Conference Paper

Hunting Trojan Horses.

DOI: 10.1145/1181309.1181312 Conference: Proceedings of the 1st Workshop on Architectural and System Support for Improving Software Dependability, ASID 2006, San Jose, California, USA, October 21, 2006
Source: DBLP

ABSTRACT Abstract In this report we present HTH (Hunting Trojan Horses), a security framework for detecting Trojan Horses and Backdoors. The framework,is composed,of two main,parts: 1) Harrier – an application,security monitor,that performs,run-time monitoring to dynamically,collect execution-related data, and 2) Secpert – a security-specific Expert System based on CLIPS, which analyzes the events collected by Harrier. Our main,contributions,to the security research are three-fold. First we identify common malicious behaviors, patterns, and characteristics of Trojan Horses and Backdoors. Second we develop a security policy that can identify such malicious behavior and open the door for effectively using expert systems to implement complex security policies. Third, we construct a prototype,that successfully detects Trojan Horses and Backdoors. 1,Introduction Computer,attacks grew,at an alarming,rate in 2004 [26] and this rate is expected,to rise.

0 Bookmarks
 · 
63 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a proof of concept of a neural network Trojan. The neural network Trojan consists of a neural network that has been trained with a compromised dataset and modified code. The Trojan implementation is carried out by insertion of a malicious payload encoded into the weights alongside with the data of the intended application. The neural Trojan is specifically designed so that when a specific entry is fed into the trained neural network, it triggers the interpretation of the data as payload. The paper presents a background on which this attack is based and provides the assumptions that make the attack possible. Two embodiments of the attack are presented consisting of a basic backpropagation network and a Neural Network Trojan with Sequence Processing Connections (NNTSPC). The two alternatives are used depending on the underlying circumstances on which the compromise is launched. Experimental results are carried out with synthetic as well as a chosen existing binary payload. Practical issues of the attack are also discussed, as well as a discussion on detection techniques.
    Journal of Computer Security. 01/2013; 21.
  • [Show abstract] [Hide abstract]
    ABSTRACT: The Research of detection malware using machine learning method attracts much attention recent years. However, most of research focused on code analysis which is signature-based or analysis of system call sequence in Linux environment. Obviously, all methods have their strengths and weaknesses. In this paper, we concentrate on detection Trojan horse by operation system information in Windows environment using data mining technology. Our main content and contribution contains as follows: First, we collect Trojan horse samples in true network environment and classify them by scanner. Secondly, we collect operation system behavior features under infected and clean circumstances separately by WMI manager tools. And then, several classic classification algorithms are applied and a performance comparison is given. Feature selection methods are applied to those features and we get a feature order list which reflects the relevance order of Trojan horse activities and the system feature. We believe the instructive meaning of the list is significant. Finally, a feature combination method is applied and features belongs different groups are combined according their characteristic for high classification performance. Results of experiments demonstrate the feasibility of our assumption that detecting Trojan horses by system behavior information is feasible and affective.
    Machine Learning and Cybernetics (ICMLC), 2010 International Conference on; 08/2010
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we present the design, implementation, and evaluation of LeakProber, a framework that leverages the whole system dynamic instrumentation and the inter-procedural analysis to enable data propagation path profiling in production system. We integrate both the static analysis and runtime tracking to establish a holistic and practical approach to generating the sensitive data propagation graph (sDPG) with minimum runtime overhead. We evaluate our system on several data stealing attacks scenario for generating sDPG. The sDPG generated by our system captures multiple aspects of data accessing patterns and provides clear insights into the data leakage path. We also measure the performance of our system and find that it degrades the production system about 6% in the trace-on mode. When our prototype works in the trace-off mode, the runtime overhead is even lower, on an average of 1.5% across each benchmark we run. We believe that it is feasible to directly apply our prototype into production system environment.
    First ACM Conference on Data and Application Security and Privacy, CODASPY 2011, San Antonio, TX, USA, February 21-23, 2011, Proceedings; 01/2011

Full-text (5 Sources)

View
16 Downloads
Available from
May 20, 2014