The ever-evolving and intricate nature of cyber environments, coupled with the escalating risk of cyber-attacks, necessitates robust solutions in the realm of cybersecurity. Knowledge graphs have emerged as a promising avenue for consolidating, representing, managing, and reasoning over cyber threat intelligence. However, applying knowledge graphs to tackle real-world challenges in cyber-attack and defense scenarios remains an area requiring further exploration. This paper aims to address this gap by providing a comprehensive overview of the fundamental concepts, schema design, and construction methodologies for the cybersecurity knowledge graph. To facilitate future research endeavors, we have carefully curated datasets and open-source libraries tailored for knowledge construction and information extraction tasks. Furthermore, we present a detailed comparative review of recent advancements in the application scenarios of cybersecurity knowledge graphs. To provide clarity and organization, we introduce a novel classification framework that categorizes interconnected works into distinct primary categories and subcategories. The paper concludes by outlining potential research directions in the cybersecurity knowledge graph domain, paving the way for further advancements and innovations in the field.

Organizations strive to secure their valuable data and minimise potential damages, recognising that critical operations are susceptible to attacks. This research paper seeks to elucidate the concept of proactive cyber threat hunting. The proposed framework is to help organisations check their preparedness against upcoming threats and their probable mitigation plan. While traditional threat detection methods have been implemented, they often need to address the evolving landscape of advanced cyber threats. Organisations must adopt proactive threat-hunting strategies to safeguard business operations and identify and mitigate unknown or undetected network threats. This research proposes a conceptual model based on a review of the literature. The proposed framework will help the organisation recover from the attack. As the recovery time is less, the financial loss for the company will also be reduced. Also, the attacker might need more time to gather data, so there will be less stealing of confidential information. Cybersecurity companies use proactive cyber defence strategies to reduce an attacker s time on the network. The different frameworks used are SANS, MITRE, Hunting ELK, Logstash, Digital Kill Chain, Model in Diamonds, and NIST Framework for Cybersecurity, which proposes a proactive approach. It is beneficial for the defensive security team to assess their capabilities to defend against Advanced Threats Persistent (ATP) and a wide range of attack vectors.

Advanced persistent threat (APT) attack is one of the most serious threats to power system cyber security. ATT\&CK framework integrates the known historical and practical APT attack tactics and techniques to form a general language for describing hacker behavior and an abstract knowledge base framework for hacker attacks. Combined with the ATT\&CK for ICS framework, this paper combed the known attack techniques used by viruses or hacker groups aimed at cyberattacks on infrastructure, especially power systems. Then found the corresponding mitigations for each attack technique, and merged them. Next, we listed the high frequency and important mitigations for reference. At last, we proposed a cyber security defense model suitable for ICS to provide a reference for security teams on how to apply ATT\&ck; other similar cyberattack frameworks.

The rapid growth of communication networks, coupled with the increasing complexity of cyber threats, necessitates the implementation of proactive measures to protect networks and systems. In this study, we introduce a federated learning-based approach for cyber threat hunting at the endpoint level. The proposed method utilizes the collective intelligence of multiple devices to effectively and confidentially detect attacks on individual machines. A security assessment tool is also developed to emulate the behavior of adversary groups and Advanced Persistent Threat (APT) actors in the network. This tool provides network security experts with the ability to assess their network environment s resilience and aids in generating authentic data derived from diverse threats for use in subsequent stages of the federated learning (FL) model. The results of the experiments demonstrate that the proposed model effectively detects cyber threats on the devices while safeguarding privacy.

Advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. APT is a sophisticated attack that masquerade their actions to navigates around defenses, breach networks, often, over multiple network hosts and evades detection. It also uses “low-and-slow” approach over a long period of time. Resource availability, integrity, and confidentiality of the operational cyber-physical systems (CPS) state and control is highly impacted by the safety and security measures in place. A framework multi-stage detection approach termed “APT$_\textrmDASAC$” to detect different tactics, techniques, and procedures (TTPs) used during various APT steps is proposed. Implementation was carried out in three stages: (i) Data input and probing layer - this involves data gathering and pre-processing, (ii) Data analysis layer; applies the core process of “APT$_\textrmDASAC$” to learn the behaviour of attack steps from the sequence data, correlate and link the related output and, (iii) Decision layer; the ensemble probability approach is utilized to integrate the output and make attack prediction. The framework was validated with three different datasets and three case studies. The proposed approach achieved a significant attacks detection capability of 86.36\% with loss as 0.32\%, demonstrating that attack detection techniques applied that performed well in one domain may not yield the same good result in another domain. This suggests that robustness and resilience of operational systems state to withstand attack and maintain system performance are regulated by the safety and security measures in place, which is specific to the system in question.

Advanced Persistent Threats (APTs) have significantly impacted organizations over an extended period with their coordinated and sophisticated cyberattacks. Unlike signature-based tools such as antivirus and firewalls that can detect and block other types of malware, APTs exploit zero-day vulnerabilities to generate new variants of undetectable malware. Additionally, APT adversaries engage in complex relationships and interactions within network entities, necessitating the learning of interactions in network traffic flows, such as hosts, users, or IP addresses, for effective detection. However, traditional deep neural networks often fail to capture the inherent graph structure and overlook crucial contextual information in network traffic flows. To address these issues, this research models APTs as heterogeneous graphs, capturing the diverse features and complex interactions in network flows. Consequently, a hetero-geneous graph transformer (HGT) model is used to accurately distinguish between benign and malicious network connections. Experiment results reveal that the HGT model achieves better performance, with 100 \% accuracy and accelerated learning time, outperferming homogeneous graph neural network models.

