Advanced Threat Intelligence: Interpretation of Anomalous Behavior in Ubiquitous Kernel Processes

Targeted attacks on digital infrastructures are a rising threat against the confidentiality, integrity, and availability of both IT systems and sensitive data. With the emergence of advanced persistent threats (APTs), identifying and understanding such attacks has become an increasingly difficult task. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. This thesis presents a multi-stage system able to detect and classify anomalous behavior within a user session by observing and analyzing ubiquitous kernel processes. Application candidates suitable for monitoring are initially selected through an adapted sentiment mining process using a score based on the log likelihood ratio (LLR). For transparent anomaly detection within a corpus of associated events, the author utilizes star structures, a bipartite representation designed to approximate the edit distance between graphs. Templates describing nominal behavior are generated automatically and are used for the computation of both an anomaly score and a report containing all deviating events. The extracted anomalies are classified using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Ultimately, the newly labeled patterns are mapped to a dedicated APT attacker–defender model that considers objectives, actions, actors, as well as assets, thereby bridging the gap between attack indicators and detailed threat semantics. This enables both risk assessment and decision support for mitigating targeted attacks. Results show that the prototype system is capable of identifying 99.8% of all star structure anomalies as benign or malicious. In multi-class scenarios that seek to associate each anomaly with a distinct attack pattern belonging to a particular APT stage we achieve a solid accuracy of 95.7%. Furthermore, we demonstrate that 88.3% of observed attacks could be identified by analyzing and classifying a single ubiquitous Windows process for a mere 10 seconds, thereby eliminating the necessity to monitor each and every (unknown) application running on a system. With its semantic take on threat detection and classification, the proposed system offers a formal as well as technical solution to an information security challenge of great significance.The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, and the National Foundation for Research, Technology and Development is gratefully acknowledged

Multi-aspect rule-based AI: Methods, taxonomy, challenges and directions towards automation, intelligence and transparent cybersecurity modeling for critical infrastructures

Critical infrastructure (CI) typically refers to the essential physical and virtual systems, assets, and services that are vital for the functioning and well-being of a society, economy, or nation. However, the rapid proliferation and dynamism of today\u27s cyber threats in digital environments may disrupt CI functionalities, which would have a debilitating impact on public safety, economic stability, and national security. This has led to much interest in effective cybersecurity solutions regarding automation and intelligent decision-making, where AI-based modeling is potentially significant. In this paper, we take into account “Rule-based AI” rather than other black-box solutions since model transparency, i.e., human interpretation, explainability, and trustworthiness in decision-making, is an essential factor, particularly in cybersecurity application areas. This article provides an in-depth study on multi-aspect rule based AI modeling considering human interpretable decisions as well as security automation and intelligence for CI. We also provide a taxonomy of rule generation methods by taking into account not only knowledge-driven approaches based on human expertise but also data-driven approaches, i.e., extracting insights or useful knowledge from data, and their hybridization. This understanding can help security analysts and professionals comprehend how systems work, identify potential threats and anomalies, and make better decisions in various real-world application areas. We also cover how these techniques can address diverse cybersecurity concerns such as threat detection, mitigation, prediction, diagnosis for root cause findings, and so on in different CI sectors, such as energy, defence, transport, health, water, agriculture, etc. We conclude this paper with a list of identified issues and opportunities for future research, as well as their potential solution directions for how researchers and professionals might tackle future generation cybersecurity modeling in this emerging area of study

Cyber indicators of compromise: a domain ontology for security information and event management

It has been said that cyber attackers are attacking at wire speed (very fast), while cyber defenders are defending at human speed (very slow). Researchers have been working to improve this asymmetry by automating a greater portion of what has traditionally been very labor-intensive work. This work is involved in both the monitoring of live system events (to detect attacks), and the review of historical system events (to investigate attacks). One technology that is helping to automate this work is Security Information and Event Management (SIEM). In short, SIEM technology works by aggregating log information, and then sifting through this information looking for event correlations that are highly indicative of attack activity. For example: Administrator successful local logon and (concurrently) Administrator successful remote logon. Such correlations are sometimes referred to as indicators of compromise (IOCs). Though IOCs for network-based data (i.e., packet headers and payload) are fairly mature (e.g., Snort's large rule-base), the field of end-device IOCs is still evolving and lacks any well-defined go-to standard accepted by all. This report addresses ontological issues pertaining to end-device IOCs development, including what they are, how they are defined, and what dominant early standards already exist.http://archive.org/details/cyberindicatorso1094553041Lieutenant, United States NavyApproved for public release; distribution is unlimited

Design of secure and robust cognitive system for malware detection

Machine learning based malware detection techniques rely on grayscale images of malware and tends to classify malware based on the distribution of textures in graycale images. Albeit the advancement and promising results shown by machine learning techniques, attackers can exploit the vulnerabilities by generating adversarial samples. Adversarial samples are generated by intelligently crafting and adding perturbations to the input samples. There exists majority of the software based adversarial attacks and defenses. To defend against the adversaries, the existing malware detection based on machine learning and grayscale images needs a preprocessing for the adversarial data. This can cause an additional overhead and can prolong the real-time malware detection. So, as an alternative to this, we explore RRAM (Resistive Random Access Memory) based defense against adversaries. Therefore, the aim of this thesis is to address the above mentioned critical system security issues. The above mentioned challenges are addressed by demonstrating proposed techniques to design a secure and robust cognitive system. First, a novel technique to detect stealthy malware is proposed. The technique uses malware binary images and then extract different features from the same and then employ different ML-classifiers on the dataset thus obtained. Results demonstrate that this technique is successful in differentiating classes of malware based on the features extracted. Secondly, I demonstrate the effects of adversarial attacks on a reconfigurable RRAM-neuromorphic architecture with different learning algorithms and device characteristics. I also propose an integrated solution for mitigating the effects of the adversarial attack using the reconfigurable RRAM architecture.Comment: arXiv admin note: substantial text overlap with arXiv:2104.0665