Detecting Selective Forwarding Attacks in Wireless Sensor Networks using Support Vector Machines

Wireless sensor networks (WSNs) are novel large-scale wireless networks that consist of distributed, self organizing, low-power, low-cost, tiny sensor devices to cooperatively collect information through infrastructure less wireless networks. These networks are envisioned to play a crucial role in variety of applications like critical military surveillance applications, forest fire monitoring, commercial applications such as building security monitoring, traffic surveillance, habitat monitoring and smart homes and many more scenarios. Node capture attack is one of the most dreadful security attack exist in wireless sensor networks. An adversary steals cryptographic key or other confidential information like node’s id etc from a captured node to compromise entire network. So, Security of wireless sensor network is an important issue for maintaining confidentiality and integrity of wireless links. Now-a-days, researchers are paying attention towards developing security schemes against Node capture attack. Our survey provides deep insights of existing techniques that enhance the attacking efficiency of the node capture attack in wireless sensor network. It also analyzes various detection and key predistribution schemes for inventing a new scheme to improve resilience against node capture attack.

Wireless Sensor Networks (WSNs) are turning into an essential portion of our lives. Devoid of assuring WSNs security (WS), there remain no broad implementations of WSNs. Because of sensor nodes’ constrained capacities concerning computation, communication, and energy, giving protection to WSNs remains competitive. Indeed, the procedure of applying WS remains adaptable and vibrant that develops consistently. Attack-defend’s crux in WS could be conveyed by collaborative schemes of interdependency when Game Theory (GT) could be employed to consider communications amidst schemes of logical decision-makers. Hence, learning WS alongside GT possesses greater logic. This study proffers a Multiplayer Reputation-based Coalition Game Model (MRCGM) for seeking the false data injection amidst the nodes. An attacker attempts to sustain an ideal level of belief by correlating the identifications within the network (NW) to initiate a victorious transfer. Concurrently, the attacker should acquire a few charges for sustaining the trustability of its identifications. This proffered procedure employs this notion and turns the attack expensive by fixing a global threshold for nodes or identifications to be trustable and functional within the NW. Additionally, the beneficial function of the attacker and defender is as well described. The MRCGM has been correlated with 2 advanced methodologies like security-aware routing scheme employing repeated game (SARSRGM) paradigm and a Game-based Fuzzy Q-learning (GBFQL) scheme concerning diverse criteria. Consequently, the proffered MRCGM attains 124 kbps of Packet Drop Rate, 76.14% of energy efficiency, 96.1% of detection accuracy, 25.45% of energy consumption, and 102.45 ms of routing latency.

Information attacks and security in wireless sensor networks of industrial SCADA systems

In recent years, with the development of the Internet of things industry, wireless sensor networks, as one of the most important technologies in its sensing layer, have been further studied and applied. However, the security threats it faces are becoming more and more diverse, and the security needs are becoming more and more urgent. However, wireless sensor network nodes are mutually exclusive, which leads to low accuracy of attack path prediction when malicious attacks occur. In order to ensure the security of wireless sensor networks, a hidden Markov based malicious attack path prediction method for wireless sensor networks is designed. The method of authentication coding is introduced to realize data integrity authentication. Extract the malicious software gene sequence, define the constraint conditions of the state transition probability matrix, build the attack means deduction model based on Hidden Markov, and realize the malicious attack path prediction. Experimental results: the average accuracy of the proposed method in four malicious attack scenarios is 73.583%, 55.674%, 32.923% and 85.014%, respectively. The results also verify that the proposed method improves the accuracy of malicious attack path prediction in wireless sensor networks based on Hidden Markov model and has good feasibility. It provides an important basis for wireless sensor network administrators to implement defense strategies in time.

Wireless sensor networks are being used more and more. Sensor nodes can be distributed in hazardous environments to collect data. As these environments are usually unsecured, provide security in wireless sensor networks is vital. Detecting attacks in sensor networks is inevitable to provide security. Various attacks threaten sensor networks one of which is Sybil attack, which can be a big threat for geographical routing protocols and multi-path routing. In this paper, a distributed method has been presented using mobile agents and local information of each sensor to detect Sybil attack. The results of simulation show the efficacy of this method in comparison with previous methods.

Security in wireless sensor networks is critical due to its way of open communication. In this paper we have provided a solution to detect malicious nodes which perform radio transmission power control attack and sinkhole attack in wireless sensor networks. In the proposed approach, data transmission is divided into multiple rounds of equal time duration. Each node chooses the parent node in the beginning of the round for forwarding the packet towards sink. Each node adds its identity in the packet as a routing path marker and encrypts before forwarding to parent. Child node observes the parent, handles acknowledgement from 2-hop distance node and decides the trust on parent based on successful and unsuccessful transactions. Each node sends a trust value report via multiple paths to Sink at the end of the round. Sink identifies the malicious node by comparing trust value report received from each node with number of data packets received. Simulated the algorithm in NS-3 and performance analysis compared with other recently proposed approach. Simulation results show that proposed method detect the malicious nodes efficiently and early.

Wireless sensor networks have been widely applied in many areas due to their unique characteristics. These have exposed them to different types of active and passive attacks. In the literature, several solutions have been proposed to mitigate these attacks. Most of the proposed solutions are too complex to be implemented in wireless sensor networks considering the resource-constraint of sensor nodes. In this work, we proposed a hierarchical trust mechanism based on clustering approach to detect and prevent denial of service attacks in wireless sensor networks. The approach was validated through simulation using Network Simulator (NS2). The following metrics were used to evaluation the proposed scheme: packet delivery ratio, network lifetime, routing delay, overhead, and number of nodes. The proposed approach is capable of detecting compromised sensor nodes vulnerable to a denial of service attacks. Moreover, it is able to detect all sensed data that have been compromised during transmission to the base station. The results show that our method can effectively detect and defend against denial of service attacks in sensor wireless sensor networks.

