Over the last five years there has been an increase in the frequency and diversity of network attacks. This holds true, as more and more organisations admit compromises on a daily basis. Many misuse and anomaly based Intrusion Detection Systems (IDSs) that rely on either signatures, supervised or statistical methods have been proposed in the literature, but their trustworthiness is debatable. Moreover, as this work uncovers, the current IDSs are based on obsolete attack classes that do not reflect the current attack trends. For these reasons, this paper provides a comprehensive overview of unsupervised and hybrid methods for intrusion detection, discussing their potential in the domain. We also present and highlight the importance of feature engineering techniques that have been proposed for intrusion detection. Furthermore, we discuss that current IDSs should evolve from simple detection to correlation and attribution. We descant how IDS data could be used to reconstruct and correlate attacks to identify attackers, with the use of advanced data analytics techniques. Finally, we argue how the present IDS attack classes can be extended to match the modern attacks and propose three new classes regarding the outgoing network communication.

Working remotely from home within lockdown conditions can have implications on wellbeing and how people perceive and interact with technology to coordinate, communicate and collaborate with others. Previous research has shown relationships amongst life satisfaction, loneliness and problematic internet use and, also, between the latter and cyber security behaviours. We re-examine these relationships during the UK COVID-19 lockdown through an online survey completed by 299 participants working from home. The survey included demographics and work conditions questions and also the Satisfaction With Life Scale (SWLS), UCLA-3 Loneliness Scale, Problematic Internet Use Questionnaire-Short Form-6 (PIUQ-SF-6), and Security Behavior Intentions Scale (SeBIS). Structural equation modelling revealed that most notably, loneliness positively predicted problematic internet use, life satisfaction negatively predicted problematic internet use and that problematic internet use negatively predicted cyber security behaviours Implications includes educational and therapeutic interventions which could be applied by employers/governing bodies to target those at risk of loneliness and problematic internet use with the hope of mitigating these experiences and improving cyber security behaviours. The current findings may be especially relevant should waves of COVID-19 or similar crisis are experienced, and when working from home becomes a norm for some organisations and jobs.

Threat intelligence helps businesses and organisations make the right decisions in their fight against cyber threats, and strategically design their digital defences for an optimised and up-to-date security situation. Combined with advanced security analysis, threat intelligence helps reduce the time between the detection of an attack and its containment. This is achieved by continuously providing information, accompanied by data, on existing and emerging cyber threats and vulnerabilities affecting corporate networks. This paper addresses challenges that organisations are bound to face when they decide to invest in effective and interoperable cybersecurity information sharing and categorises them in a layered model. Based on this, it provides an evaluation of existing sources that share cybersecurity information. The aim of this research is to help organisations improve their cyber threat information exchange capabilities, to enhance their security posture and be more prepared against emerging threats.