Security Evaluation of Support Vector Machines in Adversarial Environments

Support Vector Machines (SVMs) are among the most popular classification techniques adopted in security applications like malware detection, intrusion detection, and spam filtering. However, if SVMs are to be incorporated in real-world security systems, they must be able to cope with attack patterns that can either mislead the learning algorithm (poisoning), evade detection (evasion), or gain information about their internal parameters (privacy breaches). The main contributions of this chapter are twofold. First, we introduce a formal general framework for the empirical evaluation of the security of machine-learning systems. Second, according to our framework, we demonstrate the feasibility of evasion, poisoning and privacy attacks against SVMs in real-world security problems. For each attack technique, we evaluate its impact and discuss whether (and how) it can be countered through an adversary-aware design of SVMs. Our experiments are easily reproducible thanks to open-source code that we have made available, together with all the employed datasets, on a public repository.Comment: 47 pages, 9 figures; chapter accepted into book 'Support Vector Machine Applications

Applications in security and evasions in machine learning : a survey

In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications' perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers' knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks

On the security of machine learning in malware C & C detection:a survey

One of the main challenges in security today is defending against malware attacks. As trends and anecdotal evidence show, preventing these attacks, regardless of their indiscriminate or targeted nature, has proven difficult: intrusions happen and devices get compromised, even at security-conscious organizations. As a consequence, an alternative line of work has focused on detecting and disrupting the individual steps that follow an initial compromise and are essential for the successful progression of the attack. In particular, several approaches and techniques have been proposed to identify the command and control (C&C) channel that a compromised system establishes to communicate with its controller. A major oversight of many of these detection techniques is the design's resilience to evasion attempts by the well-motivated attacker. C&C detection techniques make widespread use of a machine learning (ML) component. Therefore, to analyze the evasion resilience of these detection techniques, we first systematize works in the field of C&C detection and then, using existing models from the literature, go on to systematize attacks against the ML components used in these approaches

Online Deception Detection Using BDI Agents

This research has two facets within separate research areas. The research area of Belief, Desire and Intention (BDI) agent capability development was extended. Deception detection research has been advanced with the development of automation using BDI agents. BDI agents performed tasks automatically and autonomously. This study used these characteristics to automate deception detection with limited intervention of human users. This was a useful research area resulting in a capability general enough to have practical application by private individuals, investigators, organizations and others. The need for this research is grounded in the fact that humans are not very effective at detecting deception whether in written or spoken form. This research extends the deception detection capability research in that typical deception detection tools are labor intensive and require extraction of the text in question following ingestion into a deception detection tool. A neural network capability module was incorporated to lend the resulting prototype Machine Learning attributes. The prototype developed as a result of this research was able to classify online data as either deceptive or not deceptive with 85% accuracy. The false discovery rate for deceptive online data entries was 20% while the false discovery rate for not deceptive was 10%. The system showed stability during test runs. No computer crashes or other anomalous system behavior were observed during the testing phase. The prototype successfully interacted with an online data communications server database and processed data using Neural Network input vector generation algorithms within second

Analysis and Classification of Current Trends in Malicious HTTP Traffic

Web applications are highly prone to coding imperfections which lead to hacker-exploitable vulnerabilities. The contribution of this thesis includes detailed analysis of malicious HTTP traffic based on data collected from four advertised high-interaction honeypots, which hosted different Web applications, each in duration of almost four months. We extract features from Web server logs that characterize malicious HTTP sessions in order to present them as data vectors in four fully labeled datasets. Our results show that the supervised learning methods, Support Vector Machines (SVM) and Decision Trees based J48 and PART, can be used to efficiently distinguish attack sessions from vulnerability scan sessions, as well as efficiently classify twenty-two different types of malicious activities with high probability of detection and very low probability of false alarms for most cases. Furthermore, feature selection methods can be used to select important features in order to improve the computational complexity of the learners

Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks

Malware still constitutes a major threat in the cybersecurity landscape, also due to the widespread use of infection vectors such as documents. These infection vectors hide embedded malicious code to the victim users, facilitating the use of social engineering techniques to infect their machines. Research showed that machine-learning algorithms provide effective detection mechanisms against such threats, but the existence of an arms race in adversarial settings has recently challenged such systems. In this work, we focus on malware embedded in PDF files as a representative case of such an arms race. We start by providing a comprehensive taxonomy of the different approaches used to generate PDF malware, and of the corresponding learning-based detection systems. We then categorize threats specifically targeted against learning-based PDF malware detectors, using a well-established framework in the field of adversarial machine learning. This framework allows us to categorize known vulnerabilities of learning-based PDF malware detectors and to identify novel attacks that may threaten such systems, along with the potential defense mechanisms that can mitigate the impact of such threats. We conclude the paper by discussing how such findings highlight promising research directions towards tackling the more general challenge of designing robust malware detectors in adversarial settings