POIROT: Aligning Attack Behavior with Kernel Audit Records for Cyber Threat Hunting

Cyber threat intelligence (CTI) is being used to search for indicators of attacks that might have compromised an enterprise network for a long time without being discovered. To have a more effective analysis, CTI open standards have incorporated descriptive relationships showing how the indicators or observables are related to each other. However, these relationships are either completely overlooked in information gathering or not used for threat hunting. In this paper, we propose a system, called POIROT, which uses these correlations to uncover the steps of a successful attack campaign. We use kernel audits as a reliable source that covers all causal relations and information flows among system entities and model threat hunting as an inexact graph pattern matching problem. Our technical approach is based on a novel similarity metric which assesses an alignment between a query graph constructed out of CTI correlations and a provenance graph constructed out of kernel audit log records. We evaluate POIROT on publicly released real-world incident reports as well as reports of an adversarial engagement designed by DARPA, including ten distinct attack campaigns against different OS platforms such as Linux, FreeBSD, and Windows. Our evaluation results show that POIROT is capable of searching inside graphs containing millions of nodes and pinpoint the attacks in a few minutes, and the results serve to illustrate that CTI correlations could be used as robust and reliable artifacts for threat hunting.Comment: The final version of this paper is going to appear in the ACM SIGSAC Conference on Computer and Communications Security (CCS'19), November 11-15, 2019, London, United Kingdo

Network-based APT profiler

Constant innovation in attack methods presents a significant problem for the security community which struggles to remain current in attack prevention, detection and response. The practice of threat hunting provides a proactive approach to identify and mitigate attacks in real-time before the attackers complete their objective. In this research, I present a matrix of adversary techniques inspired by MITRE’s ATT&CK matrix. This study allows threat hunters to classify the actions of advanced persistent threats (APTs) according to network-based behaviors

Challenges and Open Questions of Machine Learning in Computer Security

This habilitation thesis presents advancements in machine learning for computer security, arising from problems in network intrusion detection and steganography. The thesis put an emphasis on explanation of traits shared by steganalysis, network intrusion detection, and other security domains, which makes these domains different from computer vision, speech recognition, and other fields where machine learning is typically studied. Then, the thesis presents methods developed to at least partially solve the identified problems with an overall goal to make machine learning based intrusion detection system viable. Most of them are general in the sense that they can be used outside intrusion detection and steganalysis on problems with similar constraints. A common feature of all methods is that they are generally simple, yet surprisingly effective. According to large-scale experiments they almost always improve the prior art, which is likely caused by being tailored to security problems and designed for large volumes of data. Specifically, the thesis addresses following problems: anomaly detection with low computational and memory complexity such that efficient processing of large data is possible; multiple-instance anomaly detection improving signal-to-noise ration by classifying larger group of samples; supervised classification of tree-structured data simplifying their encoding in neural networks; clustering of structured data; supervised training with the emphasis on the precision in top p% of returned data; and finally explanation of anomalies to help humans understand the nature of anomaly and speed-up their decision. Many algorithms and method presented in this thesis are deployed in the real intrusion detection system protecting millions of computers around the globe

Nástroj pro penetrační testování webových aplikací

Abstract As hackers become more skilled and sophisticated and with cyber-attacks becoming the norm, it is more important than ever before to undertake regular vulnerability scans and penetration testing to identify vulnerabilities and ensure on a regular basis that the cyber controls are working. In this thesis the importance and working of penetration testing and web application based penetration testing are discussed, followed by comparison and information’s about various testing tools and techniques and their advantages and disadvantages. The next section of the thesis mainly focuses on the past, current and future state of penetration testing in the computer systems and application security, importance of General Data Protection Regulation (GDPR) and Content Management system (CMS) followed by the main goal of the thesis which explains the existing solutions in automated tools for vulnerability detection of web application their techniques, positive and negative results of the conducted tests and their merits and demerits. In the next section, based on the comparison of various existing tools selecting appropriate algorithm for discussing the importance of scanning the ports which are usually focused in very few existing web application tools, the following section practically demonstrate the scanning of ports which gives information regarding, the state of ports to understand the service information running on the server. Finally the result of the experiment will be compared with the existing web application tools.Abstraktní Vzhledem k tomu, že se hackeři stávají zkušenějšími a sofistikovanějšími a kybernetické útoky se stávají normou, je důležitější než kdy jindy provádět pravidelné kontroly zranitelnosti a penetrační testování, aby bylo možné identifikovat zranitelná místa a pravidelně zajišťovat fungování kybernetických kontrol. V této práci je diskutován význam a fungování penetračního testování a penetračního testování založeného na webových aplikacích, následuje srovnání a informace o různých testovacích nástrojích a technikách a jejich výhodách a nevýhodách. Další část práce se zaměřuje především na minulý, současný a budoucí stav penetračního testování v počítačových systémech a zabezpečení aplikací, význam nařízení o obecné ochraně údajů (GDPR) a redakčního systému (CMS) následovaného hlavním cílem práce, která vysvětluje stávající řešení v automatizovaných nástrojích pro zjišťování zranitelnosti webové aplikace, jejich techniky, pozitivní a negativní výsledky provedených testů a jejich přednosti a nedostatky. V další části, založené na srovnání různých existujících nástrojů, které vybírají vhodný algoritmus pro diskusi o důležitosti skenování portů, které jsou obvykle zaměřeny na velmi málo stávajících webových aplikací, následující část prakticky demonstruje skenování portů, které poskytují informace týkající se, stav portů pro pochopení informací o službě běžících na serveru. Nakonec bude výsledek experimentu porovnán s existujícími nástroji webové aplikace.460 - Katedra informatikyvelmi dobř

