Survey on Security Management of Multiple Spoofing Attackers in Wireless Networks

Wireless spoofing attacks are simple to introduce and can importantly impact the performance of networks. In this paper, we propose to use spatial information a physical property related to every node, complex to mispresent and self reliant on cryptography, as the initializing for detecting spoofing attacks determining the number of attackers when multiple opponent masquerading as the same node identity and localizing multiple adversaries We propose to use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. For determining the number of attackers we are using cluster based mechanism. To localize the positions of multiple attackers, we have developed an integrated detection and localization system. The generated localization results with a representative set of algorithms provide strong evidence of high accuracy of localizing multiple adversaries. As the wireless networks are easily susceptible for various types of spoofing attacks, basically this paper focuses on Identity-based spoofing attacks and the enhanced and efficient techniques to secure from such attacks

Cluster Based Intrusion Detection Technique for Wireless Networks

Wireless networks are vulnerable to spoofing attacks, which allows for many other forms of attacks on the networks. Although th e identity of a node can be verified through cryptographic authentication, authentication is not always possible because it requires key management and additional infrastructural overhead. In this paper we propose a method for both detect ing spoofing attacks, as well as locating the positions of adversaries performing the attacks. We propose to use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. We then formulate the problem of determin ing the number of attackers as a multiclass detection problem. Cluster - based mechanisms are developed to determine the number of attackers. When the training data are available, we explore using the Support Vector Machines (SVM) method to further improve t he accuracy of determining the number of attackers. In addition, we developed an integrated detection and localization system that can localize the positions of multiple attackers. We evaluated our techniques through two test beds using both an 802.11 ( Wi - Fi ) network and an 802.15.4 network in two real office buildings. Our experimental results show that our proposed methods can achieve over 90 percent Hit Rate and Precision when determining the number of attackers. Our localizatio n results using a represen tative set of algorithms provide strong evidence of high accuracy of localizing multiple adversaries

Detection and Localization of Multiple Spoofing using GADE and IDOL in WSN

Abstract Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. Spatial information, a physical property associated with each node, that is hard to falsify, and not reliant on cryptography is used, as the basis for 1) detecting spoofing attacks; 2) determining the number of attackers when multiple adversaries masquerading as the same node identity; and 3) localizing multiple adversaries. Thus received signal strength (RSS) is inherited from wireless nodes to detect the spoofing attacks. Cluster-based mechanisms are developed to determine the number of attackers. In addition, an integrated detection and localization system is developed that can localize the positions of multiple attackers. Thus this detection and localization results provide strong evidence in detecting multiple adversaries

A New MAC Address Spoofing Detection Technique Based on Random Forests

Media access control (MAC) addresses in wireless networks can be trivially spoofed using off-the-shelf devices. The aim of this research is to detect MAC address spoofing in wireless networks using a hard-to-spoof measurement that is correlated to the location of the wireless device, namely the received signal strength (RSS). We developed a passive solution that does not require modification for standards or protocols. The solution was tested in a live test-bed (i.e., a wireless local area network with the aid of two air monitors acting as sensors) and achieved 99.77%, 93.16% and 88.38% accuracy when the attacker is 8–13 m, 4–8 m and less than 4 m away from the victim device, respectively. We implemented three previous methods on the same test-bed and found that our solution outperforms existing solutions. Our solution is based on an ensemble method known as random forests.https://doi.org/10.3390/s1603028

Location Spoofing Detection for VANETs by a Single Base Station in Rician Fading Channels

In this work we examine the performance of a Location Spoofing Detection System (LSDS) for vehicular networks in the realistic setting of Rician fading channels. In the LSDS, an authorized Base Station (BS) equipped with multiple antennas utilizes channel observations to identify a malicious vehicle, also equipped with multiple antennas, that is spoofing its location. After deriving the optimal transmit power and the optimal directional beamformer of a potentially malicious vehicle, robust theoretical analysis and detailed simulations are conducted in order to determine the impact of key system parameters on the LSDS performance. Our analysis shows how LSDS performance increases as the Rician K-factor of the channel between the BS and legitimate vehicles increases, or as the number of antennas at the BS or legitimate vehicle increases. We also obtain the counter-intuitive result that the malicious vehicle's optimal number of antennas conditioned on its optimal directional beamformer is equal to the legitimate vehicle's number of antennas. The results we provide here are important for the verification of location information reported in IEEE 1609.2 safety messages.Comment: 6 pages, 5 figures, Added further clarification on constraints imposed on the detection minimization strategy. Minor typos fixe