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—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)
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)
— 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)
Distributed denial-of-service attack is one of the greatest threats to the Internet today. One of the biggest diiculties in defending against this attack is that attackers always use incorrect, or \spoofed" IP source addresses to disguise their true origin. In this paper, we present a packet marking algorithm which allows the victim to traceback the(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)
—A challenge in using machine learning for tasks such as network intrusion detection and fault diagnosis is the difficulty in obtaining clean data for training in order to model the normal behavior of the system. Unsupervised anomaly detection techniques such as one class support vector machines (SVMs) have been introduced to overcome this difficulty. One(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)