Biblio

The number of organisational cybersecurity threats continues to increase every year as technology advances. All too often, organisations assume that implementing systems security measures like firewalls and anti-virus software will eradicate cyber threats. However, even the most robust security systems are vulnerable to threats. As advanced as machine learning cybersecurity technology is becoming, it cannot be solely relied upon to solve cyber threats. There are other forces that contribute to these threats that are many-a-times out of an organisation's control i.e., human behaviour. This research article aims to create an understanding of the trends in key cybersecurity management issues that have developed in the past five years in relation to human behaviour and machine learning. The methodology adopted to guide the synthesis of this review was a systematic literature review. The guidelines for conducting the review are presented in the review approach. The key cybersecurity management issues highlighted by the research includes risky security behaviours demonstrated by employees, social engineering, the current limitations present in machine learning insider threat detection, machine learning enhanced cyber threats, and the underinvestment challenges faced in the cybersecurity domain.

In a continually evolving cyber-threat landscape, the detection and prevention of cyber attacks has become a complex task. Technological developments have led organisations to digitise the majority of their operations. This practice, however, has its perils, since cybespace offers a new attack-surface. Institutions which are tasked to protect organisations from these threats utilise mainly network data and their incident response strategy remains oblivious to the needs of the organisation when it comes to protecting operational aspects. This paper presents a system able to combine threat intelligence data, attack-trend data and organisational data (along with other data sources available) in order to achieve automated network-defence actions. Our approach combines machine learning, visual analytics and information from business processes to guide through a decision-making process for a Security Operation Centre environment. We test our system on two synthetic scenarios and show that correlating network data with non-network data for automated network defences is possible and worth investigating further.