RIDA: Robust Intrusion Detection in Ad Hoc Networks

We focus on detecting intrusions in wireless ad hoc networks using the misuse detection technique. We allow for detection modules that periodically fail to detect attacks and also generate false positives. Combining theories of hypothesis testing and approximation algorithms, we develop a framework to counter different threats while minimizing the resource consumption. We obtain computationally simple optimal rules for aggregating and thereby minimizing the errors in the decisions of the nodes executing the intrusion detection software (IDS) modules. But, we show that the selection of the optimal set of nodes for executing the IDS is an NP-hard problem. We present a polynomial complexity selection algorithm that attains a guaranteeable approximation bound. We also modify this algorithm to allow for seamless operation in time varying topologies, and evaluate the efficacy of the approximation algorithm and its modifications using simulation. We identify a selection algorithm that attains a good balance between performance and complexity for attaining robust intrusion detection in ad hoc networks

Intrusion and Anomaly Detection Model Exchange for Mobile Ad-Hoc Networks

Mobile Ad-hoc NETworks (MANETs) pose unique security requirements and challenges due to their reliance on open, peer-to-peer models that often don't require authentication between nodes. Additionally, the limited processing power and battery life of the devices used in a MANET also prevent the adoption of heavy-duty cryptographic techniques. While traditional misuse-based Intrusion Detection Systems (IDSes) may work in a MANET, watching for packet dropouts or unknown outsiders is difficult as both occur frequently in both malicious and non-malicious traffic. Anomaly detection approaches hold out more promise, as they utilize learning techniques to adapt to the wireless environment and flag malicious data. The anomaly detection model can also create device behavior profiles, which peers can utilize to help determine its trustworthiness. However, computing the anomaly model itself is a time-consuming and processor-heavy task. To avoid this, we propose the use of model exchange as a device moves between different networks as a means to minimize computation and traffic utilization. Any node should be able to obtain peers' model(s) and evaluate it against its own model of "normal" behavior. We present this model, discuss scenarios in which it may be used, and provide preliminary results and a framework for future implementation

A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: Towards Universal and Resilient Systems

The Internet-of-Things (IoT) is rapidly becoming ubiquitous. However the heterogeneous nature of devices and protocols in use, the sensitivity of the data contained within, as well as the legal and privacy issues, make security for the IoT a growing research priority and industry concern. With many security practices being unsuitable due to their resource intensive nature, it is deemed important to include second line defences into IoT networks. These systems will also need to be assessed for their efficacy in a variety of different network types and protocols. To shed light on these issues, this paper is concerned with advancements in intrusion detection practices in IoT. It provides a comprehensive review of current Intrusion Detection Systems (IDS) for IoT technologies, focusing on architecture types. A proposal for future directions in IoT based IDS are then presented and evaluated. We show how traditional practices are unsuitable due to their inherent features providing poor coverage of the IoT domain. In order to develop a secure, robust and optimised solution for these networks, the current research for intrusion detection in IoT will need to move in a different direction. An example of which is proposed in order to illustrate how malicious nodes might be passively detected

A Framework for Misuse Detection in Ad Hoc Networks—Part I

We consider ad hoc networks with multiple, mobile intruders. We investigate the placement of the intrusion detection modules for misuse-based detection strategy. Our goal is to maximize the detection rate subject to limited availability of communication and computational resources. We mathematically formulate this problem, and show that computing the optimal solution is NP-hard. Thereafter, we propose two approximation algorithms that approximate the optimal solution within a constant factor, and prove that they attain the best possible approximation ratios. The approximation algorithms though require recomputation every time the topology changes. Thereafter, we modify these algorithms to adapt seamlessly to topological changes. We obtain analytical expressions to quantify the resource consumption versus detection rate tradeoffs for different algorithms. Using analysis and simulation, we evaluate these algorithms, and identify the appropriate algorithms for different detection rate and resource consumption tradeoffs

A statistical framework for intrusion detection in ad hoc networks

We focus on detecting intrusions in ad hoc networks using the misuse detection technique. We allow for detection modules that periodically fail to detect attacks and also generate false positives. Combining theories of hypothesis testing and approximation algorithms, we develop a framework to counter different threats while minimizing the resource consumption. We obtain computationally simple optimal rules for aggregating and thereby minimizing the errors in the decisions of the nodes executing the intrusion detection software (IDS) modules. But, we show that the selection of the optimal set of nodes for executing the IDS is an NP-hard problem. We describe a polynomial complexity, distributed selection algorithm, Maximum Unsatisfied Neighbors in Extended Neighborhood (MUNEN) that attains the best possible approximation ratio. The aggregation rules and MUNEN can be executed by mobile nodes with limited processing power. The overall framework provides a good balance between complexity and performance for attaining robust intrusion detection in ad hoc networks

Study on Doping Prevention: A map of Legal, Regulatory and Prevention Practice Provisions in EU 28

Historically, anti-doping efforts have focused on the detection and deterrence of doping in elite and competitive sport. There is, however, a growing concern that doping is occurring outside the organised sporting system; giving rise to the belief that the misuse of doping agents in recreational sport has become a societal problem and a public health issue that must be addressed. The EU Commission awarded a contract (EAC/2013/0617) to a Consortium to undertake this Study with the aim of developing the evidence-base for policies designed to combat doping in recreational sport. Fourteen internationally recognised experts shaped the Study which comprised (i) the collection of primary data through a structured survey, and (ii) secondary data through literature searches and website analysis. All 28 Member States participated in the information-gathering process. Specifically, this involved a systematic study of the ethical considerations, legal position, prevention research landscape, and current practise in relation to the prevention of doping in recreational sport. The Study provides a comprehensive overview of current practice and legislation as it applies to the prevention of doping and promotes and supports the sharing of best practices in the EU regarding the fight against doping in recreational sport. It concludes with seven recommendations for future action that focus on the need for a coordinated response in relation to the problems arising from doping in recreational sport