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

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

Detection and Localization of IDS Based Spoofing Attackers in Wireless Sensor Networks

A Wireless sensor network consists of a series of sensing devices. These track parameters such as those required for tracking and surveillance and then effectively passes on this information with other such sensors over a specific geographical area within the wireless network. The problem with traditional wireless networks lies in the way that they are positioned in an unattended manner, being controlled remotely by the network operator. This opens up a pathway for attackers, which compromise and capture wireless nodes and launch a variety of attacks that impair the functioning of the system. The proposed system aims to localize and cluster these nodes together, according to their position, wherein the cluster head acts as an Intrusion Detection system by monitoring node behavior such as packet transmission. This information is used to identify the attacked nodes in the wireless sensor network

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