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Computational Complexity of Anomaly detection Methods

Computational Complexity of Anomaly Detection Methods

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7th International Conference on Broadband, Wireless Computing, Communication and Applications (BWCCA)

作者： Oshima, Shunsuke Nakashima, Takuo Kumamoto Natl Coll Technol ICT Ctr Learning Support 2627 Hirayama Shinmachi Yatsushiro Kumamoto Japan Tokai Univ Dept Elect Intelligent Syst Engn Kumamoto Japan

ISBN: (纸本)9780769548425

As the typical anomaly detection methods using statistics, entropy and chi(2) based method has been researched and reported with their performance properties for anomaly attacks. In this research, we compare the time complexity of two our proposed detection method aiming to evaluate the performance of our system. Our previous researches have clarified that the source IP address and destination port number are efficient statistical variables to view the anomaly packet property, which lead to detect correctly. In this paper, we propose EMMM method for entropy value and CSDM method of chi(2) value using multi statistical variables. The evaluation to verify the time complexity of our proposed methods were conducted using source IP address, destination port number and arriving interval of packets. We could extract the following results. Firstly, the total time complexity of the EMMM method is O(n) for the n total packets, and the time complexity of one window is O(W). Secondly, the time complexity of CSDM for one window is O(NW + N m log m).

关键词： Time Complexity dos/ddos detection anomaly detection Entropy chi-square value statistical approach

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Anomaly detection using Chi-Square Values based on the Typical Features and the Time Deviation

Anomaly Detection using Chi-Square Values based on the Typic...

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25th IEEE International Conference on Advanced Information Networking and Applications (AINA)

作者： Oshima, Shunsuke Nakashima, Takuo Sueyoshi, Toshinori Kumamoto Natl Coll Technol ICT Ctr Learning Support 2627 Hirayama Shinmachi Kumamoto Japan Tokai Univ Dep Elect & Intelligent Syst Eng Tokai Ibaraki Japan Kumamoto Univ Grad Sch Sci & Technol Kumamoto Japan

ISBN: (纸本)9780769543376

In the research of the anomaly detection system analyzing the packet header on the Internet, previous researches have proposed the anomaly detection system using chi-square values in terms of the source IP address and/or the destination port number. In these previous researches, the chi-square values were calculated from one feature causing the degradation in the False-Positive when the same symbol appears sequentially. Therefore, we propose the anomaly detection technique using chi-square values based on multi features. We also propose dynamic BIN division technique to deal with the traffic fluctuations such as day and night traffic differences. Applying our method, the chi-square values based on the time division were able to decrease the False-Positive. Our method was also able to adapt the traffic variations by applying the dynamic BIN division technique.

关键词： dos/ddos detection anomaly detection chi-square value statistical approach

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An anomaly detection system based on chi-square method with dynamic BIN algorithm

An anomaly detection system based on chi-square method with ...

引用

6th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA-2011

作者： Oshima, Shunsuke Ichimura, Yusuke Nakashima, Takuo Sueyoshi, Toshinori ICT Center for Learning Support Kumamoto National College of Technology 2627 Hirayama-Shinmachi Yatsushiro Kumamoto Japan Production Systems Engineering Course Kumamoto National College of Technology 2627 Hirayama-Shinmachi Yatsushiro Kumamoto Japan Dep. of Electronics and Intelligent Systems Engineering Tokai University 9-1-1 Toroku Kumamoto Japan Graduate School of Science and Technology Kumamoto University 2-39-1 Kurokami Kumamoto Japan

ISBN: (纸本)9780769545325

The statistic researches have been proposed to detect anomaly attacks using chi-square. In these researches, features such as the IP address and the port number are used as the probabilistic variables. The method based on multiple variables has not been proposed to aim to improve the accuracy of anomaly detection. If the number of packets increase, these packets are classified into BINs before the calculation of chi-square method. The classification method depends on the calculation parameters such as the window width and the number of BIN, and the packet distribution of night and day time. In addition, the classification method should be changed based on these parameters. In this paper, we propose the dynamic BIN method to classify the incoming packets automatically. We also propose the CSDM (Chi-square-based Space Division Method) to detect anomaly attacks using the dynamic BIN methods with multiple probabilistic variables. As the results of experiments using the source IP address, the destination port number, and the interval time deviation of arriving packets as the probabilistic variables, the proposed dynamic BIN realized the equal classification, which does not depends on the features of packets and the number of BIN. In addition, the dynamic BIN mechanism and CSDM method using two probabilistic variables could improve F-measure compared to the conventional method. © 2011 IEEE.

