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34 册 图书
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Xerox :: Mesa :: 4.0 1978 :: Listing :: Mesa 4 Microcode :: *** Sep78

引用

2016年

Xerox :: Mesa :: 4.0 1978 :: Listing :: Mesa 4 Microcode :: *** Sep78 by published by

关键词： code code segment dest link destination destination link dispatch dispatch shake entry frame frame pointer gfi word global frame link listing mesad.mu microcode nova nova code pending branch pointer second word sep78 stash xerox xfer

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Complete Amiga C (1994-01)(Future Publishing)(Gb)[supplement, Issue 33]

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2016年

Complete Amiga C (1994-01)(Future Publishing)(Gb)[supplement, Issue 33] by published by

关键词： (1994-01)(future amiga code segment complete complete amiga dice edition edition basic expression floating point issue printf program publishing)(gb)[supplement, sample sample edition scanf switch statement user user enters variable variable reply variable types void maino

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Ibm :: System3 :: Plm :: Ly21-0517-6 Ibm System-3 Disk Sort Program Logic Manual Dec75

引用

2016年

Ibm :: System3 :: Plm :: Ly21-0517-6 Ibm System-3 Disk Sort Program Logic Manual Dec75 by published by

关键词： alternate collating code code segment collating sequence data management dec75 detailed description disk disk sort error file ibm licensed logic ly21-0517-6 manual model phase program program organization routine storage summary table work area work file

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Hp :: 3000 :: Seriesiii :: 30000-90020 Serii-Iiiref 78

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2016年

Hp :: 3000 :: Seriesiii :: 30000-90020 Serii-Iiiref 78 by published by

关键词： 30000-90020 code code segment data device device controller field field code function field instruction interrupt memory multiplexer channel program scratch pad selector channel seriesiii serii-iiiref skip field special field stack store field

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code Confusion Confrontation Method Based on Feature Comparison

Code Confusion Confrontation Method Based on Feature Compari...

引用

International Conference on Intelligent Computing, Automation and Systems (ICICAS)

作者： Jiesheng Zheng Zhongzheng Tong Guangcai Wu Ruigang Liang Guangdong Power Grid Co. Ltd. Guangzhou China Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728161068

ISBN: (纸本)9781728161075

With the maturity of code hardening technology, more and more malicious applications have adopted the reinforcement technology to avoid the detection of anti-virus engines, which brings great challenges for the identification and analysis of malicious code. The paper through an in-depth analysis of common detection avoidance techniques, the technical principle of reinforcement is studied, and a code confusion countermeasure method based on feature comparison is constructed. The method takes the unobfuscated function in the target application as a reference, extracts the target application characteristics by constructing the dependency graph of the program code, and then performs code similarity query in the software gene library system constructed in this paper, and analyzes based on the query result. The code segment is similar or identical to the obfuscated code, and finally restores the confusing variable or string name based on the variable mapping relationship before and after the confusion, thereby achieving effective recovery of the obfuscated code.

关键词： computer viruses invasive software program diagnostics query processing Invasive software analysis programme Computer viruses query analysis codes program code Confusion code segment

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Application of String Kernel based Support Vector Machine for Malware Packer Identification

Application of String Kernel based Support Vector Machine fo...

引用

International Joint Conference on Neural Networks

作者： Tao BAN Ryoichi ISAWA Shanqing GUO Daisuke INOUE Koji NAKAO National Institute of Information and Communications Technology Japan. Shandong University Jinan China.

ISBN: (纸本)9781467361279

Packing is among the most popular obfuscation techniques to impede anti-virus scanners from successfully detecting malware. In this paper we propose a string-kernel-based support vector machine classifier to identify the packer that is used to create a given malware program. Our approach is featured by the following characteristics. First, the adoption of a string-kernel-based method bridges the gap between signature-based and machine-learning-base approaches. Second, the kernel function derived from the Levenshtein distance integrates important domain knowledge in the learning process. Then, application of support vector machine, a state-of-the-art classifier, enables an automated packer identification scheme with high generalization ability and time efficiency. Finally, selection of the code segment with the most essential packerrelevant information further boosts the classification performance. Experiments on a dataset of 3228 binary programs composed of packed files created by 25 packers show that the proposed approach outperforms PEiD and previous machine-learning-based approaches in prediction accuracy with a large margin. This method can help to improve the scanning efficiency of anti-virus products and promote efficient back-end malware research.

关键词： Malware Packer Identification identify packer Packers Support Vector Network Spyware code segment Levenshtein distance time efficiency Antivirus domain knowledge

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