Detecting Sybil attacks in wireless sensor networks using UWB ranging-based information

The article comprises issues of analytical modeling of attacks in wireless sensor networks. A proposed attack model specifies possible types of unauthorized influences at the network level aimed at compromising network nodes, data transmitted over the network, and information services provided by the wireless sensor network. A developed graph-based analytical model of a wireless sensor network aims at detecting network layer attacks by processing actual events received from network nodes and deciding whether the network is running under an attack. The correctness of the models is confirmed by their testing by using an example of a scenario for detecting a wormhole attack aimed at disrupting routing processes in a wireless sensor network.

Selective forwarding attacks may corrupt some mission-critical applications such as military surveillance and forest fire monitoring. In these attacks, malicious nodes behave like normal nodes in most time but selectively drop sensitive packets, such as a packet reporting the movement of the opposing forces. Such selective dropping is hard to detect. In this paper, we propose a lightweight security scheme for detecting selective forwarding attacks. The detection scheme uses a multi-hop acknowledgement technique to launch alarms by obtaining responses from intermediate nodes. This scheme is efficient and reliable in the sense that an intermediate node reports any abnormal packet loss and suspect nodes to both the base station and the source node. To the best of our knowledge, this is the first paper that presents a detailed scheme for detecting selective forwarding attacks in the environment of sensor networks. The simulation results show that even when the channel error rate is 15%, simulating very harsh radio conditions, the detection accuracy of the proposed scheme is over 95%.

Routing protocols in wireless sensor network are vulnerable to various malicious security attacks that can degrade network performance and lifetime. This becomes more important in cluster routing protocols that is composed of multiple node and cluster head, such as low energy adaptive clustering hierarchy (LEACH) protocol. Namely, if an attack succeeds in failing the cluster head, then the entire set of nodes fail. Therefore, it is necessary to develop robust recovery schemes to overcome security attacks and recover packets at short times. Hence this paper proposes a detection and recovery scheme for selective forwarding attacks in wireless sensor networks using LEACH protocol. The proposed solution features near-instantaneous recovery times, without the requirement for feedback or retransmissions once an attack occurs.

A Wireless Sensor network (WSN) consists of large number of low cost low power sensor nodes. The nature of wireless sensor networks makes them very attractive to attackers. One of the most popular and serious attacks in wireless sensor networks is sink hole attack and most existing protocols to defend against this attack used cryptographic methods with keys. In sink hole attack a sensor node will have a lot of false neighbors. Wireless Sensor Network has a dynamic topology, intermittent connectivity, and resource constrained device nodes. Researchers over the past years have encouraged the use of mobile agent to overcome these challenges. The proposed scheme is to defend against sink hole attacks using mobile agents. Mobile agent is a program segment which is self controlling. They navigate from node to node not only transmitting data but also doing computation. They are an effective paradigm for distributed applications, and especially attractive in a dynamic network environment. A routing algorithm with multiple constraints is proposed based on mobile agents. It uses mobile agents to collect information of all mobile sensor nodes to make every node aware of the entire network so that a valid node will not listen the cheating information from malicious or compromised node which leads to sink hole attack. The significant feature of the proposed mechanism is that it does not need any encryption or decryption mechanism to detect the sink hole attack. This mechanism does not require more energy than normal routing protocols like AODV. Here we implement a simulation-based model of our solution to recover from a Sinkhole attack in a Wireless Sensor Network. The mobile agents were developed using the Aglet.

Enhancing cyber security in WSN using optimized self-attention-based provisional variational auto-encoder generative adversarial network

The increasing complexity and low-power constraints of current Wireless Sensor Networks (WSN) require efficient methodologies for network simulation and embedded software performance analysis of nodes. In addition, security is also a very important feature that has to be addressed in most WSNs, since they may work with sensitive data and operate in hostile unattended environments. In this paper, a methodology for security analysis of Wireless Sensor Networks is presented. The methodology allows designing attack-aware embedded software/firmware or attack countermeasures to provide security in WSNs. The proposed methodology includes attacker modeling and attack simulation with performance analysis (node’s software execution time and power consumption estimation). After an analysis of different WSN attack types, an attacker model is proposed. This model defines three different types of attackers that can emulate most WSN attacks. In addition, this paper presents a virtual platform that is able to model the node hardware, embedded software and basic wireless channel features. This virtual simulation analyzes the embedded software behavior and node power consumption while it takes into account the network deployment and topology. Additionally, this simulator integrates the previously mentioned attacker model. Thus, the impact of attacks on power consumption and software behavior/execution-time can be analyzed. This provides developers with essential information about the effects that one or multiple attacks could have on the network, helping them to develop more secure WSN systems. This WSN attack simulator is an essential element of the attack-aware embedded software development methodology that is also introduced in this work.

With the advancement of technology, the application of Wireless Sensor Networks (WSNs) has increased in various fields, such as; military, medical and business. WSNs have the ability to control geographic areas and obtaining data and processing it in different environments. Sensor nodes independently sample local information and if necessary send this information to neighboring sensors and eventually send it to the well. These networks are independent and autonomous and humans have no interference in it. In particular, all nodes are identical, and in practice they work together to meet the overall purpose of the network. Due to inherent constraints in the resources and computing power of sensor nodes, security in WSNs has different challenges in comparison with security in traditional computer networks. Low-cost sensor networks allow the expansion of sensor groups in a variety of environments which are capable of operating in military fields and in urban areas. On the other hand, unreliable communication channels in many applications make it more difficult to provide information security in these networks. One of the common attacks in WSNs is a wormhole attack. In this paper, we will review and evaluate paper related to the wormhole attack.