A holistic and proactive approach to forecasting cyber threats

Abstract

Traditionally, cyber-attack detection relies on reactive, assistive techniques, where pattern-matching algorithms help human experts to scan system logs and network traffic for known virus or malware signatures. Recent research has introduced effective Machine Learning (ML) models for cyber-attack detection, promising to automate the task of detecting, tracking and blocking malware and intruders. Much less effort has been devoted to cyber-attack prediction, especially beyond the short-term time scale of hours and days. Approaches that can forecast attacks likely to happen in the longer term are desirable, as this gives defenders more time to develop and share defensive actions and tools. Today, long-term predictions of attack waves are mostly based on the subjective perceptiveness of experienced human experts, which can be impaired by the scarcity of cyber-security expertise. This paper introduces a novel ML-based approach that leverages unstructured big data and logs to forecast the trend of cyber-attacks at a large scale, years in advance. To this end, we put forward a framework that utilises a monthly dataset of major cyber incidents in 36 countries over the past 11 years, with new features extracted from three major categories of big data sources, namely the scientific research literature, news, blogs, and tweets. Our framework not only identifies future attack trends in an automated fashion, but also generates a threat cycle that drills down into five key phases that constitute the life cycle of all 42 known cyber threats.

CBF-IDS: Addressing Class Imbalance Using CNN-BiLSTM with Focal Loss in Network Intrusion Detection System

“…Additionally, attackers employ various strategies to evade detection, resulting in diverse patterns and temporal distributions in terms of their attack behavior. For instance, some attacks may occur during nonworking hours, or attackers might intentionally carry out attacks during holidays when network traffic is typically more congested, making it easier to conceal their malicious activities [47,48]. Therefore, network intrusion detection needs to consider the temporal dependencies before and after attack events to gain a comprehensive understanding of intrusion behavior patterns.…”

How to punish cyber criminals: A study to investigate the target and consequence based punishments for malware attacks in UK, USA, China, Ethiopia & Pakistan

“…Cybercriminals use malware or malicious software for many reasons. For instance: To illegally obtain sensitive data from the individual via stealing or online fraud, for example, passwords, bank and credit card details and business-related data, and so on [23] . To acquire remote admittance to machines and networks.…”

“…To illegally obtain sensitive data from the individual via stealing or online fraud, for example, passwords, bank and credit card details and business-related data, and so on [23] .…”

Rapid Forecasting of Cyber Events Using Machine Learning-Enabled Features

“…The trend is to move away from traditional security approaches and embrace predictive methods capable of detecting sophisticated attacks [4]. The cost of cyber attacks continues to increase, and it will likely follow the same trajectory.…”