Cybersecurity is a significant concern for businesses worldwide, as cybercriminals target business data and system resources. Cyber threat intelligence (CTI) enhances organizational cybersecurity resilience by obtaining, processing, evaluating, and disseminating information about potential risks and opportunities inside the cyber domain. This research investigates how companies can employ CTI to improve their precautionary measures against security breaches. The study follows a systematic review methodology, including selecting primary studies based on specific criteria and quality valuation of the selected papers. As a result, a comprehensive framework is proposed for implementing CTI in organizations. The proposed framework is comprised of a knowledge base, detection models, and visualization dashboards. The detection model layer consists of behavior-based, signature-based, and anomaly-based detection. In contrast, the knowledge base layer contains information resources on possible threats, vulnerabilities, and dangers to key assets. The visualization dashboard layer provides an overview of key metrics related to cyber threats, such as an organizational risk meter, the number of attacks detected, types of attacks, and their severity level. This relevant systematic study also provides insight for future studies, such as how organizations can tailor their approach to their needs and resources to facilitate more effective collaboration between stakeholders while navigating legal/regulatory constraints related to information sharing. 

A Systematic Literature Review on Cyber Threat Intelligence for Organizational Cybersecurity Resilience

Since its first steps in the cybersecurity field, Cyber Threat Intelligence (CTI) has gained recognition and increased its importance in the daily operations of cybersecurity teams. However, the many forms of CTI exchanged, the vast amount of CTI products, and the plurality of the sources have raised doubts about the CTI quality. This paper discusses the problem of CTI quality, focusing on the quality factors that better evaluate the products of CTI and how we measure them. Consequently, we propose a methodology for developing and assessing CTI quality metrics and demonstrate the application of this methodology by developing the relevance (<inline-formula> <tex-math notation="LaTeX">$RE$ </tex-math></inline-formula>) and weighted completeness (<inline-formula> <tex-math notation="LaTeX">$WC$ </tex-math></inline-formula>) metrics for unstructured and structured CTI products, respectively. We created two sets of structured and unstructured CTI data for this demonstration, utilizing them as benchmark datasets for estimating <inline-formula> <tex-math notation="LaTeX">$RE$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$WC$ </tex-math></inline-formula>.The proposed methodology introduces a systematic approach for developing and assessing quantitative CTI quality metrics for evaluating CTI data and CTI sources.

A Methodology for Developing & Assessing CTI Quality Metrics

Towards Cross-Physical-Domain Threat Inference for Industrial Control System Defense Adaptation

As an important part of the power system, the Internet of Things of power distribution is essential for the stable and reliable operation of the entire power system. In order to deal with new cyber attacks and threats, this paper proposes a threat tracing technology for power distribution Internet of Things security events. Firstly, the types of security incidents that may occur in the “cloud, pipe, edge, and end” links of the power distribution Internet of Things are sorted out. Secondly, on the basis of multi-source databases, the knowledge graph of security events is constructed to realize the correlation of security events. Thirdly, the knowledge graph is used to model the scene, and the attack scenario is reconstructed through the association between the event and the scene, so as to accurately reconstruct the generation path of the security event. Finally, experiments show that the technology can quickly locate the source of security events and realize the threat tracing of power distribution Internet of Things security events.

Threat Tracing of Power Distribution Automation System Based on Spatiotemporal and Business Logic Correlation Technology of Security Events

Automatic assessment and expansion of English vocabulary play a crucial role in language learning and proficiency evaluation. In this paper, we propose the use of an Optimized Bi-directional Long Short-Term Memory Deep Learning (Obi-LSTM-DL) model for these purposes. We conduct experiments using a comprehensive vocabulary dataset and evaluate the model's performance across various scenarios. The results demonstrate the effectiveness of the Obi-LSTM-DL model in accurately assessing vocabulary proficiency levels and expanding vocabulary knowledge. Through optimization experiments, we show that increasing the vocabulary size leads to improved model performance. Additionally, the model's proficiency level assessment capability allows for tailored instruction and support for language learners. Comparative analysis with baseline models further confirms the superiority of the Obi-LSTM-DL model. The results demonstrate the effectiveness of the Obi-LSTM-DL model in accurately assessing vocabulary proficiency levels and expanding vocabulary knowledge. Through optimization experiments, we show that increasing the vocabulary size leads to improved model performance, with an average increase in accuracy of 1.5% for every 1,000 words added to the vocabulary. Additionally, the model's proficiency level assessment capability allows for tailored instruction and support for language learners. Comparative analysis with baseline models further confirms the superiority of the Obi-LSTM-DL model, with an average increase in accuracy of 3.2% over traditional models.   

pdf/algorithm-design-and-implementation-of-automatic-assessment-1wfciqlkdp.pdf

Algorithm Design and Implementation of Automatic Assessment and Expansion of English Vocabulary

With the occurrence of cyber security incidents, the value of threat intelligence is coming to the fore. Timely extracting Indicator of Compromise (IOC) from cyber threat intelligence can quickly respond to threats. However, the sparse text in public threat intelligence scatters useful information, which makes it challenging to assess unstructured threat intelligence. In this paper, we proposed Cyber Threat Intelligence Automated Assessment Model (TIAM), a method to automatically assess highly sparse threat intelligence from multiple dimensions. TIAM implemented automatic classification of threat intelligence based on feature extraction, defined assessment criteria to quantify the value of threat intelligence, and combined ATT&CK to identify attack techniques related to IOC. Finally, we associated the identified IOCs, ATT&CK techniques, and intelligence quantification results. The experimental results shown that TIAM could better assess threat intelligence and help security managers to obtain valuable cyber threat intelligence.

An Automatic Assessment Method of Cyber Threat Intelligence Combined with ATT&CK Matrix

Internet of Things (IoT)has numerous applications in the industry and society, thanks to its ability to achieve automation and connectivity in a range of activities. Despite its great potentials, IoT is susceptible to physical and cyber-attacks, which causes security threats (e.g., financial risk and leakage of privacy). To address this problem, an approach for attack prediction is proposed for IoT. Aiming at a high degree of flexibility, an intelligent model is designed to construct knowledge graph by integrating equipment information CPE, vulnerability information CVE and attack pattern information CAPEC disclosed by the National Institute of Standards and Technology (NIST) and the security organization MITRE. Based on the knowledge graph, the safety analysis and operation analysis of many IOT information are carried out. To conclude the possible attack, knowledge representation learning method that fuses the triple information and semantic path combination information of the knowledge graph (FTSPC) was employed. We transform the attack prediction task into the link prediction problem. The suggested method is evaluated on a public dataset and our dataset, the results demonstrated that the method can predict the attack of IoT infrastructure, providing rich IoT security knowledge to security researchers and professionals and a useful reference for active defense.

Attack prediction in Internet of Things using knowledge graph

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An Automatic Assessment Method of Cyber Threat Intelligence Combined with ATT&CK Matrix

Attack prediction in Internet of Things using knowledge graph