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

Security and computer forensics in web engineering education

The integration of security and forensics into Web Engineering curricula is imperative! Poor security in web-based applications is continuing to cost organizations millions and the losses are still increasing annually. Security is frequently taught as a stand-alone course, assuming that security can be 'bolted on' to a web application at some point. Security issues must be integrated into Web Engineering processes right from the beginning to create secure solutions and therefore security should be an integral part of a Web Engineering curriculum. One aspect of Computer forensics investigates failures in security. Hence, students should be aware of the issues in forensics and how to respond when security failures occur; collecting evidence is particularly difficult for Web-based applications

Web Vulnerability Study of Online Pharmacy Sites

Consumers are increasingly using online pharmacies, but these sites may not provide an adequate level of security with the consumers’ personal data. There is a gap in this research addressing the problems of security vulnerabilities in this industry. The objective is to identify the level of web application security vulnerabilities in online pharmacies and the common types of flaws, thus expanding on prior studies. Technical, managerial and legal recommendations on how to mitigate security issues are presented. The proposed four-step method first consists of choosing an online testing tool. The next steps involve choosing a list of 60 online pharmacy sites to test, and then running the software analysis to compile a list of flaws. Finally, an in-depth analysis is performed on the types of web application vulnerabilities. The majority of sites had serious vulnerabilities, with the majority of flaws being cross-site scripting or old versions of software that have not been updated. A method is proposed for the securing of web pharmacy sites, using a multi-phased approach of technical and managerial techniques together with a thorough understanding of national legal requirements for securing systems

Towards Vulnerability Discovery Using Staged Program Analysis

Eliminating vulnerabilities from low-level code is vital for securing software. Static analysis is a promising approach for discovering vulnerabilities since it can provide developers early feedback on the code they write. But, it presents multiple challenges not the least of which is understanding what makes a bug exploitable and conveying this information to the developer. In this paper, we present the design and implementation of a practical vulnerability assessment framework, called Melange. Melange performs data and control flow analysis to diagnose potential security bugs, and outputs well-formatted bug reports that help developers understand and fix security bugs. Based on the intuition that real-world vulnerabilities manifest themselves across multiple parts of a program, Melange performs both local and global analyses. To scale up to large programs, global analysis is demand-driven. Our prototype detects multiple vulnerability classes in C and C++ code including type confusion, and garbage memory reads. We have evaluated Melange extensively. Our case studies show that Melange scales up to large codebases such as Chromium, is easy-to-use, and most importantly, capable of discovering vulnerabilities in real-world code. Our findings indicate that static analysis is a viable reinforcement to the software testing tool set.Comment: A revised version to appear in the proceedings of the 13th conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA), July 201