Replication Attack Mitigations for Static and Mobile WSN

Security is important for many sensor network applications. Wireless Sensor Networks (WSN) are often deployed in hostile environments as static or mobile, where an adversary can physically capture some of the nodes. once a node is captured, adversary collects all the credentials like keys and identity etc. the attacker can re-program it and replicate the node in order to eavesdrop the transmitted messages or compromise the functionality of the network. Identity theft leads to two types attack: clone and sybil. In particularly a harmful attack against sensor networks where one or more node(s) illegitimately claims an identity as replicas is known as the node replication attack. The replication attack can be exceedingly injurious to many important functions of the sensor network such as routing, resource allocation, misbehavior detection, etc. This paper analyzes the threat posed by the replication attack and several novel techniques to detect and defend against the replication attack, and analyzes their effectiveness in both static and mobile WSN.Comment: 12 page

Clone Node Detection in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are often deployed in unfavourable situations where an assailant can physically capture some of the nodes, first can reprogram, and then, can replicate them in a large number of clones, easily taking control over the network. This replication node is also called as Clone node. The clone node or replicated node behave as a genuine node. It can damage the network. In node replication attack detecting the clone node important issue in Wireless Sensor Networks. A few distributed solutions have been recently proposed, but they are not satisfactory. First, they are intensity and memory demanding: A serious drawback for any protocol to be used in the WSN- resource constrained environment. In this project first investigate the selection criteria of clone detection schemes with regard to device types, detection methodologies, deployment strategies, and detection ranges. Further, they are vulnerable to the specific assailant models introduced in this paper. In this scenario, a particularly dangerous attack is the replica attack, in which the assailant takes the secret keying materials from a compromised node, generates a large number of assailant-controlled replicas that share the node’s keying materials and ID, and then spreads these replicas throughout the network. With a single captured node, the assailant can create as many replica nodes as he has the hardware to generate.. The replica nodes are controlled by the assailant, but have keying materials that allow them to seem like authorized participants in the network. Our implementation specifies, user will specify its ID, which means client id, secret key will be create, and then include the port number. The witness node will verify the internally bounded user Id and secret key. The witness node means original node. If the verification is success, the information collecting to the packets that packets are send to the destination

Patrol Detection for Replica Attacks on Wireless Sensor Networks

Replica attack is a critical concern in the security of wireless sensor networks. We employ mobile nodes as patrollers to detect replicas distributed in different zones in a network, in which a basic patrol detection protocol and two detection algorithms for stationary and mobile modes are presented. Then we perform security analysis to discuss the defense strategies against the possible attacks on the proposed detection protocol. Moreover, we show the advantages of the proposed protocol by discussing and comparing the communication cost and detection probability with some existing methods

Node-Replication Attack Detection in Vehicular Ad-hoc Networks based on Automatic Approach

Recent advances in smart cities applications enforce security threads such as node replication attacks. Such attack is take place when the attacker plants a replicated network node within the network. Vehicular Ad hoc networks are connecting sensors that have limited resources and required the response time to be as low as possible. In this type networks, traditional detection algorithms of node replication attacks are not efficient. In this paper, we propose an initial idea to apply a newly adapted statistical methodology that can detect node replication attacks with high performance as compared to state-of-the-art techniques. We provide a sufficient description of this methodology and a road-map for testing and experiment its performance

Zone Based Node Replica Detection in Wireless Sensor Network Using Trust

Abstract-A wireless sensor network is a group of sensor nodes. Sensor senses the environment, process data and communicates wirelessly over a short distance. Wireless sensor network has been used in various critical applications. Hence security is of prime concern. The node replication attack is one in which an adversary captures a sensor node and creates copies of the captured sensor node. These newly created copies called clone nodes will be placed at strategic locations in the network, from where they can provide many insider attacks. Replica node will act like an authenticated sensor node. Hence it will be very difficult to detect the clone nodes. In zone based node replica detection with trust, the entire network is divided into a number of zones and node replica detection is done based on the values of trust values calculated

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

Security and Privacy for Green IoT-based Agriculture: Review, Blockchain solutions, and Challenges

open access articleThis paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture