In the last years there has been an increase in the amount of malware using HTTPS traffic for their communications. This situation pose a challenge for the security analysts because the traffic is encrypted and because it mostly looks like normal traffic. Therefore, there is a need to discover new features and methods to detect malware without decrypting the traffic. A detection method that does not need to unencrypt the traffic is cheaper (because no traffic interceptor is needed), faster and private, respecting the original idea of HTTPS. The goal of this thesis is to detect HTTPS malware connections by extracting new features and using data from the Bro IDS program. Since the data for the research is hard to come by, we used data from the Stratosphere project and we created, by hand, our own datasets. Our unit of analysis is an aggregation of all the information that is possible to obtain without decrypting the data. We group together flows, SSL data and X.509 certificates data as they are generated by Bro. To classify the HTTPS malware traffic we used several algorithms, such as Neural Networks, XGBoost and Random Forest. Our results show that the HTTPS malware behaviour is distinct from normal HTTPS behaviour and that our methods are able to separate them with an accuracy of at least 96.64%.
Identifying, Modeling and Detecting Botnet Behaviors in the Network
Garcia, S. (2014). Identifying, Modeling and Detecting Botnet Behaviors in the Network. UNICENUniversity. PhD Thesis. doi:(10.13140/2.1.3488.8006)
Abstract
Botnets are the technological backbone supporting myriad of attacks, including identity stealing, organizational spying, DoS, SPAM, government-sponsored attacks and spying of political dissidents among others. The research community works hard creating detection algorithms of botnet network traffic. These algorithms have been partially successful, but are difficult to reproduce and verify; being often commercialized. However, the advances in machine learning algorithms and the access to better botnet datasets start showing promising results. The shift of the detection techniques to behavioral-based models has proved to be a better approach to the analysis of botnet patterns. However, the current knowledge of the botnet actions and patterns does not seem to be deep enough to create adequate traffic models that could be used to detect botnets in real networks. This thesis proposes three new botnet detection methods and a new model of botnet behavior that are based in a deep understanding of the botnet behaviors in the network. First the SimDetect method, that analyzes the structural similarities of clustered botnet traffic. Second the BClus method, that clusters traffic according to its connection patterns and uses decision rules to detect unknown botnet in the network. Third, the CCDetector method, that uses a novel state-based behavioral model of known Command and Control channels to train a Markov Chain and to detect similar traffic in unknown real networks. The BClus and CCDetector methods were compared with third-party detection methods, showing their use in real environments. The core of the CCDetector method is our state-based behavioral model of botnet actions. This model is capable of representing the changes in the behaviors over time. To support the research we use a huge dataset of botnet traffic that was captured in our Malware Capture Facility Project. The dataset is varied, large, public, real and has Background, Normal and Botnet labels. The tools, dataset and algorithms were released as free software. Our algorithms give a new high-level interface to identify, visualize and block botnet behaviors in the networks.
