Detection of Intrusions and Malware, and Vulnerability by Roland Buschkes, Pavel Laskov

By Roland Buschkes, Pavel Laskov

This e-book constitutes the refereed complaints of the 3rd foreign convention on Detection of Intrusions and Malware, and Vulnerability review, DIMVA 2006, held in Berlin, Germany in July 2006. The eleven revised complete papers offered have been rigorously reviewed and chosen from forty-one submissions. The papers are equipped in topical sections on code research, intrusion detection, risk safety and reaction, malware and forensics, and deployment situations.

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Additional resources for Detection of Intrusions and Malware, and Vulnerability Assessment: Third International Conference, DIMVA 2006, Berlin, Germany, July 13-14, 2006, Proceedings ... Computer Science Security and Cryptology)

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User3’s feature file Then, we classify the unknown [label] data by using the SVM model. While classifying new command sequences, we refine the detection method by updating the SVM training model with the new sequences, which is discussed later. 3 Experimental Studies In this section, we describe the experiments where we compared our method with the previous researches and also considered the feasibility of online update of our method. 1 45 The UNIX Commands Dataset In the experiments, we used the dataset provided by Schonlau et al.

10. User3’s feature file Then, we classify the unknown [label] data by using the SVM model. While classifying new command sequences, we refine the detection method by updating the SVM training model with the new sequences, which is discussed later. 3 Experimental Studies In this section, we describe the experiments where we compared our method with the previous researches and also considered the feasibility of online update of our method. 1 45 The UNIX Commands Dataset In the experiments, we used the dataset provided by Schonlau et al.

Our experimental results show that our method will be able to work well in real situations. An SVM-Based Masquerade Detection Method 39 The rest of this paper is organized as follows. Section 2 proposes a new masquerade detection method using co-occurrence matrix based on SVM. Experimental studies using the dataset are shown in Section 3, and Section 4 concludes this paper. 2 Masquerade Detection Using Co-occurrence Matrix Based on SVM In this section, we describe how we model users’ behaviors in our method, which is derived from ECM [4].

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