Exploiting Lack of Hardware Reciprocity for Sender-Node Authentication at the PHY Layer

This paper proposes to exploit the so-called reciprocity parameters (modelling non-reciprocal communication hardware) to use them as decision metric for binary hypothesis testing based authentication framework at a receiver node Bob. Specifically, Bob first learns the reciprocity parameters of the legitimate sender Alice via initial training. Then, during the test phase, Bob first obtains a measurement of reciprocity parameters of channel occupier (Alice, or, the intruder Eve). Then, with ground truth and current measurement both in hand, Bob carries out the hypothesis testing to automatically accept (reject) the packets sent by Alice (Eve). For the proposed scheme, we provide its success rate (the detection probability of Eve), and its performance comparison with other schemes

Achieving Energy Efficiency Using Fuzzy Logic-Based Cluster Algorithms in Wireless Sensor Networks

Now a days Wireless Sensor Network (WSN) [1, 2] is an emerging and challenging aspect in the field of communication research. It is a type of infrastructure-less wireless networks, which have the capability of self-configuration. These nodes are deployed for environmental data collection by measuring different environmental condition like moisture, pressure and temperature etc. The nodes are working continuous or waiting for event happening to send information. Energy consumption is a major issue in WSN. In this thesis, our proposed approach Fuzzy Based Energy Efficient Clustering in which cluster head is selected based on the distance to BS, remaining energy of node and node density. The non-head nodes join with the cluster head node based on the distance to CH, remaining energy of CH and CH density. The simulation results show that the proposed approach gives better performance than that of LEACH in terms of energy consumption and lifetime of the network for first node death and half of node death

Wireless sensor network performance analysis and effect of blackhole and sinkhole attacks

The widespread usage of Wireless sensor networks in various fields and application make it vulnerable to variety of security threats and attacks. These security attacks occur when an adversary compromised a sensor node by inject false measurements and divert real time network traffic. Sinkhole and Blackhole attacks are very common attacks in network, where an attacker advertises un-authorized routing update in network. To deal with these types of attacks, there is a need to tighten the network security and prevent from attackers. In this study, we discuss security threats and presents the effects of Black and Sink hole attacks. Further, the study presents related work and current issues in wireless sensor network. The simulation results illustrated that, how these attacks affect the network performance

Intrusion Detection in Homogeneous and Heterogeneous Wireless Sensor Networks (WSN)

Intrusion detection in Wireless Sensor Network (WSN) is of practical interest in many applications such as detecting an intruder in a combat zone. The intrusion detection is defined as machinery for a WSN to detect the subsistence of unfortunate, incorrect, or anomalous moving attackers. For this purpose, it is a fundamental issue to differentiate the WSN parameters such as node density and sensing range in terms of a desirable detection probability. In this paper, we consider this issue according to two WSN models: homogeneous and heterogeneous WSN. Furthermore, we derive the detection possibility by considering two sensing models: single-singing detection and multiple-sensing detection. In addition, we converse the network connectivity and broadcast reach ability, which are necessary conditions to make certain the corresponding detection probability in a WSN. Our simulation results validate the analytical values for both homogeneous and heterogeneous WSNs

Challenges of Misbehavior Detection in Industrial Wireless Networks

In recent years, wireless technologies are increasingly adopted in many application domains that were either unconnected before or exclusively used cable networks. This paradigm shift towards - often ad-hoc - wireless communication has led to significant benefits in terms of flexibility and mobility. Alongside with these benefits, however, arise new attack vectors, which cannot be mitigated by traditional security measures. Hence, mechanisms that are orthogonal to cryptographic security techniques are necessary in order to detect adversaries. In traditional networks, such mechanisms are subsumed under the term "intrusion detection system" and many proposals have been implemented for different application domains. More recently, the term "misbehavior detection" has been coined to encompass detection mechanisms especially for attacks in wireless networks. In this paper, we use industrial wireless networks as an exemplary application domain to discuss new directions and future challenges in detecting insider attacks. To that end, we review existing work on intrusion detection in mobile ad-hoc networks. We focus on physical-layer-based detection mechanisms as these are a particularly interesting research direction that had not been reasonable before widespread use of wireless technology.Peer Reviewe

Discrete R-Contiguous bit Matching mechanism appropriateness for anomaly detection in Wireless Sensor Networks

Resource exhaustion is one of the main challenges for the security of Wireless Sensor Networks (WSNs). The challenge can be addressed by using algorithms that are light weighted. In this paper use of light-weighted R-Contiguous Bit matching for attack detection in WSNs has been evaluated. Use of R-Contiguous bit matching in Negative Selection Algorithm (NSA) has improved the performance of anomaly detection resulting in low false positive, false negative and high detection rates. The proposed model has been tested against some of the attacks. The high detection rate has proved the appropriateness of R-Contiguous bit matching mechanism for anomaly detection in WSNs

3-WAY Secured WSN with CSDSM-DNN based Intrusion Detection Model

In Wireless Sensor Networks (WSNs), intrusion aims indegrading or even eliminating the capacity of these networks for providing their functions. Thus, in recent years, several ideas are brought and employed. However, these techniques still did not fulfill their requirements in attaining better classification accuracy. This paper proposes a novel Cosine Similarity Distance integrated Sammon Mapping learning layer-Deep Neural Network (CSDSM-DNN)-centricIntrusion Detection Model (IDM) in WSNfor attaining better outcomes. Initially, the nodes are clustered; after that, utilizing Binomial Distribution based Dwarf Mongoose Optimization (BD-DMO), the cluster heads are selected. Then, theIdentity Matrix Function-Kalman Filter (IMF-KF) identified the optimal route. Subsequently, the data is transferred via the secured route. The transferred data is pre-processed and then, the important features are selected. Lastly, to classify whether the data is attacked or non-attacked, the selected features are given into the CSDSM-DNN. Therefore, with the prevailing approaches, the experiential outcomes are evaluated and analogized and it exhibits the proposed model’s higher reliability and efficacy

Design and Implementation of Intrusion Detection Systems using RPL and AOVD Protocols-based Wireless Sensor Networks

Wireless Sensor Network (WSN) technology has grown in importance in recent years. All WSN implementations need secure data transmission between sensor nodes and base stations. Sensor node attacks introduce new threats to the WSN. As a result, an appropriate Intrusion Detection System (IDS) is required in WSN for defending against security attacks and detecting attacks on sensor nodes. In this study, we use the Routing Protocol for Low Power and Lossy Networks (RPL) for addressing security services in WSN by identifying IDS with a network size of more or less 20 nodes and introducing 10% malicious nodes. The method described above is used on Cooja in the VMware virtual machine Workstation with the InstantContiki2.7 operating system. To track the movement of nodes, find network attacks, and spot dropped packets during IDS in WSN, an algorithm is implemented in the Network Simulator (NS2) using the Ad-hoc On-Demand Distance Vector (AODV) protocol in the Linux operating system.Keywords—Intrusion Detection Systems, wireless sensor networks, Cooja simulator, sensor nodes, NS