Biblio

Device management in large networks is of growing importance to network administrators and security analysts alike. The composition of devices on a network can help forecast future traffic demand as well as identify devices that may pose a security risk. However, the sheer number and diversity of devices that comprise most modern networks have vastly increased the management complexity. Motivated by a need for an encryption-invariant device management strategy, we use affiliation graphs to develop a methodology that reveals key insights into the devices acting on a network using only the source and destination IP addresses. Through an empirical analysis of the devices on a university campus network, we provide an example methodology to infer a device's characteristics (e.g., operating system) through the services it communicates with via the Internet.

Similarity digests have gained popularity for many security applications like blacklisting/whitelisting, and finding similar variants of malware. TLSH has been shown to be particularly good at hunting similar malware, and is resistant to evasion as compared to other similarity digests like ssdeep and sdhash. Searching and clustering are fundamental tools which help the security analysts and security operations center (SOC) operators in hunting and analyzing malware. Current approaches which aim to cluster malware are not scalable enough to keep up with the vast amount of malware and goodware available in the wild. In this paper, we present techniques which allow for fast search and clustering of TLSH hash digests which can aid analysts to inspect large amounts of malware/goodware. Our approach builds on fast nearest neighbor search techniques to build a tree-based index which performs fast search based on TLSH hash digests. The tree-based index is used in our threshold based Hierarchical Agglomerative Clustering (HAC-T) algorithm which is able to cluster digests in a scalable manner. Our clustering technique can cluster digests in O (n logn) time on average. We performed an empirical evaluation by comparing our approach with many standard and recent clustering techniques. We demonstrate that our approach is much more scalable and still is able to produce good cluster quality. We measured cluster quality using purity on 10 million samples obtained from VirusTotal. We obtained a high purity score in the range from 0.97 to 0.98 using labels from five major anti-virus vendors (Kaspersky, Microsoft, Symantec, Sophos, and McAfee) which demonstrates the effectiveness of the proposed method.

Rapidly growing shared information for threat intelligence not only helps security analysts reduce time on tracking attacks, but also bring possibilities to research on adversaries' thinking and decisions, which is important for the further analysis of attackers' habits and preferences. In this paper, we analyze current models and frameworks used in threat intelligence that suited to different modeling goals, and propose a three-layer model (Goal, Behavior, Capability) to study the statistical characteristics of APT groups. Based on the proposed model, we construct a knowledge network composed of adversary behaviors, and introduce a similarity measure approach to capture similarity degree by considering different semantic links between groups. After calculating similarity degrees, we take advantage of Girvan-Newman algorithm to discover community groups, clustering result shows that community structures and boundaries do exist by analyzing the behavior of APT groups.

Intrusion Detection Systems (IDS) generate a high volume of alerts that security analysts do not have the resources to explore fully. Modelling attacks, especially the coordinated campaigns of Advanced Persistent Threats (APTs), in a visually-interpretable way is a useful approach for network security. Graph models combine multiple alerts and are well suited for visualization and interpretation, increasing security effectiveness. In this paper, we use feature embeddings, learned from network event logs, and community detection to construct and segment alert graphs of related alerts and networks hosts. We posit that such graphs can aid security analysts in investigating alerts and may capture multiple aspects of an APT attack. The eventual goal of this approach is to construct interpretable attack graphs and extract causality information to identify coordinated attacks.

As AI systems become more ubiquitous, securing them becomes an emerging challenge. Over the years, with the surge in online social media use and the data available for analysis, AI systems have been built to extract, represent and use this information. The credibility of this information extracted from open sources, however, can often be questionable. Malicious or incorrect information can cause a loss of money, reputation, and resources; and in certain situations, pose a threat to human life. In this paper, we use an ensembled semi-supervised approach to determine the credibility of Reddit posts by estimating their reputation score to ensure the validity of information ingested by AI systems. We demonstrate our approach in the cybersecurity domain, where security analysts utilize these systems to determine possible threats by analyzing the data scattered on social media websites, forums, blogs, etc.

Security analysts implement various security mechanisms to protect systems from attackers. Even though these mechanisms try to secure systems, a talented attacker may use these same techniques to launch a sophisticated attack. This paper discuss about such an attack called as user account Denial of Service (DoS) where an attacker uses user account lockout features of the application to lockout all user accounts causing an enterprise wide DoS. The attack has being simulated usingastealthy attack mechanism called as Advanced Persistent Threats (APT) using a XMPP based botnet. Through the simulation, researchers discuss about the patterns associated with the attack which can be used to detect the attack in real time and how the attack can be prevented from the perspective of developers, system engineers and security analysts.