Artificial intelligence (AI) and blockchain technology have emerged as increasingly prevalent and influential elements shaping global trends in Information and Communications Technology (ICT). Namely, the synergistic combination of blockchain and AI introduces beneficial, unique features with the potential to enhance the performance and efficiency of existing ICT systems. However, presently, the confluence of these two disruptive technologies remains in a rather nascent stage, undergoing continuous exploration and study. In this context, the work at hand offers insight regarding the most significant features of the AI and blockchain intersection. Sixteen outstanding, recent articles exploring the combination of AI and blockchain technology have been systematically selected and thoroughly investigated. From them, fourteen key features have been extracted, including data security and privacy, data encryption, data sharing, decentralized intelligent systems, efficiency, automated decision systems, collective decision making, scalability, system security, transparency, sustainability, device cooperation, and mining hardware design. Moreover, drawing upon the related literature stemming from major digital databases, we constructed a timeline of this technological convergence comprising three eras: emerging, convergence, and application. For the convergence era, we categorized the pertinent features into three primary groups: data manipulation, potential applicability to legacy systems, and hardware issues. For the application era, we elaborate on the impact of this technology fusion from the perspective of five distinct focus areas, from Internet of Things applications and cybersecurity, to finance, energy, and smart cities. This multifaceted, but succinct analysis is instrumental in delineating the timeline of AI and blockchain convergence and pinpointing the unique characteristics inherent in their integration. The paper culminates by highlighting the prevailing challenges and unresolved questions in blockchain and AI-based systems, thereby charting potential avenues for future scholarly inquiry.

This work serves two key objectives. First, it markedly supplements and extends the well-known AWID corpus by capturing and studying traces of a wide variety of attacks hurled in the IEEE 802.1X Extensible Authentication Protocol (EAP) environment. Second, given that all the 802.11-oriented attacks have been carried out when the defenses introduced by Protected Management Frames (PMF) were operative, it offers the first to our knowledge full-fledged empirical study regarding the robustness of the IEEE 802.11w amendment, which is mandatory for WPA3 certified devices. Under both the aforementioned settings, the dataset, and study at hand are novel and are anticipated to be of significant aid towards designing and evaluating intrusion detection systems. Moreover, in an effort to deliver a well-rounded dataset of greater lifespan, and under the prism of an attacker escalating their assault from the wireless MAC layer to higher ones, we have additionally included several assaults that are common to IEEE 802.3 networks. Since the corpus is publicly offered in the form of raw cleartext pcap files, future research can straightforwardly exploit any subset of features, depending on the particular application scenario.

It is without doubt that today the volume and sophistication of cyber attacks keeps consistently growing, militating an endless arm race between attackers and defenders. In this context, full-fledged frameworks, methodologies, or strategies that are able to offer optimal or near-optimal reaction in terms of countermeasure selection, preferably in a fully or semi-automated way, are of high demand. This is reflected in the literature, which encompasses a significant number of major works on this topic spanning over a time period of 5 years, that is, from 2012 to 2016. The survey at hand has a dual aim, namely: first, to critically analyze all the pertinent works in this field, and second to offer an in-depth discussion and side-by-side comparison among them based on 7 common criteria. Also, a quite extensive discussion is offered to highlight on the shortcomings and future research challenges and directions in this timely area.

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.