Anomaly Detection in Log Data using Graph Databases and Machine Learning to Defend Advanced Persistent Threats

Advanced Persistent Threats (APTs) are a main impendence in cyber security of computer networks. In 2015, a successful breach remains undetected 146 days on average, reported by [Fi16].With our work we demonstrate a feasible and fast way to analyse real world log data to detect breaches or breach attempts. By adapting well-known kill chain mechanisms and a combine of a time series database and an abstracted graph approach, it is possible to create flexible attack profiles. Using this approach, it can be demonstrated that the graph analysis successfully detects simulated attacks by analysing the log data of a simulated computer network. Considering another source for log data, the framework is capable to deliver sufficient performance for analysing real-world data in short time. By using the computing power of the graph database it is possible to identify the attacker and furthermore it is feasible to detect other affected system components. We believe to significantly reduce the detection time of breaches with this approach and react fast to new attack vectors.Comment: Lecture Notes in Informatics (LNI), Gesellschaft f\"ur Informatik, Bonn 2017 237

A cyber-kill-chain based taxonomy of crypto-ransomware features

In spite of being just a few years old, ransomware is quickly becoming a serious threat to our digital infrastructures, data and services. Majority of ransomware families are requesting for a ransom payment to restore a custodian access or decrypt data which were encrypted by the ransomware earlier. Although the ransomware attack strategy seems to be simple, security specialists ranked ransomware as a sophisticated attack vector with many variations and families. Wide range of features which are available in different families and versions of ransomware further complicates their detection and analysis. Though the existing body of research provides significant discussions about ransomware details and capabilities, the all research body is fragmented. Therefore, a ransomware feature taxonomy would advance cyber defenders’ understanding of associated risks of ransomware. In this paper we provide, to the best of our knowledge, the first scientific taxonomy of ransomware features, aligned with Lockheed Martin Cyber Kill Chain (CKC) model. CKC is a well-established model in industry that describes stages of cyber intrusion attempts. To ease the challenge of applying our taxonomy in real world, we also provide the corresponding ransomware defence taxonomy aligned with Courses of Action matrix (an intelligence-driven defence model). We believe that this research study is of high value for the cyber security research community, as it provides the researchers with a means of assessing the vulnerabilities and attack vectors towards the intended victims

Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks

Intrusion detection has become one of the most critical tasks in a wireless network to prevent service outages that can take long to fix. The sheer variety of anomalous events necessitates adopting cognitive anomaly detection methods instead of the traditional signature-based detection techniques. This paper proposes an anomaly detection methodology for wireless systems that is based on monitoring and analyzing radio frequency (RF) spectrum activities. Our detection technique leverages an existing solution for the video prediction problem, and uses it on image sequences generated from monitoring the wireless spectrum. The deep predictive coding network is trained with images corresponding to the normal behavior of the system, and whenever there is an anomaly, its detection is triggered by the deviation between the actual and predicted behavior. For our analysis, we use the images generated from the time-frequency spectrograms and spectral correlation functions of the received RF signal. We test our technique on a dataset which contains anomalies such as jamming, chirping of transmitters, spectrum hijacking, and node failure, and evaluate its performance using standard classifier metrics: detection ratio, and false alarm rate. Simulation results demonstrate that the proposed methodology effectively detects many unforeseen anomalous events in real time. We discuss the applications, which encompass industrial IoT, autonomous vehicle control and mission-critical communications services.Comment: 7 pages, 7 figures, Communications Workshop ICC'1

Tracking advanced persistent threats in critical infrastructures through opinion dynamics

Advanced persistent threats pose a serious issue for modern industrial environments, due to their targeted and complex attack vectors that are difficult to detect. This is especially severe in critical infrastructures that are accelerating the integration of IT technologies. It is then essential to further develop effective monitoring and response systems that ensure the continuity of business to face the arising set of cyber-security threats. In this paper, we study the practical applicability of a novel technique based on opinion dynamics, that permits to trace the attack throughout all its stages along the network by correlating different anomalies measured over time, thereby taking the persistence of threats and the criticality of resources into consideration. The resulting information is of essential importance to monitor the overall health of the control system and cor- respondingly deploy accurate response procedures. Advanced Persistent Threat Detection Traceability Opinion Dynamics.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Identification of Attack Paths Using Kill Chain and Attack Graphs

The ever-evolving capabilities of cyber attackers force security administrators to focus on the early identification of emerging threats. Targeted cyber attacks usually consist of several phases, from initial reconnaissance of the network environment to final impact on objectives. This paper investigates the identification of multi-step cyber threat scenarios using kill chain and attack graphs. Kill chain and attack graphs are threat modeling concepts that enable determining weak security defense points. We propose a novel kill chain attack graph that merges kill chain and attack graphs together. This approach determines possible chains of attacker’s actions and their materialization within the protected network. The graph generation uses a categorization of threats according to violated security properties. The graph allows determining the kill chain phase the administrator should focus on and applicable countermeasures to mitigate possible cyber threats. We implemented the proposed approach for a predefined range of cyber threats, especially vulnerability exploitation and network threats. The approach was validated on a real-world use case. Publicly available implementation contains a proof-of-concept kill chain attack graph generator

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

The Intersection of Law and Ethics in Cyberwar: Some Reflections

The purpose of this short essay is to reflect upon a few issues that illustrate how legal and ethical issues intersect in the cyber realm. Such an intersection should not be especially surprising., Historian Geoffrey Best insists, “[I]t must never be forgotten that the law of war, wherever it began at all, began mainly as a matter of religion and ethics . . . “It began in ethics” Best says “and it has kept one foot in ethics ever since.” Understanding that relationship is vital to appreciating the full scope of the responsibilities of a cyber-warrior in the 21st century