Arms Race in Adversarial Malware Detection: A Survey

Malicious software (malware) is a major cyber threat that has to be tackled with Machine Learning (ML) techniques because millions of new malware examples are injected into cyberspace on a daily basis. However, ML is vulnerable to attacks known as adversarial examples. In this article, we survey and systematize the field of Adversarial Malware Detection (AMD) through the lens of a unified conceptual framework of assumptions, attacks, defenses, and security properties. This not only leads us to map attacks and defenses to partial order structures, but also allows us to clearly describe the attack-defense arms race in the AMD context. We draw a number of insights, including: knowing the defender’s feature set is critical to the success of transfer attacks; the effectiveness of practical evasion attacks largely depends on the attacker’s freedom in conducting manipulations in the problem space; knowing the attacker’s manipulation set is critical to the defender’s success; and the effectiveness of adversarial training depends on the defender’s capability in identifying the most powerful attack. We also discuss a number of future research directions.

Ransomware Mitigation in the Modern Era: A Comprehensive Review, Research Challenges, and Future Directions

Although ransomware has been around since the early days of personal computers, its sophistication and aggression have increased substantially over the years. Ransomware, as a type of malware to extort ransom payments from victims, has evolved to deliver payloads in different attack vectors and on multiple platforms, and creating repeated disruptions and financial loss to many victims. Many studies have performed ransomware analysis and/or presented detection, defense, or prevention techniques for ransomware. However, because the ransomware landscape has evolved aggressively, many of those studies have become less relevant or even outdated. Previous surveys on anti-ransomware studies have compared the methods and results of the studies they surveyed, but none of those surveys has attempted to critique on the internal or external validity of those studies. In this survey, we first examined the up-to-date concept of ransomware, and listed the inadequacies in current ransomware research. We then proposed a set of unified metrics to evaluate published studies on ransomware mitigation, and applied the metrics to 118 such studies to comprehensively compare and contrast their pros and cons, with the attempt to evaluate their relative strengths and weaknesses. Finally, we forecast the future trends of ransomware evolution, and propose future research directions.

A Survey on Privacy Preservation in Fog-Enabled Internet of Things

Despite the rapid growth and advancement in the Internet of Things (IoT ), there are critical challenges that need to be addressed before the full adoption of the IoT. Data privacy is one of the hurdles towards the adoption of IoT as there might be potential misuse of users’ data and their identity in IoT applications. Several researchers have proposed different approaches to reduce privacy risks. However, most of the existing solutions still suffer from various drawbacks, such as huge bandwidth utilization and network latency, heavyweight cryptosystems, and policies that are applied on sensor devices and in the cloud. To address these issues, fog computing has been introduced for IoT network edges providing low latency, computation, and storage services. In this survey, we comprehensively review and classify privacy requirements for an in-depth understanding of privacy implications in IoT applications. Based on the classification, we highlight ongoing research efforts and limitations of the existing privacy-preservation techniques and map the existing IoT schemes with Fog-enabled IoT schemes to elaborate on the benefits and improvements that Fog-enabled IoT can bring to preserve data privacy in IoT applications. Lastly, we enumerate key research challenges and point out future research directions.

Centralized, Distributed, and Everything in between

The Internet of Things is taking hold in our everyday life. Regrettably, the security of IoT devices is often being overlooked. Among the vast array of security issues plaguing the emerging IoT, we decide to focus on access control, as privacy, trust, and other security properties cannot be achieved without controlled access. This article classifies IoT access control solutions from the literature according to their architecture (e.g., centralized, hierarchical, federated, distributed) and examines the suitability of each one for access control purposes. Our analysis concludes that important properties such as auditability and revocation are missing from many proposals while hierarchical and federated architectures are neglected by the community. Finally, we provide an architecture-based taxonomy and future research directions: a focus on hybrid architectures, usability, flexibility, privacy, and revocation schemes in serverless authorization.

Human Factors in Phishing Attacks: A Systematic Literature Review

Phishing is the fraudulent attempt to obtain sensitive information by disguising oneself as a trustworthy entity in digital communication. It is a type of cyber attack often successful because users are not aware of their vulnerabilities or are unable to understand the risks. This article presents a systematic literature review conducted to draw a “big picture” of the most important research works performed on human factors and phishing. The analysis of the retrieved publications, framed along the research questions addressed in the systematic literature review, helps in understanding how human factors should be considered to defend against phishing attacks. Future research directions are also highlighted.

A Survey on Blockchain Interoperability: Past, Present, and Future Trends

Blockchain interoperability is emerging as one of the crucial features of blockchain technology, but the knowledge necessary for achieving it is fragmented. This fact makes it challenging for academics and the industry to achieve interoperability among blockchains seamlessly. Given this new domain’s novelty and potential, we conduct a literature review on blockchain interoperability by collecting 284 papers and 120 grey literature documents, constituting a corpus of 404 documents. From those 404 documents, we systematically analyzed and discussed 102 documents, including peer-reviewed papers and grey literature. Our review classifies studies in three categories: Public Connectors, Blockchain of Blockchains, and Hybrid Connectors. Each category is further divided into sub-categories based on defined criteria. We classify 67 existing solutions in one sub-category using the Blockchain Interoperability Framework, providing a holistic overview of blockchain interoperability. Our findings show that blockchain interoperability has a much broader spectrum than cryptocurrencies and cross-chain asset transfers. Finally, this article discusses supporting technologies, standards, use cases, open challenges, and future research directions, paving the way for research in the area.

Graph Neural Networks: Taxonomy, Advances, and Trends

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers cannot see a panorama of the graph neural networks. This survey aims to overcome this limitation and provide a systematic and comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 327 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the challenges faced. It is expected that more and more scholars can understand and exploit the graph neural networks and use them in their research community.