using M‐Qubed‐based opportunistic routing protocol

Mitigating jamming attacks in underwater sensor networks using M‐Qubed‐based opportunistic routing protocol

Sensor Network Attack Synthesis against Fault Diagnosis of Discrete Event Systems.

This paper investigates the problem of synthesizing network attacks against fault diagnosis in the context of discrete event systems (DESs). It is assumed that the sensor observations sent to the operator that monitors a system are tampered with by an active attacker. We first formulate the process of online fault diagnosis under attack. Then, from the attack viewpoint, we define a sensor network attacker as successful if it can degrade the fault diagnosis in the case of maintaining itself as undiscovered by the operator. To verify such an attacker, an information structure called a joint diagnoser (JD) is proposed, which describes all possible attacks in a given attack scenario. Based on the refined JD, i.e., stealthy joint diagnoser (SJD), we present an algorithmic procedure for synthesizing a successful attacker if it exists.

Detection of False Data Attack in Sensor Networks Based on APIT Location Algorithm

Detection of False Data Attack in Sensor Networks Based on APIT Location Algorithm

Distributed Secure Filtering Against Eavesdropping Attacks in SINR-Based Sensor Networks

Distributed Secure Filtering Against Eavesdropping Attacks in SINR-Based Sensor Networks

Nussbaum gain adaptive control for full‐state constrained cyber‐physical system under network attacks and actuator failures

AbstractIn this article, an adaptive security control scheme is proposed for a class of nonlinear time‐varying full‐state constrained cyber‐physical system with false data injection attacks and actuator failures. Aiming at the problem of sensor network attack, the influence of sensor network attack is reduced by backstepping technique and Nussbaum function. In the control design process, the time‐varying barrier Lyapunov function is used to constrain the system states. In addition, adaptive laws are designed to eliminate the influence of actuator faults (effectiveness loss and bias fault). And the developed controller ensures that the state constraints will not be violated when sensor attacks and actuator faults occur simultaneously. Finally, the simulation result verify the effectiveness of the scheme.

A detection method of wormhole attack in power communication sensor networks based on hops

Aiming at the problems of low accuracy of determining attack nodes and large detection error in the wormhole attack detection method of power communication sensor networks, a wormhole attack detection method based on hops is proposed. Firstly, the working process of power communication sensor network nodes is analyzed; Regard the node area as a closed circle, set the distance between nodes and communication radius in the area, and determine the wormhole attack location; Then, introduce the hop number, replace the linear distance between attack nodes with the hop distance of hop number, the centroid position is calculated by polygon calculation, the node hop distance is determined, and the wormhole attack probability is calculated; Finally, the average hop distance between wormhole attack nodes is calculated, and the path hop distance of attack behavior is corrected with the help of objective function and minimum mean square error criterion to complete attack detection. The experimental results show that the detection accuracy of this method is 98%, and the detection error is only 2.6%, so it has application value.

Adaptive resilient control for cyber-physical systems against unknown injection attacks in sensor networks

Adaptive resilient control for cyber-physical systems against unknown injection attacks in sensor networks

Detection of false data attacks in sensor networks based on the APIT location algorithm

Detection of false data attacks in sensor networks based on the APIT location algorithm

A secure model against mobile sink replication attacks in unattended sensor networks

Unattended wireless sensor networks (UWSNs), in which mobile sinks (MSs) are responsible for the data collection, and which are vulnerable to multiple attacks. For example, the adversaries can initiate MS replication attack after compromising a large number of sensor nodes (SNs). To resist such attacks, some scholars have proposed some schemes. In these schemes, it is assumed that MSs are equipped with tamper resistance hardware, so they are pre-distributed most of keys of a key pool for achieving authentication with SNs. However, in outdoor and even sensitive areas, tamper-resistant hardware is not always absolutely safe. Once MSs are compromised, networks become insecure. In this paper, we propose a secure model. The model contains three types of nodes, namely SNs, MSs, and a base station (BS). MSs are responsible for collecting the data encrypted by SNs and forwarding it to BS; BS is responsible for decrypting and analyzing the collected data. During the data collection process, an MS can collect an SN's data only after being authenticated by the SN using the pre-distribution key information. But it cannot decrypt the collected data. In this model, the adversaries can induce a new type of false data injection attack. In the attack, the replicated MSs impersonate uncompromised SNs to send false data to BS by using the compromised key information. If BS accepts a large amount of false data, it will make a wrong judgment. Analysis and simulation show that the proposed model has excellent resistance against MS replication attacks and false data injection attacks by setting appropriate parameter values.

