A New Localization Scheme with Statistical Estimation against Wormhole Attack in Wireless Sensor Networks

Wormhole attack is one of the most devastating threats for range-free localization in wireless sensor networks. In this paper, we evaluate three statistical estimation methods with the same network model and geographic information obtailaed by the DV-Hop algorithm. We analyze the limits of Minimum Mean Square Estimate （MMSE）, Least Median of Squares （LMS） and Enhanced greedy At- tack-Resistant MMSE （EARMMSE） and propose an improved EARMMSE with the hop-distance relationship, named EARMMSE＋. Simulation results illustrate the performance of MMSE, LMS and EARMMSE＋ with different anchor fraction, the length of wormhole link and the average local neighborhood and show that EARMMSE＋ outperforms MMSE and LMS.

Defending Against Wormhole Attack in OLSR

OLSR （optimal link state routing） is one of the four basic routing protocols used in mobile ad hoe Networks by the MANET working group of IETF（Internet engineering task force）. OLSR, a proactive routing protocol, is based on a multipoint relaying flooding technique to reduce the number of topology broadcast. OLSR uses periodic HELLO packets to neighbor detection. As introduced in Reference [1], the wormhole attack can form a serious threat in wireless Networks, especially against many ad hoc Network routing protocols and location-based wireless security systems. Here, a trust model to handle this attack in OLSR is provided and simulated in NS2.

Recovery from Wormhole Attack in Mobile Ad Hoc Network (MANET)

Wormhole attack is a serious threat against MANET (mobile ad hoc network) and its routing protocols. A new approach—tunnel key node identification (TKNI) was proposed. Based on tunnel-key-node identification and priority-based route discovery, TKNI can rapidly rebuild the communications that have been blocked by wormhole attack. Compared to previous approaches, the proposed approach aims at both static and dynamic topology environment, involves addressing visible and invisible wormhole attack modes, requires no extra hardware, has a low overhead, and can be easily applied to MANET.

Multi-Step Detection of Simplex and Duplex Wormhole Attacks over Wireless Sensor Networks

Detection of thewormhole attacks is a cumbersome process,particularly simplex and duplex over thewireless sensor networks(WSNs).Wormhole attacks are characterized as distributed passive attacks that can destabilize or disable WSNs.The distributed passive nature of these attacks makes them enormously challenging to detect.The main objective is to find all the possible ways in which how the wireless sensor network’s broadcasting character and transmission medium allows the attacker to interrupt network within the distributed environment.And further to detect the serious routing-disruption attack“Wormhole Attack”step by step through the different network mechanisms.In this paper,a new multi-step detection(MSD)scheme is introduced that can effectively detect the wormhole attacks for WSN.The MSD consists of three algorithms to detect and prevent the simplex and duplex wormhole attacks.Furthermore,the proposed scheme integrated five detection modules to systematically detect,recover,and isolate wormhole attacks.Simulation results conducted inOMNET++show that the proposedMSDhas lower false detection and false toleration rates.Besides,MSDcan effectively detect wormhole attacks in a completely distributed network environment,as suggested by the simulation results.

Forced Collision:Detecting Wormhole Attacks with Physical Layer Network Coding

Previous research on security of network coding focused on the protection of data dissemination procedures and the detection of malicious activities such as pollution attacks. The capabilities of network coding to detect other attacks have not been fully explored. In this paper, we propose a new mechanism based on physical layer network coding to detect wormhole attacks. When two signal sequences collide at the receiver, the starting point of the collision is determined by the distances between the receiver and the senders. Therefore, by comparing the starting points of the collisions at two receivers, we can estimate the distance between them and detect fake neighbor connections via wormholes. While the basic idea is clear, we have proposed several schemes at both physical and network layers to transform the idea into a practical approach. Simulations using BPSK modulation at the physical layer show that the wireless nodes can effectively detect fake neighbor connections without the adoption of special hardware or time synchronization.

Genetic-based Fuzzy IDS for Feature Set Reduction and Worm Hole Attack Detection

The wireless ad-hoc networks are decentralized networks with a dynamic topology that allows for end-to-end communications via multi-hop routing operations with several nodes collaborating themselves,when the destination and source nodes are not in range of coverage.Because of its wireless type,it has lot of security concerns than an infrastructure networks.Wormhole attacks are one of the most serious security vulnerabilities in the network layers.It is simple to launch,even if there is no prior network experience.Signatures are the sole thing that preventive measures rely on.Intrusion detection systems(IDS)and other reactive measures detect all types of threats.The majority of IDS employ features from various network layers.One issue is calculating a huge layered features set from an ad-hoc network.This research implements genetic algorithm(GA)-based feature reduction intrusion detection approaches to minimize the quantity of wireless feature sets required to identify worm hole attacks.For attack detection,the reduced feature set was put to a fuzzy logic system(FLS).The performance of proposed model was compared with principal component analysis(PCA)and statistical parametric mapping(SPM).Network performance analysis like delay,packet dropping ratio,normalized overhead,packet delivery ratio,average energy consumption,throughput,and control overhead are evaluated and the IDS performance parameters like detection ratio,accuracy,and false alarm rate are evaluated for validation of the proposed model.The proposed model achieves 95.5%in detection ratio with 96.8%accuracy and produces very less false alarm rate(FAR)of 14%when compared with existing techniques.