KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Access type: UnknownAuthor: search.filters.author.Ohrui, Masayuki

Search Results

Now showing 1 - 3 of 3
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Apriori-PrefixSpan hybrid approach for automated detection of botnet coordinated attacks
    (2011-11-09)
    Ohrui, Masayuki
    ;
    Kikuchi, Hiroaki
    ;
    Terada, Masato
    ;
    Rosyid, Nur Rohman
    This paper aims to detect features of coordinated attacks by applying data mining techniques, Apriori and PrefixSpan, to the CCC DATAset 2008-2010 which consists of the captured packets data and the downloading logs. Data mining algorithms allow us to automate detecting characteristics from large amount of data, which the conventional heuristics could not apply. Apriori achives high recall but with false positive, while PrefixSpan has high precision but low recall. Hence, we propose hybriding these algorithms. Our analysis shows the change in behavior of malware over the past 3 years. © 2011 IEEE.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Analysis on the sequential behavior of malware attacks
    (2011-01-01)
    Rosyid, Nur Rohman
    ;
    Ohrui, Masayuki
    ;
    Kikuchi, Hiroaki
    ;
    Sooraksa, Pitikhate
    ;
    Terada, Masato
    Overcoming the highly organized and coordinated malware threats by botnets on the Internet is becoming increasingly difficult. A honeypot is a powerful tool for observing and catching malware and virulent activity in Internet traffic. Because botnets use systematic attack methods, the sequences of malware downloaded by honeypots have particular forms of coordinated pattern. This paper aims to discover new frequent sequential attack patterns in malware automatically. One problem is the difficulty in identifying particular patterns from full yearlong logs because the dataset is too large for individual investigations. This paper proposes the use of a data-mining algorithm to overcome this problem. We implement the PrefixSpan algorithm to analyze malware-attack logs and then show some experimental results. Analysis of these results indicates that botnet attacks can be characterized either by the download times or by the source addresses of the bots. Finally, we use entropy analysis to reveal how frequent sequential patterns are involved in coordinated attacks. Copyright © 2011 The Institute of Electronics, Information and Communication Engineers.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A discovery of sequential attack patterns of malware in botnets
    (2010-12-01)
    Rosyid, Nur Rohman
    ;
    Ohrui, Masayuki
    ;
    Kikuchi, Hiroaki
    ;
    Sooraksa, Pitikhate
    ;
    Terada, Masato
    More than 90 independent honeypots have observed malware traffic at the Japanese tier-1 backbone. Typical attacks were made by multiple servers, coordinating to send many kinds of malware. T his paper aims to discover some frequent new sequential attack patterns of malware. It is not easy to identify particular patterns logs of one year because the volume of dataset is too large to investigate one by one. To overcome the problem, this paper proposes data mining algorithm, the PrejixSpan method. We implement the PrejixSpan algorithm to analyze the malware footprints and show the experimental result. The result of analysis shows that the attacks are performed by multiple sequential attack patterns within a short amount of time. ©2010 IEEE.