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The Domain Name System (DNS) is a critical infrastructure in the Internet; thus, monitoring its traffic, and protecting DNS from malicious activities are important for security in cyberspace. However, it is often difficult to determine whether a DNS query is caused by malicious or normal activity, because information available in DNS traffic is limited.We focus on the activities of mass-mailing worms and propose a method to detect hosts infected by mass-mailing worms by mining DNS traffic data. Our method begins with a small amount of a priori knowledge about a signature query. By assuming that queries sent by most hosts that have sent the signature query of worms have been sent by worm behavior, we detect infected hosts using Bayesian estimation.We apply our method to DNS traffic data captured at one of the largest commercial Internet Service Providers in Japan, and the experimental result indicates that an 89% reduction of mail exchange queries can be achieved with the method.

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The Botnet threats, such as server attacks or sending of spam email, have been increasing. A method of using a blacklist of domain names has been proposed to find infected hosts. However, not all infected hosts may be found by this method because a blacklist does not cover all black domain names. In this paper, we present a method for finding unknown black domain names and extend the blacklist by using DNS traffic data and the original blacklist of known black domain names. We use co-occurrence relation of two different domain names to find unknown black domain names and extend a blacklist. If a domain name co-occurs with a known black name frequently, we assume that the domain name is also black. We evaluate the proposed method by cross validation, about 91 % of domain names that are in the validation list can be found as top 1 %.

We present two major information examination strategies for diagnosing the reasons for system disappointments and for identifying system disappointments early. Syslogs contain log information created by the framework. We dissected syslogs what's more, prevailing with regards to distinguishing the reason for a system disappointment via consequently learning more than 100 million logs without requiring any past learning of log information. Investigation of the information of an interpersonal interaction benefit (in particular, Twitter) empowered us to recognize conceivable system disappointments by extricating system disappointment related tweets, which represent under 1% of all tweets, continuously and with high exactness.