Intrusion Detection in Mobile Ad-Hoc Networks using Bayesian Game Methodology

The dynamic and distributed nature of MANETs make them vulnerable to various types of attacks like black hole attack, traffic distortion, IP spoofing, DoS attack etc. Malicious nodes can launch attacks against other normal nodes and deteriorate the overall performance of the entire network [1�3]. Unlike in wired networks, there are no fixed checkpoints like router and switches in MANETs, where the Intrusion Detection System (IDS) can be deployed .However, due to limited wireless communication range and node mobility, nodes in MANET must cooperate with each other to provide networking services among themselves. Therefore, each node in a MANET acts both as a host and a router. Present Intrusion Detection Systems (IDSs) for MANETs require continuous monitoring which leads to rapid depletion of a node�s battery life. To avoid this issue we propose a system to prevent intrusion in MANET using Bayesian model based MAC Identification from multiple nodes in network. Using such system we can provide lightweight burden to nodes hence improving energy efficiency. Simulated results shows improvement in estimated delay and average bits transfer parameter

Intrusion Detection in Mobile Adhoc Network with Bayesian model based MAC Identification

Mobile Ad-hoc Networks (MANETs) are a collection of heterogeneous, infrastructure less, self-organizing and battery powered mobile nodes with different resources availability and computational capabilities. The dynamic and distributed nature of MANETs makes them suitable for deployment in extreme and volatile environmental conditions. They have found applications in diverse domains such as military operations, environmental monitoring, rescue operations etc. Each node in a MANET is equipped with a wireless transmitter and receiver, which enables it to communicate with other nodes within its wireless transmission range. However, due to limited wireless communication range and node mobility, nodes in MANET must cooperate with each other to provide networking services among themselves. Therefore, each node in a MANET acts both as a host and a router. Present Intrusion Detection Systems (IDSs) for MANETs require continuous monitoring which leads to rapid depletion of a node?s battery life. To avoid this issue we propose a system to prevent intrusion in MANET using Bayesian model based MAC Identification from multiple nodes in network. Using such system we can provide lightweight burden to nodes hence improving energy efficiency

Activity Recognition for Smart Building Application Using Complex Event Processing Approach

Activity recognition has become one of the most interesting and challenging subjects in performing surveillance or monitoring of smart building system. Although there are several systems already available in the market, limitations and several unresolved issues remain, especially when it involves complex engineering applications. As such, activity recognition is purposely incorporated in the smart system to detect simple and complex events that happen in the building. In all existing event detections, the complex event processing (CEP) approach has been used for the detection of complex events. The CEP is capable of abstracting meaningful events from various and heterogeneous data sources, filtering and processing both simple and complex events, as well as, producing fast mitigation action based on specific scenarios. The work reported in this paper intends to explain in detail on the development of activity recognition application using CAISER™ and NESPER© platform as well as the complex event detection that uses the CEP approach. In assessing the system performance, Matthew Coefficient Correlation (MCC) has been used as the main performance parameter.  Results obtained showed that the Temporal Constraint Template Match Detector (TCD) is more accurate, stable and better in complex event detection compared to NESPER© detector

A Generic Intrusion Detection and Diagnoser System Based on Complex Event Processing

This work presents a generic Intrusion Detection and Diagnosis System, which implements a comprehensive alert correlation workflow for detection and diagnosis of complex intrusion scenarios in Large scale Complex Critical Infrastructures. The on-line detection and diagnosis process is based on an hybrid and hierarchical approach, which allows to detect intrusion scenarios by collecting diverse information at several architectural levels, using distributed security probes, as well as perform complex event correlation based on a Complex Event Processing Engine. The escalation process from intrusion symptoms to the identified target and cause of the intrusion is driven by a knowledge-base represented by an ontology. A prototype implementation of the proposed Intrusion Detection and Diagnosis framework is also presented