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. 

/pdf/cyber-threat-intelligence-mining-for-proactive-cybersecurity-15a8iezo.pdf

Cyber Threat Intelligence Mining for Proactive Cybersecurity Defense: A Survey and New Perspectives

A rising number of organizations are showing a growing willingness to leverage cyber threat intelligence (CTI) for obtaining a full picture of cyber threat situation. Owing to the limited labels of cyber threat infrastructure nodes involved in CTI, automatically identifying the threat type of infrastructure nodes for early warning is also challenging. To tackle these challenges, a practical system called HinCTI is developed for modeling cyber threat intelligence and identifying threat types. We first design a threat intelligence meta-schema to depict the semantic relatedness of infrastructure nodes. We then model CTI on heterogeneous information network (HIN). Following, we define a meta-path and meta-graph instances-based threat Infrastructure similarity (MIIS) measure between threat infrastructure nodes and present a MIIS measure-based heterogeneous graph convolutional network (GCN) approach to identify the threat types of infrastructure nodes involved in CTI. To the best of our knowledge, this work is the first to model CTI on HIN for threat identification and propose a heterogeneous GCN-based approach for threat type identification of infrastructure nodes. With HinCTI, comprehensive experiments are conducted on real-world datasets, and experimental results demonstrate that our proposed approach can significantly improve the performance of threat type identification compared to the existing state-of-the-art baseline methods.

HinCTI: A Cyber Threat Intelligence Modeling and Identification System Based on Heterogeneous Information Network

Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.

/pdf/cyber-risk-at-the-edge-current-and-future-trends-on-cyber-2admm410oq.pdf

Cyber risk at the edge: current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains

Cyber Threat Intelligence (CTI) is threat information intended for security purposes. However, use for incident response demands standardization. This study examines the broader security incident response perspective. Introducing 18 core concepts, we assist efforts to establish and assess current standardization approaches. We further provide the reader with a detailed analysis of 6 incident response formats. While we synthesize structural elements, we point to characteristics and show format deficiencies. Also, we describe how core concepts can be used to determine a suitable format for a given use case. Our surveys’ findings indicate a consistent focus on incident response actions within all formats. Besides, playbooks are used to represent procedures. Different use cases suggest that organizations can leverage and combine multiple formats. Finally, we discuss open research challenges to fully realize incident response potentials.

https://epub.uni-regensburg.de/52721/1/Accepted_CTI%20Survey_IEEE_COMST_public.pdf

A Comparative Study on Cyber Threat Intelligence: The Security Incident Response Perspective

The very raison d’etre of cyber threat intelligence (CTI) is to provide meaningful knowledge about cyber security threats. The exchange and collaborative generation of CTI by the means of sharing platforms has proven to be an important aspect of practical application. It is evident to infer that inaccurate, incomplete, or outdated threat intelligence is a major problem as only high-quality CTI can be helpful to detect and defend against cyber attacks. Additionally, while the amount of available CTI is increasing it is not warranted that quality remains unaffected. In conjunction with the increasing number of available CTI, it is thus in the best interest of every stakeholder to be aware of the quality of a CTI artifact. This allows for informed decisions and permits detailed analyses. Our work makes a twofold contribution to the challenge of assessing threat intelligence quality. We first propose a series of relevant quality dimensions and configure metrics to assess the respective dimensions in the context of CTI. In a second step, we showcase the extension of an existing CTI analysis tool to make the quality assessment transparent to security analysts. Furthermore, analysts’ subjective perceptions are, where necessary, included in the quality assessment concept.

/pdf/measuring-and-visualizing-cyber-threat-intelligence-quality-25zrcx3r23.pdf

Measuring and visualizing cyber threat intelligence quality

Since the introduction of Security Operations Centers (SOCs) around 15 years ago, their importance has grown significantly, especially over the last five years. This is mainly due to the paramount necessity to prevent major cyber incidents and the resulting adoption of centralized security operations in businesses. Despite their popularity, existing academic work on the topic lacks a generally accepted view and focuses mainly on fragments rather than looking at it holistically. These shortcomings impede further innovation. In this paper, a comprehensive literature survey is conducted to collate different views. The discovered literature is then used to determine the current state-of-the-art of SOCs and derive primary building blocks. Current challenges within a SOC are identified and summarized. A notable shortcoming of academic research is its focus on the human and technological aspects of a SOC while neglecting the connection of these two areas by specific processes (especially by non-technical processes). However, this area is essential for leveraging the full potential of a SOC in the future.

/pdf/security-operations-center-a-systematic-study-and-open-4tgeqhxckq.pdf

Security Operations Center: A Systematic Study and Open Challenges

Today, permissioned blockchains are being adopted by large organizations for business critical operations. Consequently, they are subject to attacks by malicious actors. Researchers have discovered and enumerated a number of attacks that could threaten availability, integrity and confidentiality of blockchain data. However, currently it remains difficult to detect these attacks. We argue that security experts need appropriate visualizations to assist them in detecting attacks on blockchain networks. To achieve this, we develop HyperSec, a visual analytics monitoring tool that provides relevant information at a glance to detect ongoing attacks on Hyperledger Fabric. For evaluation, we connect the HyperSec prototype to a Hyperledger Fabric test network. The results show that common attacks on Fabric can be detected by a security expert using HyperSec’s visualizations.

HyperSec: Visual Analytics for Blockchain Security Monitoring

Isolated and easily protectable IT systems have developed into fragile and complex structures over the past years. These systems host manifold, flexible and highly connected applications, mainly in virtual environments. To ensure protection of those infrastructures, Security Incident and Event Management (SIEM) systems have been deployed. Such systems, however, suffer from many shortcomings such as lack of mechanisms for forensic readiness. In this extended abstract, we identify these shortcomings and propose an architecture which addresses them. It is developed within the DINGfest project, on which we report and for which we seek initial feedback from the community.

Introducing DINGfest: An architecture for next generation SIEM systems

ForCyRange: An Educational IoT Cyber Range for Live Digital Forensics

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