Search results for "Intrusion detection system"
showing 10 items of 51 documents
An Efficient Intrusion Detection System for Selective Forwarding and Clone Attackers in IPv6-based Wireless Sensor Networks under Mobility
2017
Security in mobile wireless sensor networks is a big challenge because it adds more complexity to the network in addition to the problems of mobility and the limited sensor node resources. Even with authentication and encryption mechanisms, an attacker can compromise nodes and get all the keying materials. Therefore, an intrusion detection system is necessary to detect and defend against the insider attackers. Currently, there is no intrusion detection system applied to IPv6-based mobile wireless sensor networks. This paper is mainly interested in detecting the selective forwarding and clone attacks because they are considered among the most dangerous attackers. In this work, the authors de…
Machine Learning Techniques for Intrusion Detection: A Comparative Analysis
2016
International audience; With the growth of internet world has transformed into a global market with all monetary and business exercises being carried online. Being the most imperative resource of the developing scene, it is the vulnerable object and hence needs to be secured from the users with dangerous personality set. Since the Internet does not have focal surveillance component, assailants once in a while, utilizing varied and advancing hacking topologies discover a path to bypass framework " s security and one such collection of assaults is Intrusion. An intrusion is a movement of breaking into the framework by compromising the security arrangements of the framework set up. The techniq…
Combining conjunctive rule extraction with diffusion maps for network intrusion detection
2013
Network security and intrusion detection are important in the modern world where communication happens via information networks. Traditional signature-based intrusion detection methods cannot find previously unknown attacks. On the other hand, algorithms used for anomaly detection often have black box qualities that are difficult to understand for people who are not algorithm experts. Rule extraction methods create interpretable rule sets that act as classifiers. They have mostly been combined with already labeled data sets. This paper aims to combine unsupervised anomaly detection with rule extraction techniques to create an online anomaly detection framework. Unsupervised anomaly detectio…
Estimating Accuracy of Mobile-Masquerader Detection Using Worst-Case and Best-Case Scenario
2006
In order to resist an unauthorized use of the resources accessible through mobile terminals, masquerader detection means can be employed. In this paper, the problem of mobile-masquerader detection is approached as a classification problem, and the detection is performed by an ensemble of one-class classifiers. Each classifier compares a measure describing user behavior or environment with the profile accumulating the information about past behavior and environment. The accuracy of classification is empirically estimated by experimenting with a dataset describing the behavior and environment of two groups of mobile users, where the users within groups are affiliated with each other. It is as…
Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
2019
In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of …
Intrusion Detection and Ejection Framework Against Lethal Attacks in UAV-Aided Networks: A Bayesian Game-Theoretic Methodology
2017
International audience; Advances in wireless communications and microelectronics have spearheaded the development of unmanned aerial vehicles (UAVs), which can be used to augment a ground network composed of sensors and/or vehicles in order to increase coverage, enhance the end-to-end delay, and improve data processing. While UAV-aided networks can potentially find applications in many areas, a number of issues, particularly security, have not been readily addressed. The intrusion detection system is the most commonly used technique to detect attackers. In this paper, we focus on addressing two main issues within the context of intrusion detection and attacker ejection in UAV-aided networks…
Dynamic Distributed Intrusion Detection for Secure Multi-Robot Systems
2009
A general technique to build a dynamic and distributed intrusion detector for a class of multi–agent systems is proposed in this paper, by which misbehavior in the motion of one or more agents can be discovered. Previous work from the authors has focused on how to distinguish the behavior of a misbehaving agent in a completely distributed way, by developing a solution where agents act as local monitors of their neighbors and use locally sensed information as well as data received from other monitors at a particular time. In this work, we improve the system detection capability by allowing monitors to use information collected at different instants and thus realizing a dynamic state observer…
Decentralized intrusion detection for secure cooperative multi-agent systems
2007
In this paper we address the problem of detecting faulty behaviors of cooperative mobile agents. A novel decentralized and scalable architecture that can be adopted to realize a monitor of the agents’ behavior is proposed. We consider agents which may perform different independent tasks, but cooperate to guarantee the entire system’s safety. Agents plan their next actions by following a set of rules which is shared among them. Such rules are decentralized, i.e. they dictate actions that depend only on configurations of neighboring agents. Some agents may not be acting according to this cooperation protocol, due to tampering or spontaneous failure. To detect such misbehaviors we propose a so…
Consensus-based Distributed Intrusion Detection for Multi-Robot Systems
2008
This paper addresses a security problem in robotic multi-agent systems, where agents are supposed to cooperate according to a shared protocol. A distributed Intrusion Detection System (IDS) is proposed here, that detects possible non-cooperative agents. Previous work by the authors showed how single monitors embedded on-board the agents can detect non- cooperative behavior, using only locally available information. In this paper, we allow such monitors to share the collected information in order to overcome their sensing limitation. In this perspective, we show how an agreement on the type of behavior of a target-robot may be reached by the monitors, through execution of a suitable consensu…
Distributed Intrusion Detection for the Security of Industrial Cooperative Robotic Systems
2014
Abstract This paper addresses the problem of detecting possible intruders in a group of autonomous robots which coexist in a shared environment and interact with each other according to a set of common rules. We consider intruders as robots which misbehave, i.e. do not follow the rules, because of either spontaneous failures or malicious reprogramming. Our goal is to detect intruders by observing the congruence of their behavior with the social rules as applied to the current state of the overall system. Moreover, in accordance with the fully distributed nature of the problem, the detection itself must be performed by individual robots, based only on local information. We present a general …