There have been many warnings that automobiles are vulnerable to the attacks through the network, CAN which connects the ECUs (Electrical Control Units) embedded in the automobiles. Some previous studies showed that the warnings were actual treats. They analyzed the packets flowing on the network and used the packets constructed based on the analysis. We show that it is possible to attack automobiles without any in-depth knowledge about automobiles and specially designed tools to analyze the packets. Experiments are performed in two phases. In the first phase, the victims automobiles are attacked with the packets constructed with the CAN IDs gathered from the sniffed packets flowing in the automobiles. It is not a problem at all to gather CANIDs since CAN is an open simple standard protocol and there are many tools to sniff CAN packets in the Internet. In the second phase, the attack packets are constructed in a completely random manner without any previous information such as CAN IDs. The packets are injected into the network via Bluetooth, a wireless channel. Through the experiments, we show the network vulnerability of automobiles.

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.