Today’s cyber attacks have become more severe and frequent, which calls for a new line of security defenses to protect against them. The dynamic nature of new-generation threats, which are evasive, resilient, and complex, makes traditional security systems based on heuristics and signatures struggle to match. Organizations aim to gather and share real-time cyber threat information and then turn it into threat intelligence for preventing attacks or, at the very least, responding quickly in a proactive manner. Cyber Threat Intelligence (CTI) mining, which uncovers, processes, and analyzes valuable information about cyber threats, is booming. However, most organizations today mainly focus on basic use cases, such as integrating threat data feeds with existing network and firewall systems, intrusion prevention systems, and Security Information and Event Management systems (SIEMs), without taking advantage of the insights that such new intelligence can deliver. In order to make the most of CTI so as to significantly strengthen security postures, we present a comprehensive review of recent research efforts on CTI mining from multiple data sources in this article. Specifically, we provide and devise a taxonomy to summarize the studies on CTI mining based on the intended purposes (i.e., cybersecurity-related entities and events, cyber attack tactics, techniques and procedures, profiles of hackers, indicators of compromise, vulnerability exploits and malware implementation, and threat hunting), along with a comprehensive review of the current state-of-the-art. Lastly, we discuss research challenges and possible future research directions for CTI mining.

Driven by the increasing scale and high profile cybersecurity incidents related public data, recent years we have witnessed a paradigm shift in understanding and defending against the evolving cyber threats, from primarily reactive detection towards proactive prediction. Meanwhile, governments, businesses, and individual internet users show the growing public appetite to improve cyber resilience that refers to their ability to prepare for, combat and recover from cyber threats and incidents. Undoubtedly, predicting cybersecurity incidents is deemed to have excellent potential for proactively advancing cyber resilience. Research communities and industries have begun proposing cybersecurity incident prediction schemes by utilizing different types of data sources, including organization’s reports and datasets, network data, synthetic data, data crawled from webpages, and data retrieved from social media. With a focus on the dataset, this survey paper investigates the emerging research by reviewing recent representative works appeared in the dominant period. We also extract and summarize the data-driven research methodology commonly adopted in this fast-growing area. In consonance with the phases of the methodology, each work that predicts cybersecurity incident is comprehensively studied. Challenges and future directions in this field are also discussed.

The popularity of social media networks, such as Twitter, leads to an increasing number of spamming activities. Researchers employed various machine learning methods to detect Twitter spam. However, majorities of existing researches are limited to theoretically study, few of them can apply detection techniques to real-time scenario. In this paper, we bridge the gap by proposing a near real-time Twitter spam detection system, which provides near real-time tweets data acquisition, light-weight features extraction from a specific Twitter account, training detection model, and online visualizing detection results. In this system, account-based and content-based features are extracted to facilitate spam detection. The models that are applied to our Twitter spam detection system are trained based on 1.5 million public tweets and nine mainstream algorithms. In addition, in order to efficiently reduce training time spent on massive data and save the cost of model updating, a parallel computing technique is introduced to train and update the models in this system. Empirical results verify that the model can achieve satisfactory performance based on our datasets. Furthermore, we implement a near real-time Twitter spam detection system which can better protect users from combating spams. This system also acts as a tweets collection tool, allowing researchers to test the performance of trained classifiers in realistic scenarios.

This article reviews the latest in machine unlearning for large language models (LLMs), introducing methods for the targeted forgetting of information to address privacy, ethical, and legal challenges without necessitating full-model retraining. Machine unlearning is a cutting-edge solution, mitigating risk concerns of LLMs and artificial intelligence.

Advances in emerging Information and Communications Technology (ICT) technologies push the boundaries of what is possible and open up new markets for innovative ICT products and services. The adoption of ICT products and systems with security properties depends on consumers’ confidence and markets’ trust in the security functionalities and whether the assurance measures applied to these products meet the inherent security requirements. Such confidence and trust are primarily gained through the rigorous development of security requirements, validation criteria, evaluation, and certification. The Common Criteria for Information Technology Security Evaluation (often referred to as Common Criteria or CC) is an international standard (ISO/IEC 15408) for cyber security. Motivated by encouraging the adoption of the CC that is used for ICT security evaluation and certification, in this paper, we conduct a systematic review of the CC standard and its adoptions. Adoption barriers of the CC are investigated based on the analysis of current trends in cyber security evaluation. In addition, we share the experiences and lessons gained through the recent Development of Australian Cyber Criteria Assessment (DACCA) project on the development of the Protection Profile that defines security requirements with the CC. Best practices, challenges, and future directions on defining security requirements for trusted cyber security advancement are presented.