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2020-01-20
Ingols, Kyle, Chu, Matthew, Lippmann, Richard, Webster, Seth, Boyer, Stephen.  2009.  Modeling Modern Network Attacks and Countermeasures Using Attack Graphs. 2009 Annual Computer Security Applications Conference. :117–126.
By accurately measuring risk for enterprise networks, attack graphs allow network defenders to understand the most critical threats and select the most effective countermeasures. This paper describes substantial enhancements to the NetSPA attack graph system required to model additional present-day threats (zero-day exploits and client-side attacks) and countermeasures (intrusion prevention systems, proxy firewalls, personal firewalls, and host-based vulnerability scans). Point-to-point reachability algorithms and structures were extensively redesigned to support "reverse" reachability computations and personal firewalls. Host-based vulnerability scans are imported and analyzed. Analysis of an operational network with 84 hosts demonstrates that client-side attacks pose a serious threat. Experiments on larger simulated networks demonstrated that NetSPA's previous excellent scaling is maintained. Less than two minutes are required to completely analyze a four-enclave simulated network with more than 40,000 hosts protected by personal firewalls.
2017-02-14
B. Gu, Y. Fang, P. Jia, L. Liu, L. Zhang, M. Wang.  2015.  "A New Static Detection Method of Malicious Document Based on Wavelet Package Analysis". 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). :333-336.

More and more advanced persistent threat attacks has happened since 2009. This kind of attacks usually use more than one zero-day exploit to achieve its goal. Most of the times, the target computer will execute malicious program after the user open an infected compound document. The original detection method becomes inefficient as the attackers using a zero-day exploit to structure these compound documents. Inspired by the detection method based on structural entropy, we apply wavelet analysis to malicious document detection system. In our research, we use wavelet analysis to extract features from the raw data. These features will be used todetect whether the compound document was embed malicious code.