By Raoul Strackx, Yves Younan, Pieter Philippaerts, Frank Piessens (auth.), Pierangela Samarati, Michael Tunstall, Joachim Posegga, Konstantinos Markantonakis, Damien Sauveron (eds.)
This quantity constitutes the refereed complaints of the 4th IFIP WG 11.2 overseas Workshop on info protection conception and Practices: defense and privateness of Pervasive structures and shrewdpermanent units, WISTP 2010, held in Passau, Germany, in April 2010. The 20 revised complete papers and 10 brief papers have been conscientiously reviewed and chosen from sixty nine submissions. they're prepared in topical sections on embedded defense, protocols, hugely limited embedded structures, safety, clever card safety, algorithms, implementations, embedded structures and anonymity/database defense.
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Extra info for Information Security Theory and Practices. Security and Privacy of Pervasive Systems and Smart Devices: 4th IFIP WG 11.2 International Workshop, WISTP 2010, Passau, Germany, April 12-14, 2010. Proceedings
The two-sample KS test can also be compared to the non-parametric Mann-Whitney test that is the non-parametric equivalent to the T-test used in DPA. The Mann-Whitney test measures the diﬀerence in central tendency between two distributions whereas the KS test seems sensitive to any kind of distributional diﬀerence. We ﬁrst brieﬂy introduce the principle of the two-sample KS test. Let two samples Xi and Yj with size n and m. The samples can be characterized by their empirical cumulative density functions: cdfX = #i : Xi ≤ x n and cdfY = #j : Yj ≤ x , m Eﬃcient Entropy Estimation for Mutual Information Analysis 27 that correspond to the proportion of observed values inferior or equal to x.
Chaotic responses like these virtually rule out anomaly detection. The algorithm, we proposed in the previous section, on the other hand, produces a clear peak at the point of activity of the malware. Figure 4(e) even shows that this effect becomes more pronounced the more training examples there are. While stochastic diffusion over a sample of 50 prototypes yields peaks of almost the same magnitude for normal and abnormal behavior alike, a growing number of training samples considerably widens the gap between peaks at points of normal and abnormal behavior (note the changing scale of the y-axis of the plots).
Obviously, none of the common malware detection techniques (a) – (d) which we considered for baseline comparison was able to detect its activity. Regardless of the number of prototypes used for training, they fail to produce a coherent interpretation as to whether the mobile device’s activity patterns are normal or not. Chaotic responses like these virtually rule out anomaly detection. The algorithm, we proposed in the previous section, on the other hand, produces a clear peak at the point of activity of the malware.