Evaluating payload features for malware infection detection
スポンサーリンク
概要
- 論文の詳細を見る
Analysis of malware-infected traffic data revealed the payload features that are the most effective for detecting infection. The traffic data was attack traffic using the D3M2012 dataset and CCC DATAsets 2009, 2010, and 2011. Traffic flowing on an intranet at two different sites was used as normal traffic data. Since the type of malware (worm, Internet connection confirmation, etc.) affects the type of traffic generated, the malware was divided into three types — worm, Trojan horse, and file-infected virus — and the most effective features were identified for each type.
- 一般社団法人 情報処理学会の論文
一般社団法人 情報処理学会 | 論文
- Interest Point Detection Based on Stochastically Derived Stability
- Efficient Algorithms for Extracting Pareto-optimal Hardware Configurations in DEPS Framework
- Verification of Substitution Theorem Using HOL (プログラミング Vol.5 No.2)
- Programmable Architectures and Design Methods for Two-Variable Numeric Function Generators
- An Exact Estimation Algorithm of Error Propagation Probability for Sequential Circuits