Robust multiple frequency multiple power localization schemes in the presence of multiple jamming attacks

Localization of the wireless sensor network is a vital area acquiring an impressive research concern and called upon to expand more with the rising of its applications. As localization is gaining prominence in wireless sensor network, it is vulnerable to jamming attacks. Jamming attacks disrupt communication opportunity among the sender and receiver and deeply impact the localization process, leading to a huge error of the estimated sensor node position. Therefore, detection and elimination of jamming influence are absolutely indispensable. Range-based techniques especially Received Signal Strength (RSS) is facing severe impact of these attacks. This paper proposes algorithms based on Combination Multiple Frequency Multiple Power Localization (C-MFMPL) and Step Function Multiple Frequency Multiple Power Localization (SF-MFMPL). The algorithms have been tested in the presence of multiple types of jamming attacks including capture and replay, random and constant jammers over a log normal shadow fading propagation model. In order to overcome the impact of random and constant jammers, the proposed method uses two sets of frequencies shared by the implemented anchor nodes to obtain the averaged RSS readings all over the transmitted frequencies successfully. In addition, three stages of filters have been used to cope with the replayed beacons caused by the capture and replay jammers. In this paper the localization performance of the proposed algorithms for the ideal case which is defined by without the existence of the jamming attack are compared with the case of jamming attacks. The main contribution of this paper is to achieve robust localization performance in the presence of multiple jamming attacks under log normal shadow fading environment with a different simulation conditions and scenarios

The Effects of Cognitive Jamming on Wireless Sensor Networks used for Geolocation

The increased use of Wireless Sensor Networks (WSN) for geolocation has led to an increased reliance on this technology. Jamming, protecting jamming, and detecting jamming in a WSN are areas of study that have greatly increased in interest. This research uses simulations and data collected from hardware experiments to test the effects of jamming on a WSN. Hardware jamming was tested using a Universal Software Radio Peripheral (USRP) Version 2 to assess the effects of jamming on a cooperative network of Java Sun SPOTs. The research combines simulations and data collected from the hardware experiments to see the effects of jamming on cooperative and noncooperative geolocation

Data analytics methods for attack detection and localization in wireless networks

Wireless ad hoc network operates without any fixed infrastructure and centralized administration. It is a group of wirelessly connected nodes having the capability to work as host and router. Due to its features of open communication medium, dynamic changing topology, and cooperative algorithm, security is the primary concern when designing wireless networks. Compared to the traditional wired network, a clean division of layers may be sacrificed for performance in wireless ad hoc networks. As a result, they are vulnerable to various types of attacks at different layers of the protocol stack. In this paper, I present real-time series data analysis solutions to detect various attacks including in- band wormholes attack in the network layer, various MAC layer misbehaviors, and jamming attack in the physical layer. And, I also investigate the problem of node localization in wireless and sensor networks, where a total of n anchor nodes are used to determine the locations of other nodes based on the received signal strengths. A range-based machine learning algorithm is developed to tackle the challenges --Abstract, page iii

A Framework for Localizing and Neutralization of Jammers in Wireless Networks.

The communication in wireless networks can be severely interrupted by Jammers and information about the jammers position can help to eliminate its attack. The main approach of this paper is to localize and neutralize the jammer in the network based on the energy(JSS) and finding the new route through the network i.e., best route through the network. The best route is defined as the minimum distance in the network that a sender node selects as its boundary or next hop node Thus, in this paper we aim to design a frame work to localize multiple jammers with high accuracy and minimal error factor. An effective means is to measure the strength of jamming signals (JSS). It conceives an evaluation feedback mechanism for localizing jammer as non linear optimization problem, to quantify result close to true jammer position. This paper focuses on localizing multiple jammer considering energy(JSS) as parameter for identifying a node as a jammer and using another parameter i.e., distance between nodes along with their energy(JSS) to find a best path for the communication in a network jammed scenario. DOI: 10.17762/ijritcc2321-8169.15057

Cognitive Security Framework For Heterogeneous Sensor Network Using Swarm Intelligence

Rapid development of sensor technology has led to applications ranging from academic to military in a short time span. These tiny sensors are deployed in environments where security for data or hardware cannot be guaranteed. Due to resource constraints, traditional security schemes cannot be directly applied. Unfortunately, due to minimal or no communication security schemes, the data, link and the sensor node can be easily tampered by intruder attacks. This dissertation presents a security framework applied to a sensor network that can be managed by a cohesive sensor manager. A simple framework that can support security based on situation assessment is best suited for chaotic and harsh environments. The objective of this research is designing an evolutionary algorithm with controllable parameters to solve existing and new security threats in a heterogeneous communication network. An in-depth analysis of the different threats and the security measures applied considering the resource constrained network is explored. Any framework works best, if the correlated or orthogonal performance parameters are carefully considered based on system goals and functions. Hence, a trade-off between the different performance parameters based on weights from partially ordered sets is applied to satisfy application specific requirements and security measures. The proposed novel framework controls heterogeneous sensor network requirements,and balance the resources optimally and efficiently while communicating securely using a multi-objection function. In addition, the framework can measure the affect of single or combined denial of service attacks and also predict new attacks under both cooperative and non-cooperative sensor nodes. The cognitive intuition of the framework is evaluated under different simulated real time scenarios such as Health-care monitoring, Emergency Responder, VANET, Biometric security access system, and Battlefield monitoring. The proposed three-tiered Cognitive Security Framework is capable of performing situation assessment and performs the appropriate security measures to maintain reliability and security of the system. The first tier of the proposed framework, a crosslayer cognitive security protocol defends the communication link between nodes during denial-of-Service attacks by re-routing data through secure nodes. The cognitive nature of the protocol balances resources and security making optimal decisions to obtain reachable and reliable solutions. The versatility and robustness of the protocol is justified by the results obtained in simulating health-care and emergency responder applications under Sybil and Wormhole attacks. The protocol considers metrics from each layer of the network model to obtain an optimal and feasible resource efficient solution. In the second tier, the emergent behavior of the protocol is further extended to mine information from the nodes to defend the network against denial-of-service attack using Bayesian models. The jammer attack is considered the most vulnerable attack, and therefore simulated vehicular ad-hoc network is experimented with varied types of jammer. Classification of the jammer under various attack scenarios is formulated to predict the genuineness of the attacks on the sensor nodes using receiver operating characteristics. In addition to detecting the jammer attack, a simple technique of locating the jammer under cooperative nodes is implemented. This feature enables the network in isolating the jammer or the reputation of node is affected, thus removing the malicious node from participating in future routes. Finally, a intrusion detection system using `bait\u27 architecture is analyzed where resources is traded-off for the sake of security due to sensitivity of the application. The architecture strategically enables ant agents to detect and track the intruders threateningthe network. The proposed framework is evaluated based on accuracy and speed of intrusion detection before the network is compromised. This process of detecting the intrusion earlier helps learn future attacks, but also serves as a defense countermeasure. The simulated scenarios of this dissertation show that Cognitive Security Framework isbest suited for both homogeneous and heterogeneous sensor networks