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We study the problem of tracking moving objects using distributed Wireless Sensor Networks (WSNs) in which sensors are deployed randomly. Due to the uncertainty and unpredictability of real-world objects' motion, the tracking algorithm is needed to adapt to real-time changes of velocities and directions of a moving target. Moreover, the energy consumption(More)
Decision problems with the features of prisoner's dilemma are quite common. A general solution to this kind of social dilemma is that the agents cooperate to play a joint action. The Nash bargaining solution is an attractive approach to such cooperative games. In this paper, a multi-agent learning algorithm based on the Nash bargaining solution is(More)
Machine learning has great utility within the context of network intrusion detection systems. In this paper, a behavior analysis-based learning framework for host level network intrusion detection is proposed, consisting of two parts, anomaly detection and alert verification. The anomaly detection module processes unlabeled data using a clustering algorithm(More)
Intrusions impose tremendous threats to today's computer hosts. Intrusions using security breaches to achieve unauthorized access or misuse of critical information can have catastrophic consequences. To protect computer hosts from the increasing threat of intrusion, various kinds of Intrusion Detection Systems (IDSs) have been developed. The main(More)
Data clustering is an important procedure to detect hidden patterns of a data set in a variety of fields, yet clustering analysis is a challenging problem, because many factors play together in devising and selecting a well tuned clustering technique and there are no predefined classes or examples to show whether the clusters are valid or not. In this(More)
In today's computing environment, unauthorized accesses and misuse of critical data can be catastrophic to personal users, businesses, emergency services, and even national defense and security. To protect computers from the ever-increasing threat of intrusion, we propose an event-driven architecture that provides fine grained intrusion detection and(More)
Conventional wearable sensors are mainly used to detect the physiological and activity information of individuals who wear them, but fail to perceive the information of the surrounding environment. This paper presents a wearable thermal sensing system to detect and perceive the information of surrounding human subjects. The proposed system is developed(More)
Oja's neuron is extended to find the dominant eigenvalue alongside the computation of the dominant eigenvector. This is achieved through a stochastic gradient descent learning rule that computes the second moment of the neuron output. The effectiveness of this family of learning rules is further demonstrated in a network that verifies the law of total(More)