Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data

Recent years have seen the rise of more sophisticated attacks including advanced persistent threats (APTs) which pose severe risks to organizations and governments by targeting confidential proprietary information. Additionally, new malware strains are appearing at a higher rate than ever before. Since many of these malware are designed to evade existing security products, traditional defenses deployed by most enterprises today, e.g., anti-virus, firewalls, intrusion detection systems, often fail at detecting infections at an early stage. We address the problem of detecting early-stage infection in an enterprise setting by proposing a new framework based on belief propagation inspired from graph theory. Belief propagation can be used either with "seeds" of compromised hosts or malicious domains (provided by the enterprise security operation center -- SOC) or without any seeds. In the latter case we develop a detector of C&C communication particularly tailored to enterprises which can detect a stealthy compromise of only a single host communicating with the C&C server. We demonstrate that our techniques perform well on detecting enterprise infections. We achieve high accuracy with low false detection and false negative rates on two months of anonymized DNS logs released by Los Alamos National Lab (LANL), which include APT infection attacks simulated by LANL domain experts. We also apply our algorithms to 38TB of real-world web proxy logs collected at the border of a large enterprise. Through careful manual investigation in collaboration with the enterprise SOC, we show that our techniques identified hundreds of malicious domains overlooked by state-of-the-art security products

Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences

In this survey, we first briefly review the current state of cyber attacks, highlighting significant recent changes in how and why such attacks are performed. We then investigate the mechanics of malware command and control (C2) establishment: we provide a comprehensive review of the techniques used by attackers to set up such a channel and to hide its presence from the attacked parties and the security tools they use. We then switch to the defensive side of the problem, and review approaches that have been proposed for the detection and disruption of C2 channels. We also map such techniques to widely-adopted security controls, emphasizing gaps or limitations (and success stories) in current best practices.Comment: Work commissioned by CPNI, available at c2report.org. 38 pages. Listing abstract compressed from version appearing in repor

Comparing the Effectiveness of Different Classification Techniques in Predicting DNS Tunnels

DNS is one of the most widely used protocols on the internet and is used in the translation of domain names into IP address in order to correctly route messages between computers. It presents an attractive attack vector for criminals as the service is not as closely monitored by security experts as other protocols such as HTTP or FTP. Its use as a covert means of communication has increased with the availability of tools that allow for the creation of DNS tunnels using the protocol. One of the primary motivations for using DNS tunnels is the illegal extraction of information from a company’s network. This can lead to reputational damage for the organisation and result in significant fines – particularly with the introduction of General Data Protection Regulations in the EU. Most of the research into the detection of DNS tunnels has used anomalies in the relationship between DNS requests and other protocols, or anomalies in the rate of DNS requests made over specific time periods. This study will look at the characteristics of an individual DNS requests to see how effective different classification techniques are at identifying tunnels. The different techniques selected are Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). The effectiveness of the different techniques will be measured and compared to see if there are statistically significant differences between them using a Cochran’s Q test. The results will indicate that DT, RF and SVM, are the most effective techniques at categorising DNS requests, and that they are significantly different to the other models. Key Words: DNS Tunnel, Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Cochran’s Q Test