Intrusion-aware Alert Validation Algorithm for Cooperative Distributed Intrusion Detection Schemes of Wireless Sensor Networks

Existing anomaly and intrusion detection schemes of wireless sensor networks have mainly focused on the detection of intrusions. Once the intrusion is detected, an alerts or claims will be generated. However, any unidentified malicious nodes in the network could send faulty anomaly and intrusion claims about the legitimate nodes to the other nodes. Verifying the validity of such claims is a critical and challenging issue that is not considered in the existing cooperative-based distributed anomaly and intrusion detection schemes of wireless sensor networks. In this paper, we propose a validation algorithm that addresses this problem. This algorithm utilizes the concept of intrusion-aware reliability that helps to provide adequate reliability at a modest communication cost. In this paper, we also provide a security resiliency analysis of the proposed intrusion-aware alert validation algorithm.Comment: 19 pages, 7 figure

NeuDetect: A neural network data mining system for wireless network intrusion detection

This thesis proposes an Intrusion Detection System, NeuDetect, which applies Neural Network technique to wireless network packets captured through hardware sensors for purposes of real time detection of anomalous packets. To address the problem of high false alarm rate confronted by the current wireless intrusion detection systems, this thesis presents a method of applying the artificial neural networks technique to the wireless network intrusion detection system. The proposed system solution approach is to find normal and anomalous patterns on preprocessed wireless packet records by comparing them with training data using Back-propagation algorithm. An anomaly score is assigned to each packet by calculating the difference between the output error and threshold. If the anomaly score is positive then the wireless packet is flagged as anomalous and is negative then the packet is flagged as normal. If the anomaly score is zero or close to zero it will be flagged as an unknown attack and will be sent back to training process for re-evaluation

A lightweight and secure multilayer authentication scheme for wireless body area networks in healthcare system

Wireless body area networks (WBANs) have lately been combined with different healthcare equipment to monitor patients' health status and communicate information with their healthcare practitioners. Since healthcare data often contain personal and sensitive information, it is important that healthcare systems have a secure way for users to log in and access resources and services. The lack of security and presence of anonymous communication in WBANs can cause their operational failure. There are other systems in this area, but they are vulnerable to offline identity guessing attacks, impersonation attacks in sensor nodes, and spoofing attacks in hub node. Therefore, this study provides a secure approach that overcomes these issues while maintaining comparable efficiency in wireless sensor nodes and mobile phones. To conduct the proof of security, the proposed scheme uses the Scyther tool for formal analysis and the Canetti–Krawczyk (CK) model for informal analysis. Furthermore, the suggested technique outperforms the existing symmetric and asymmetric encryption-based schemes

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)