Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI's potential in improving cybersecurity solutions, by identifying both its strengths and weaknesses. We also discuss future research opportunities associated with the development of AI techniques in the cybersecurity field across a range of application domains.

The Internet of Things (IoT) applications has grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e. cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data is processed for machine learning analysis, and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications, and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.

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HTTP/2 is the second major version of the HTTP protocol published by the internet engineering steering group. The protocol is designed to improve reliability and performance Such enhancements have thus delineated the protocol as being more vulnerable to distributed denial-of-service (DDoS) attacks when compared to its predecessor. Recent phenomenon showed that legitimate traffic or flash crowds could have high-traffic flow characteristics as seen in DDoS attacks. In this paper, we demonstrate that legitimate HTTP/2 flash crowd traffic can be launched to cause denial of service. To the best of our knowledge, no previous study has been conducted to analyse the effect of both DDoS as well as flash crowd traffic against HTTP/2 services. Results obtained prove the effect of such attacks when tested under four varying protocol-dependant attack scenarios. © 2016, Springer Science+Business Media New York