The ability to dynamically collect and analyze threat data and to accurately report the current battlefield situation is critical in the face of emergent hostile attacks, and enables battlefield helicopters to continually function despite of potential threats. The paper is to model threats to battlefield helicopters, which represents a specific threat pattern and a methodology that compiles the threat into a set of rules using machine learning algorithms. This methodology based upon the inductive threat model can be used to detect real-time threats. We report experimental results that demonstrate the distinctive and predictive patterns of threats in simulated battlefield settings, and show the potential of compilation methods for the successful detection of threat systems.

A new approach to increase the throughput of firewall is proposed. Packet filtering and connection management usually done in firewall are relocated to the proposed firewall accelerator engine, named FA. FA also learns the behavior of firewall in an intelligent and unique way. With the knowledge acquired by the self-learning, FA can significantly reduce the number of packets to be fed to firewall and let firewall focus on content filtering that is widely used for protecting the Internet hackings. Some empirical studies show the proposed without changing the existing (or guessing) network configuration.

In this paper, we present an automated Web page classifier based on adaptive ontology. As a first step, to identify the representative terms given a set of classes, we compute the product of term frequency and document frequency. Secondly, the information gain of each term prioritizes it based on the possibility of classification. We compile the selected terms and classification into rules using machine learning algorithms. The compiled rules classify any Web page into categories defined on a domain ontology. In the experiments, 11 terms out of 1,700 terms were identified as representative features given a set of Web pages. The resulting accuracy of the classification was, on the average, 95.2%.

Scheduling policies for real-time tasks which receive rewards that depend on the amount of service received were presented by Dey et al.(1996). The policies utilized heuristic approaches to maximize the total accrued reward. In this comment, we propose an extension of their work that may reduce the average computational complexity.

The authors develop a VHDL based popularity-independent multimedia-on-demand distributed server model. The three server subsystems: the control subsystem, the communication subsystem and the storage subsystem are modeled. The communication subsystem is designed as an interconnection network with meshed-pipe structure and delivers packets to their destination through the sub-optimal shortest path. A model for the video traffic generated at the server side for modeling the data sent to the users is also presented. We use simulation experiments to validate our modeling approach and compare the results with those obtained using the OPNET simulation tool. The comparison results show that our approach is accurate compared to the simulation results. Our modeling approach also shows that using VHDL (very high speed integrated circuits hardware description language) in modeling digital systems is powerful and accurate.

A method to complement the tables for Boolean equations was presented by S.H. Unger (see ibid., vol. 43, no. 3, pp. 591-596, 1994). The method was utilized to solve the equations of the general form. We present a way to perform Exclusive-OR operation between two tabular type Boolean expressions. The proposed method allows to solve the equations in a rather direct manner.>