AUV-Aided Data Importance Based Scheme for Protecting Location Privacy in Smart Ocean

Data collection in underwater acoustic sensor networks (UASNs) and the exposure of node location information pose a threat to the security of the entire network. Therefore, the main challenge for underwater acoustic sensor network security is to protect the security and privacy of the node locations. Compared to active attacks, the characteristics of passive attacks are indistinguishable. Therefore, this research focuses on passive attacks in underwater acoustic sensor networks, and an autonomous underwater vehicle (AUV)-aided data-importance-based scheme for protecting location privacy (DIS-PLP) is proposed. The DIS-PLP comprises three main parts. First, the anchor node calculates the importance of the current data based on the historical dataset and decides whether to send data packets to the base station according to the importance result. Subsequently, an AUV privacy violation is defined, and an AUV path planning method is proposed to prevent attackers from tracking the AUV to obtain the location of the source node. Finally, to prevent nodes from waiting too long for AUVs, a secure multi-hop transmission method is proposed that uses fake source nodes to send fake source data packets to hide the real traffic. The simulation results show that the DIS-PLP offers well performance in terms of safety time and delay, which has strong practical significance for Smart Ocean to improve the level of network security.

Vulnerabilities in Wireless Sensor Networks: Attacks and Countermeasures

In wireless sensor networks, we look at routing security. There have been numerous sensor network routing protocols proposed, but none of them have been developed with security in mind. We propose security goals for sensor network routing, show how ad-hoc and peer-to-peer network attacks can be adapted into powerful sensor network attacks, introduce two new types of sensor network attacks––sinkholes and HELLO floods––and assess the security of all major sensor network routing protocols. We detail all of them as targets for debilitating attacks and offer countermeasures and design considerations. This is the first study of secure routing in sensor networks of its kind.

A Secure Model Against Mobile Sink Replication Attacks in Unattended Sensor Networks

A Secure Model Against Mobile Sink Replication Attacks in Unattended Sensor Networks

Bipolar fully recurrent deep structured neural learning based attack detection for securing industrial sensor networks

AbstractAttack detection is a significant problem to be resolved to attain security in industrial sensor network. Few research works have been designed for performing attack discovery process with the help of different classification algorithms. However, classification performance was lowered to accurately find attack nodes and thereby getting higher security level (SL). In order to overcome such limitations, bipolar fully recurrent deep structured neural learning based attack detection (BFRDSNL‐AD) technique is proposed in this article. The BFRDSNL‐AD technique comprises of three main layers, namely, input, hidden, and output layer to accurately carry‐out attack detection process. The input layer in BFRDSNL‐AD Technique takes a number of sensor nodes as input. The designed BFRDSNL‐AD technique utilized numbers of hidden layers in order to deeply examine each sensor node in industrial sensor network. The result of the hidden layer is fed‐back into the network along with the inputs in order to discover the temporal dynamic behavior of sensor nodes. In BFRDSNL‐AD technique, output layer applies bipolar activation function to determine the association between identified node features to give attack detection result. If the output layer result is 1, then sensor node is considered as normal. Otherwise, sensor node is considered as malicious attack in industrial sensor network. Thus, the BFRDSNL‐AD technique increases the attack detection performance with higher accuracy and minimal time. From that, BFRDSNL‐AD technique gets enhanced SL in industrial sensor network. The simulation of BFRDSNL‐AD technique is conducted using metrics such as security, data delivery rate, data loss rate, and delay with respect to a different number of sensor nodes and data packets.

The Design of a Defense Mechanism to Mitigate Sinkhole Attack in Software Defined Wireless Sensor Cognitive Radio Networks

The sink hole attack is a typical wireless sensor network attack. The sink hole node advertises itself as the best route to the sink node or the base station. The nodes in the communication zone of the sink hole node then redirects their observed data to the sink hole node upon receiving the broadcasted supposedly the best route. The sink node receives the packets and then drops them. It can also modify them before relaying them to the sink node or base station. This study proposes a scheme designed to address the effects of the sink hole attack called the Hop Count-Based Detection Scheme for Sinkhole Attack. The performance of the scheme is evaluated using Matlab. The scheme is compared to Ibrahim’s algorithm which is the best algorithm in literature. The simulation results show that HCODESSA achieves good results in comparison to Ibrahim’s. The results show that the sinkhole attack has adverse effect on the performance of the network and that its severity depends on the number of nodes it can mislead. We further evaluated the two schemes using statistical techniques which proved that HCODESSA performs better.

An Energy-Balanced Trust Cloud Migration Scheme for Underwater Acoustic Sensor Networks

