挑染 发表于 2025-3-21 18:53:55
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An HMM-Based Anomaly Detection Approach for SCADA Systemsvailable by other researchers. These datasets refer to real industrial networks and contain a variety of identified attack vectors. We benchmark our findings against a large set of machine learning algorithms and demonstrate that our proposal exhibits superior performance characteristics.PUT 发表于 2025-3-22 01:46:44
Analysis of a Code-Based Countermeasure Against Side-Channel and Fault Attacksto retrieve the sensitive data from the observed side-channel leakage. Going further, we show that this bias is inherent to the nature of the encoding and that randomizing the code itself can be useful to reduce the bias but cannot completely fix the scheme.敌手 发表于 2025-3-22 05:15:42
http://reply.papertrans.cn/47/4654/465339/465339_4.pngFISC 发表于 2025-3-22 11:58:13
http://reply.papertrans.cn/47/4654/465339/465339_5.png不能和解 发表于 2025-3-22 13:14:13
Conference proceedings 2016P 2016, held in Heraklion, Crete, Greece, in September 2016. The 13 revised full papers and 5 short papers presented together in this book were carefully reviewed and selected from 29 submissions. ..WISTP 2016 sought original submissions from academia and industry presenting novel research on all th能得到 发表于 2025-3-22 20:11:53
http://reply.papertrans.cn/47/4654/465339/465339_7.png一瞥 发表于 2025-3-22 22:11:07
Privacy-Preserving Two-Party Skyline Queries Over Horizontally Partitioned Datastributedly owned by multiple parties. We adopt the classical honest-but-curious attack model, and design a suite of efficient protocols for skyline queries over horizontally partitioned data. We analyze in detail the efficiency of each of the proposed protocols as well as their privacy guarantees.ellagic-acid 发表于 2025-3-23 01:53:39
http://reply.papertrans.cn/47/4654/465339/465339_9.pngcalumniate 发表于 2025-3-23 07:08:46
Attacking and Defending Dynamic Analysis System-Calls Based IDSenough to provide a decision tree based classifier with a large training set to counter malware. One must also be aware of the possibility that the classifier would be fooled by a camouflage algorithm, and try to counter such an attempt with techniques such as input transformation or training set updates.