Techniques for the reverse engineering of banking malware

Malware attacks are a significant and frequently reported problem, adversely affecting the productivity of organisations and governments worldwide. The well-documented consequences of malware attacks include financial loss, data loss, reputation damage, infrastructure damage, theft of intellectual property, compromise of commercial negotiations, and national security risks. Mitiga-tion activities involve a significant amount of manual analysis. Therefore, there is a need for automated techniques for malware analysis to identify malicious behaviours. Research into automated techniques for malware analysis covers a wide range of activities. This thesis consists of a series of studies: an anal-ysis of banking malware families and their common behaviours, an emulated command and control environment for dynamic malware analysis, a technique to identify similar malware functions, and a technique for the detection of ransomware. An analysis of the nature of banking malware, its major malware families, behaviours, variants, and inter-relationships are provided in this thesis. In doing this, this research takes a broad view of malware analysis, starting with the implementation of the malicious behaviours through to detailed analysis using machine learning. The broad approach taken in this thesis differs from some other studies that approach malware research in a more abstract sense. A disadvantage of approaching malware research without domain knowledge, is that important methodology questions may not be considered. Large datasets of historical malware samples are available for countermea-sures research. However, due to the age of these samples, the original malware infrastructure is no longer available, often restricting malware operations to initialisation functions only. To address this absence, an emulated command and control environment is provided. This emulated environment provides full control of the malware, enabling the capabilities of the original in-the-wild operation, while enabling feature extraction for research purposes. A major focus of this thesis has been the development of a machine learn-ing function similarity method with a novel feature encoding that increases feature strength. This research develops techniques to demonstrate that the machine learning model trained on similarity features from one program can find similar functions in another, unrelated program. This finding can lead to the development of generic similar function classifiers that can be packaged and distributed in reverse engineering tools such as IDA Pro and Ghidra. Further, this research examines the use of API call features for the identi-fication of ransomware and shows that a failure to consider malware analysis domain knowledge can lead to weaknesses in experimental design. In this case, we show that existing research has difficulty in discriminating between ransomware and benign cryptographic software. This thesis by publication, has developed techniques to advance the disci-pline of malware reverse engineering, in order to minimize harm due to cyber-attacks on critical infrastructure, government institutions, and industry.Doctor of Philosoph

Improving intrusion detection systems using data mining techniques

Recent surveys and studies have shown that cyber-attacks have caused a lot of damage to organisations, governments, and individuals around the world. Although developments are constantly occurring in the computer security field, cyber-attacks still cause damage as they are developed and evolved by hackers. This research looked at some industrial challenges in the intrusion detection area. The research identified two main challenges; the first one is that signature-based intrusion detection systems such as SNORT lack the capability of detecting attacks with new signatures without human intervention. The other challenge is related to multi-stage attack detection, it has been found that signature-based is not efficient in this area. The novelty in this research is presented through developing methodologies tackling the mentioned challenges. The first challenge was handled by developing a multi-layer classification methodology. The first layer is based on decision tree, while the second layer is a hybrid module that uses two data mining techniques; neural network, and fuzzy logic. The second layer will try to detect new attacks in case the first one fails to detect. This system detects attacks with new signatures, and then updates the SNORT signature holder automatically, without any human intervention. The obtained results have shown that a high detection rate has been obtained with attacks having new signatures. However, it has been found that the false positive rate needs to be lowered. The second challenge was approached by evaluating IP information using fuzzy logic. This approach looks at the identity of participants in the traffic, rather than the sequence and contents of the traffic. The results have shown that this approach can help in predicting attacks at very early stages in some scenarios. However, it has been found that combining this approach with a different approach that looks at the sequence and contents of the traffic, such as event- correlation, will achieve a better performance than each approach individually

Towards Understanding Man-on-the-Side Attacks (MotS) in SCADA Networks

We describe a new class of packet injection attacks called Man-on-the-Side Attacks (MotS), previously only seen where state actors have "compromised" a number of telecommunication companies. MotS injection attacks have not been widely investigated in scientific literature, despite having been discussed by news outlets and security blogs. MotS came to attention after the Edward Snowden revelations, which described large scale pervasive monitoring of the Internet's infrastructure. For an advanced adversary attempting to interfere with IT connected systems, the next logical step is to adapt this class of attack to a smaller scale, such as enterprise or critical infrastructure networks. MotS is a weaker form of attack compared to a Man-in-the-Middle (MitM). A MotS attack allows an adversary to read and inject packets, but not modify packets sent by other hosts. This paper presents practical experiments where we have implemented and performed MotS attacks against two testbeds: 1) on HTTP connections, by redirecting a victim to a host controlled by an adversary; and 2) on an Industrial Control network, where we inject falsified command responses to the victim. In both cases, the victims accept the injected packets without generating a suspiciously large number of unusual packets on the network. We then perform an analysis of three leading Network IDS to determine whether the attacks are detected, and discuss mitigation methods