关键词： BIN CSDM method Chi-square-based space division method Computer crime dos/ddos detection Equations F-measure Feature extraction Heuristic algorithms IP networks IP networks Mathematical model Probabilistic logic anomaly detection system chi-square value d

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The evaluation of an anomaly detection system based on Chi-square method

The evaluation of an anomaly detection system based on Chi-s...

引用

26th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2012

作者： Oshima, Shunsuke Nakashima, Takuo Sueyoshi, Toshinori ICT Center for Learning Support Kumamoto National College of Technology 2627 Hirayama-Shinmachi Yatsushiro Kumamoto Japan Dep. of Electronics and Intelligent Systems Engineering Tokai University 9-1-1 Toroku Kumamoto Japan Graduate School of Science and Technology Kumamoto University 2-39-1 Kurokami Kumamoto Japan

ISBN: (纸本)9780769546520

The conventional methods using χ2 value have been proposed to detect anomaly attacks. These systems, however, merely treat the one feature such as the source IP address or the destination port number as the probabilistic variable. The method based on multiple variables has not been proposed to aim to improve the accuracy of anomaly detection. In this paper, we propose the multiple features χ2 method named the CSDM (Chi-square-based Space Division Method) to improve the detection accuracy. The F-measure values of CSDM and the conventional method are compared to evaluate these systems. We also focus on the learning mechanism and it's affection for both systems. As the results of experiments using the source IP address, the destination port number, and the interval time deviation of arriving packets as the probabilistic variables, the proposed CSDM improves the F-measure compared to the conventional method meaning that the CSDM using multiple features can improve the F-measure over dos/ddos attacks and double attacks with 30% attacking rate. In addition, the learning time of the 2 days in the CSDM system is enough to learn the behavior of normal condition and can reveal the quick learning@performance with the high F-measures. © 2012 IEEE.

关键词： chi-square value dos/ddos detection learning time statistical approach

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Classifying DNS Servers based on Response Message Matrix using Machine Learning 6

Classifying DNS Servers based on Response Message Matrix usi...

引用

6th Annual Conference on Computational Science and Computational Intelligence (CSCI)

作者： Shima, Keiichi Nakamura, Ryo Okada, Kazuya Ishihara, Tomohiro Miyamoto, Daisuke Sekiya, Yuji IIJ Innovat Inst Inc Tokyo Japan Univ Tokyo Tokyo Japan

ISBN: (纸本)9781728155845

Improperly configured Domain Name System (DNS) servers are sometimes used as packet reflectors as part of a dos or ddos attack. Detecting packets created as a result of this activity is logically possible by monitoring the DNS request and response traffic. Any response that does not have a corresponding request can be considered a reflected message;checking and tracking every DNS packet, however, is a non-trivial operation. In this paper, we propose a detection mechanism for DNS servers used as reflectors by using a DNS server feature matrix built from a small number of packets and a machine learning algorithm. The F1 score of bad DNS server detection was over 0.9 when the test and training data are generated within the same day.

关键词： DNS reflection dos/ddos detection machine learning

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Detecting dos/ddos Attack under IPv4/IPv6 Translation Covert Channel

Detecting DoS/DDoS Attack under IPv4/IPv6 Translation Covert...

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2016 2nd International Conference on Humanity and Social Science（ICHSS 2016）

作者： Reen-cheng WANG Bo-xian CHEN Bo-jyun HUANG Shi-yi XIE Yu-tine CHO Taitung University

With the IPv6 becomes more and more popular, people start to notice its security problems. The problems not only happen in the protocol itself, but also exist in the deployment scenarios. In this paper, we present our research about detecting Do S/DDo S attack under IPv4/IPv6 translation covert channel. The NAT64/DNS64 is the translation scheme which focused. The two scenes test in this research show the attacks launch from IPv6 side still can affect the IPv4 host a lot and can hide the source of attackers owing to the covert channel effect. We walk around the classical methods and make our new rules for detecting the attacks. The results show promise for our rules which can detect SYN6 and Smurf6 attack from IPv4 host correctly.

关键词： IPv4/IPv6 covert channel NAT64 dos/ddos detection

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