As a candidate trust management scheme, trust models based on the cloud theory are always taken into account when detecting malicious attacks in Underwater Acoustic Sensor Networks (UASNs). To evaluate the trust values of nodes accurately, the evidence of trust ought to be collected frequently. As a result, continual trust update results in excessive energy consumption or premature death of some sensor nodes that are close to the trust cloud node. To address the above issues, in this paper, we propose an Energy-balanced Trust Cloud Migration scheme (ETCM) for UASNs, which consists of Destination Node Determination (DND), trust cloud migration and trust cloud update. Particularly, DND is performed hierarchically to obtain the destination node for trust cloud migration by selecting the candidate destination clusters, determining the destination cluster and the destination node, respectively. First, the candidate destination clusters are selected based on the distribution for the overall residual energy in UASNs using the simulated annealing algorithm. Then, an indicator of Cluster Ability (CA) is proposed to seek the destination cluster. Particularly, to calculate CA, the improved standardized Euclidean distance formula is employed to evaluate the connectivity between clusters and their neighboring clusters. Finally, on the basis of the defined node density reachability and the residual energy, the Node Ability (NA) is presented to indicate the capacity of nodes for trust cloud storage, calculation and update. The destination node in the destination cluster can be determined as the new trust cloud node using the NA. Simulation results demonstrate that the proposed ETCM scheme can balance energy consumption, increase node survival ratio and prolong lifetime effectively.

Corrigendum to “Mitigation strategy against spectrum sensing data falsification attack in cognitive radio sensor networks”

Corrigendum to “Mitigation strategy against spectrum sensing data falsification attack in cognitive radio sensor networks”

Mitigation strategy against spectrum-sensing data falsification attack in cognitive radio sensor networks

To detect the primary user’s activity accurately in cognitive radio sensor networks, cooperative spectrum sensing is recommended to improve the sensing performance and the reliability of spectrum-sensing process. However, spectrum-sensing data falsification attack being launched by malicious users may lead to fatal mistake of global decision about spectrum availability at the fusion center. It is a tough task to mitigate the negative effect of spectrum-sensing data falsification attack and even eliminate these attackers from the network. In this article, we first discuss the randomly false attack model and analyze the effects of two classes of attacks, individual and collaborative, on the global sensing performance at the fusion center. Afterwards, a linear weighted combination scheme is designed to eliminate the effects of the attacks on the final sensing decision. By evaluating the received sensing result, each user can be assigned a weight related to impact factors, which includes result consistency degree and data deviation degree. Furthermore, an adaptive reputation evaluation mechanism is introduced to discriminate malicious and honest sensor node. The evaluation is conducted through simulations, and the results reveal the benefits of the proposed in aspect of mitigation of spectrum-sensing data falsification attack.

Mitigation of Selective Forwarding attacks in Wireless Sensor Network

Security issue in Wireless Sensor Networks (WSNs) is a major problem while dealing with WSNs. Therefore, WSNs are susceptible to various kinds of safety assaults. The restricted capability of sensor nodes is one reason for attacks in sensor networks. In WSNs, on the network layer, there are different kinds of safety attack detection methods. There are also many severe limitations in sensor nodes such as energy efficiency, reliability, scalability that affect WSN safety. As sensor nodes have restricted ability for the majority of the limitations, a selective forwarding attack in the networks is hard to identify. In selective forwarding attack, malicious nodes function as a ordinary nodes. However, it tries to find and crash messages prior to forwarding the packet to further nodes. For keeping this sort of attack aside from networks, we suggest a multi-layer strategy, Selective Forwarding Detection (SFD) that maintains the safe transmission of information among sensor nodes at the same time as detecting the selective forwarding attack. In addition, energy efficiency, reliability and scalability are part of the approach.

Detecting Denial of Service Attack in Wireless Sensor Network using Energy Efficient Extreme Learning Neural Network (EEELNN)

One of the effective communication technology is wireless sensor network technology which helps to monitor the surrounding information by sensed nodes. The effective utilization of sensed nodes is utilized in different applications such as military, health information, environmental monitoring, disaster relief and target analyze. The application requires the collection of information which may be collected from one location and transferred to the other location for making their process so easier. During the information transformation process, the network may affect by several intermediate attack, in which denial of service is one of the serious attack because it affects the entire network resources such as network energy, power, bandwidth. The unavailability of the resources reduces the entire sensor network performance. For managing the attack related issues, in this paper introduces the Energy Efficient Extreme Learning Neural Network (EEELNN) approach for overcoming the attack related issues. Initially the network transmitted zone is computed along with energy, power, bandwidth, neighboring node information and lifetime for eliminating the attack in sensor network. The computed information is processed and trained by extreme learning neural network that successfully predict the attack related data, node and network zone with effective manner that leads to improve the overall network performance. At last system efficiency is evaluated using simulation results such as detection rate, classification accuracy, false alarm rate and detection time.

Learning-Based PHY-Layer Authentication for Underwater Sensor Networks

In this letter, we propose a physical (PHY)-layer authentication framework to detect spoofing attacks in underwater sensor networks. This scheme exploits the power delay profile of the underwater acoustic channel to discriminate the sensors and applies reinforcement learning (RL) to choose the authentication parameter without being aware of the network and spoofing model. We propose an RL-based authentication scheme to provide light-weight spoofing detection and a deep RL-based authentication scheme to further improve the authentication accuracy for sinks that support deep learning. Experiment results show that this scheme improves the spoofing detection accuracy and increases the utility of the network compared with the benchmark PHY-layer authentication.