Christopher Leckie

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This article presents a survey of denial of service attacks and the methods that have been proposed for defense against these attacks. In this survey, we analyze the design decisions in the Internet that have created the potential for denial of service attacks. We review the state-of-art mechanisms for defending against denial of service attacks, compare(More)
Most current network intrusion detection systems employ signature-based methods or data mining-based methods which rely on labeled training data. This training data is 90 typically expensive to produce. Moreover, these methods have difficulty in detecting new types of attack. In this paper, we have discussed anomaly based instruction detection, pros and(More)
Anomaly detection is an important challenge for tasks such as fault diagnosis and intrusion detection in energy constrained wireless sensor networks. A key problem is how to minimise the communication overhead in the network while performing in-network computation when detecting anomalies. Our approach to this problem is based on a formulation that uses(More)
In this paper, we introduce a practical scheme to defend against Distributed Denial of Service (DDoS) attacks based on IP source address filtering. The edge router keeps a history of all the legitimate IP addresses which have previously appeared in the network. When the edge router is overloaded, this history is used to decide whether to admit an incoming(More)
Anomaly detection in wireless sensor networks is an important challenge for tasks such as intrusion detection and monitoring applications. This paper proposes two approaches to detecting anomalies from measurements from sensor networks. The first approach is a linear programming-based hyperellipsoidal formulation, which is called a centered hyperellipsoidal(More)
Security is a critical challenge for creating robust and reliable sensor networks. For example, routing attacks have the ability to disconnect a sensor network from its central base station. In this paper, we present a method for intrusion detection in wireless sensor networks. Our intrusion detection scheme uses a clustering algorithm to build a model of(More)
Identifying misbehaviors is an important challenge for monitoring, fault diagnosis and intrusion detection in wireless sensor networks. A key problem is how to minimise the communication overhead and energy consumption in the network when identifying misbehaviors. Our approach to this problem is based on a distributed, cluster-based anomaly detection(More)
Very large (VL) data or big data are any data that you cannot load into your computer’s working memory. This is not an objective definition, but a definition that is easy to understand and one that is practical, because there is a dataset too big for any computer you might use; hence, this is VL data for you. Clustering is one of the primary tasks used in(More)
In this paper, we propose a simple but robust scheme to detect denial of service attacks (including distributed denial of service attacks) by monitoring the increase of new IP addresses. Unlike previous proposals for bandwidth attack detection schemes which are based on monitoring the traffic volume, our scheme is very effective for highly distributed(More)