Year after year, mobile malware attacks grow in both sophistication and diffusion. As the open source Android platform continues to dominate the market, malware writers consider it as their preferred target. Almost strictly, state-of-the-art mobile malware detection solutions in the literature capitalize on machine learning to detect pieces of malware. Nevertheless, our findings clearly indicate that the majority of existing works utilize different metrics and models and employ diverse datasets and classification features stemming from disparate analysis techniques, i.e., static, dynamic, or hybrid. This complicates the cross-comparison of the various proposed detection schemes and may also raise doubts about the derived results. To address this problem, spanning a period of the last seven years, this work attempts to schematize the so far ML-powered malware detection approaches and techniques by organizing them under four axes, namely, the age of the selected dataset, the analysis type used, the employed ML techniques, and the chosen performance metrics. Moreover, based on these axes, we introduce a converging scheme which can guide future Android malware detection techniques and provide a solid baseline to machine learning practices in this field.

Breaches in the cyberspace due to cyber-physical attacks can harm the physical space, and any type of vehicle is an alluring target for wrongdoers for an assortment of reasons. Especially, as the automobiles are becoming increasingly interconnected within the Cooperative Intelligent Transport System (C-ITS) realm and their level of automation elevates, the risk for cyberattacks augments along with the attack surface, thus inexorably rendering the risk of complacency and inaction sizable. Next to other defensive measures, intrusion detection systems (IDS) already comprise an inextricable component of modern automobiles in charge of detecting intrusions in the system while in operation. This work concentrates on in-vehicle IDS with the goal to deliver a fourfold comprehensive survey of surveys on this topic. First, we collect and analyze all existing in-vehicle IDS classifications and fuse them into a simpler, overarching one that can be used as a base for classifying any work in this area. Second, we gather and elaborate on the so-far available datasets which can be possibly used to train and evaluate an in-vehicle IDS. Third, we survey non-commercial simulators which may be utilized for creating a dataset or evaluating an IDS. The last contribution pertains to a thorough exposition of the future trends and challenges in this area. To our knowledge, this work provides the first wholemeal survey on in-vehicle IDS, and it is therefore anticipated to serve as a groundwork and point of reference for multiple stakeholders at varying levels.

The evolution of cybersecurity is closely linked to the development and improvement of artificial intelligence (AI). As a key tool for realizing more cybersecure ecosystems, Intrusion Detection Systems (IDSs) have evolved tremendously in recent years by integrating machine learning (ML) techniques to detect increasingly sophisticated cybersecurity attacks hidden in big data. However, traditional approaches rely on centralized learning, in which data from end nodes are shared with data centers for analysis. Recently, the application of federated learning (FL) in this context has attracted great interest to come up with collaborative intrusion detection approaches where data does not need to be shared. Due to the recent rise of this field, this work presents a complete, contemporary taxonomy for FL-enabled IDS approaches that stems from a comprehensive survey of the literature from 2018 to 2022. Precisely, our discussion includes an analysis of the main ML models, datasets, aggregation functions, as well as implementation libraries employed by the proposed FL-enabled IDS approaches. On top of everything else, we provide a critical view of the current state of the research around this topic, and describe the main challenges and future directions based on the analysis of the literature and our own experience in this area.

Modern mobile devices are equipped with a variety of tools and services, and handle increasing amounts of sensitive information. In the same trend, the number of vulnerabilities exploiting mobile devices are also augmented on a daily basis and, undoubtedly, popular mobile platforms, such as Android and iOS, represent an alluring target for malware writers. While researchers strive to find alternative detection approaches to fight against mobile malware, recent reports exhibit an alarming increase in mobile malware exploiting victims to create revenues, climbing towards a billion-dollar industry. Current approaches to mobile malware analysis and detection cannot always keep up with future malware sophistication [2],[4]. The aim of this work is to provide a structured and comprehensive overview of the latest research on mobile malware detection techniques and pinpoint their benefits and limitations.

The openness of the Android operating system and its immense penetration into the market makes it a hot target for malware writers. This work introduces Androtomist, a novel tool capable of symmetrically applying static and dynamic analysis of applications on the Android platform. Unlike similar hybrid solutions, Androtomist capitalizes on a wealth of features stemming from static analysis along with rigorous dynamic instrumentation to dissect applications and decide if they are benign or not. The focus is on anomaly detection using machine learning, but the system is able to autonomously conduct signature-based detection as well. Furthermore, Androtomist is publicly available as open source software and can be straightforwardly installed as a web application. The application itself is dual mode, that is, fully automated for the novice user and configurable for the expert one. As a proof-of-concept, we meticulously assess the detection accuracy of Androtomist against three different popular malware datasets and a handful of machine learning classifiers. We particularly concentrate on the classification performance achieved when the results of static analysis are combined with dynamic instrumentation vis-à-vis static analysis only. Our study also introduces an ensemble approach by averaging the output of all base classification models per malware instance separately, and provides a deeper insight on the most influencing features regarding the classification process. Depending on the employed dataset, for hybrid analysis, we report notably promising to excellent results in terms of the accuracy, F1, and AUC metrics.

Built on top of UDP, the recently standardized QUIC protocol primarily aims to gradually replace the TCP plus TLS plus HTTP/2 model. For instance, HTTP/3 is designed to exploit QUIC’s features, including reduced connection establishment time, multiplexing without head of line blocking, always-encrypted end-to-end security, and others. This work serves two key objectives. Initially, it offers the first to our knowledge full-fledged review on QUIC security as seen through the lens of the relevant literature so far. Second and more importantly, through extensive fuzz testing, we conduct a hands-on security evaluation against the six most popular QUIC-enabled production-grade servers. This assessment identified several effective and practical zero-day vulnerabilities, which, if exploited, can quickly overwhelm the server resources. This finding is a clear indication that the fragmented production-level implementations of this contemporary protocol are not yet mature enough. Overall, the work at hand provides the first wholemeal appraisal of QUIC security from both a literature review and empirical standpoint, and it is therefore foreseen to serve as a reference for future research in this timely area.