RIDES: Robust Intrusion Detection System for IP-Based Ubiquitous Sensor Networks

The IP-based Ubiquitous Sensor Network (IP-USN) is an effort to build the “Internet of things”. By utilizing IP for low power networks, we can benefit from existing well established tools and technologies of IP networks. Along with many other unresolved issues, securing IP-USN is of great concern for researchers so that future market satisfaction and demands can be met. Without proper security measures, both reactive and proactive, it is hard to envisage an IP-USN realm. In this paper we present a design of an IDS (Intrusion Detection System) called RIDES (Robust Intrusion DEtection System) for IP-USN. RIDES is a hybrid intrusion detection system, which incorporates both Signature and Anomaly based intrusion detection components. For signature based intrusion detection this paper only discusses the implementation of distributed pattern matching algorithm with the help of signature-code, a dynamically created attack-signature identifier. Other aspects, such as creation of rules are not discussed. On the other hand, for anomaly based detection we propose a scoring classifier based on the SPC (Statistical Process Control) technique called CUSUM charts. We also investigate the settings and their effects on the performance of related parameters for both of the components

Enhanced Prediction of Network Attacks Using Incomplete Data

For years, intrusion detection has been considered a key component of many organizations’ network defense capabilities. Although a number of approaches to intrusion detection have been tried, few have been capable of providing security personnel responsible for the protection of a network with sufficient information to make adjustments and respond to attacks in real-time. Because intrusion detection systems rarely have complete information, false negatives and false positives are extremely common, and thus valuable resources are wasted responding to irrelevant events. In order to provide better actionable information for security personnel, a mechanism for quantifying the confidence level in predictions is needed. This work presents an approach which seeks to combine a primary prediction model with a novel secondary confidence level model which provides a measurement of the confidence in a given attack prediction being made. The ability to accurately identify an attack and quantify the confidence level in the prediction could serve as the basis for a new generation of intrusion detection devices, devices that provide earlier and better alerts for administrators and allow more proactive response to events as they are occurring

SecSip: A Stateful Firewall for SIP-based Networks

SIP-based networks are becoming the de-facto standard for voice, video and instant messaging services. Being exposed to many threats while playing an major role in the operation of essential services, the need for dedicated security management approaches is rapidly increasing. In this paper we present an original security management approach based on a specific vulnerability aware SIP stateful firewall. Through known attack descriptions, we illustrate the power of the configuration language of the firewall which uses the capability to specify stateful objects that track data from multiple SIP elements within their lifetime. We demonstrate through measurements on a real implementation of the firewall its efficiency and performance

FOREVER: Fault/intrusiOn REmoVal through Evolution & Recovery

The goal of the FOREVER project is to develop a service for Fault/intrusiOn REmoVal through Evolution & Recovery. In order to achieve this goal, our work addresses three main tasks: the definition of the FOREVER service architecture; the analysis of how diversity techniques can improve resilience; and the evaluation of the FOREVER service. The FOREVER service is an important contribution to intrustion-tolerant replication middleware and significantly enhances the resilience