Advanced persistent threats (APTs) have novel features such as multi-stage penetration, highly-tailored intention, and evasive tactics. APTs defense requires fusing multi-dimensional Cyber threat intelligence data to identify attack intentions and conducts efficient knowledge discovery strategies by data-driven machine learning to recognize entity relationships. However, data-driven machine learning lacks generalization ability on fresh or unknown samples, reducing the accuracy and practicality of the defense model. Besides, the private deployment of these APT defense models on heterogeneous environments and various network devices requires significant investment in context awareness (such as known attack entities, continuous network states, and current security strategies). In this paper, we propose a few-shot multi-domain knowledge rearming (FMKR) scheme for context-aware defense against APTs. By completing multiple small tasks that are generated from different network domains with meta-learning, the FMKR firstly trains a model with good discrimination and generalization ability for fresh and unknown APT attacks. In each FMKR task, both threat intelligence and local entities are fused into the support/query sets in meta-learning to identify possible attack stages. Secondly, to rearm current security strategies, an finetuning-based deployment mechanism is proposed to transfer learned knowledge into the student model, while minimizing the defense cost. Compared to multiple model replacement strategies, the FMKR provides a faster response to attack behaviors while consuming less scheduling cost. Based on the feedback from multiple real users of the Industrial Internet of Things (IIoT) over 2 months, we demonstrate that the proposed scheme can improve the defense satisfaction rate.

Past Advanced Persistent Threat (APT) attacks on Industrial Internet-of-Things (IIoT), such as the 2016 Ukrainian power grid attack and the 2017 Saudi petrochemical plant attack, have shown the disruptive effects of APT campaigns while new IIoT malware continue to be developed by APT groups. Existing APT detection systems have been designed using cyberattack TTPs modelled for enterprise IT networks and leverage specific data sources (e.g., Linux audit logs, Windows event logs) which are not found on ICS devices. In this work, we propose RAPTOR, a system to detect APT campaigns in IIoT. Using cyberattack TTPs modelled for ICS/OT environments and focusing on ‘invariant’ attack phases, RAPTOR detects and correlates various APT attack stages in IIoT leveraging data which can be readily collected from ICS devices/networks (packet traffic traces, IDS alerts). Subsequently, it constructs a high-level APT campaign graph which can be used by cybersecurity analysts towards attack analysis and mitigation. A performance evaluation of RAPTOR’s APT attack-stage detection modules shows high precision and low false positive/negative rates. We also show that RAPTOR is able to construct the APT campaign graph for APT attacks (modelled after real-world attacks on ICS/OT infrastructure) executed on our IIoT testbed.

With the rapid evolution of the Internet and the prevalence of sophisticated adversarial cyber threats, it has become apparent that an equally rapid development of new Situation Awareness techniques is needed. The vast amount of data produced everyday by Intrusion Detection Systems, Firewalls, Honeypots and other systems can quickly become insurmountable to analyze by the domain experts. To enhance the human - machine interaction, new Visual Analytics systems need to be implemented and tested, bridging the gap between the detection of possible malicious activity, identifying it and taking the necessary measures to stop its propagation. The detection of previously unknown, highly sophisticated Advanced Persistent Threats (APT) adds a higher degree of complexity to this task. In this paper, we discuss the principles inherent to Visual Analytics and propose a new technique for the detection of APT attacks through the use of anomaly and behavior-based analysis. Our ultimate goal is to define sophisticated cyber threats by their defining characteristics and combining those to construct a pattern of behavior, which can be presented in visual form to be explored and analyzed. This can be achieved through the use of our Multi-Agent System for Advanced Persistent Threat Detection (MASFAD) framework and the combination of highly-detailed and dynamic visualization techniques. This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST) Panel, IST-200 RSY - the ICMCIS, held in Skopje, North Macedonia, 16–17 May 2023.

As cyber attacks grow in complexity and frequency, cyber threat intelligence (CTI) remains a priority objective for defenders. A critical component of CTI at the strategic level of defensive operations is attack attribution. Attributing an attack to a threat group informs defenders on adversaries that are actively engaging them and advances their ability respond. In this paper, we propose a data analytic approach towards threat attribution using adversary playbooks of tactics, techniques, and procedures (TTPs). Specifically, our approach uses association rule mining on a large real world CTI dataset to extend known threat TTP playbooks with statistically probable TTPs the adversary may deploy. The benefits are twofold. First, we offer a dataset of learned TTP associations and extended threat playbooks. Second, we show that we can attribute attacks using a weighted Jaccard similarity with 96\% accuracy.

Advanced Persistent Threat (APT) attacks are complex, employing diverse attack elements and increasingly intelligent techniques. This paper introduces a tool for security risk assessment specifically designed for these attacks. This tool assists security teams in systematically analyzing APT attacks to derive adaptive security requirements for mission-critical target systems. Additionally, the tool facilitates the assessment of security risks, providing a comprehensive understanding of their impact on target systems. By leveraging this tool, security teams can enhance defense strategies, mitigating potential threats and ensuring the security of target systems.

The rapid growth of communication networks, coupled with the increasing complexity of cyber threats, necessitates the implementation of proactive measures to protect networks and systems. In this study, we introduce a federated learning-based approach for cyber threat hunting at the endpoint level. The proposed method utilizes the collective intelligence of multiple devices to effectively and confidentially detect attacks on individual machines. A security assessment tool is also developed to emulate the behavior of adversary groups and Advanced Persistent Threat (APT) actors in the network. This tool provides network security experts with the ability to assess their network environment s resilience and aids in generating authentic data derived from diverse threats for use in subsequent stages of the federated learning (FL) model. The results of the experiments demonstrate that the proposed model effectively detects cyber threats on the devices while safeguarding privacy.