COMPARATIVE ANALYSIS OF THREAT INTELLIGENCE SHARING PLATFORMS IN CYBER ATTACK PREDICTION AND PREVENTION

Abstract

Cyber threats have increased risk levels for the individuals, organizations, and countries. The threat of these risks is easily mitigated however, employing threat intelligence sharing platforms (TISPs) has come out as one of the most effective ways of dealing with such risks in cybersecurity. The above platforms allow entity to share and analyze threat data so as to deter and forecast attacks. However, there is a problem of selecting the best TISP given the number and variety of firms in this business. This paper aims at comparing different TISPs in terms of attributes, efficiency, and applicability to identify and counteract an attack type. Some of the important decision criteria include: ‘Number and frequency of data sharing, analytical capability, flexibility of use, compatibility with existing systems’. The research shows that TISPs differ greatly in how well they can estimate an attack and offer protection proposals. Based on findings from this study, it is found that practicing professionals might be interested in is how to choose the right TISP for their cybersecurity needs.