Field Of Cybersecurity Research Articles

Quantum intrusion detection system using outlier analysis

In the field of cybersecurity, hackers often enter computer systems despite current security measures, owing to the huge amount of network traffic that makes intruder identification difficult. Differentiating between authorized traffic and abnormal data produced by Distributed Denial of Service (DDoS) attackers is still a major difficulty. This research provides a unique technique that uses Quantum Machine Learning (QML) to improve security protocols for secure communication between two parties. Our strategy enhances detection accuracy and speed by using the characteristics of quantum neural networks. The approach includes creating a dataset from network traffic patterns, preprocessing it, and then turning it into quantum bits via angle embedding. The research uses outlier analysis, min-entropy, and quantum state fidelity to distinguish between normal and abnormal data patterns. The dispersed randomness of network header data is measured using entropy, which aids in identifying security concerns. The suggested QML-based methodology outperforms conventional approaches and current models, including AMM-CNN and ANN models, with a detection accuracy of 99.87% for DDoS attacks. This advancement leads to more effective and secure communication networks.

Navigating Challenges in Online Cybersecurity Education: Insights from Postgraduate Students and Prospects for a Standardized Framework

This study focuses on the challenges encountered in online cybersecurity education. It adopts an exploratory research design using a mixed-methods approach to investigate the perceptions and experiences of postgraduate students enrolled in an online cybersecurity program. The collection of data is structured into two distinct phases. In the initial phase, qualitative insights are gathered through workshops with students and industry experts, followed by administering a questionnaire to delve deeper into students’ perceptions and challenges. Thematic analysis of the responses reveals significant interest in online cybersecurity programs but also highlights issues such as ineffective communication and poor engagement. The findings highlight the necessity for a standardized framework to improve communication, engagement, and overall effectiveness in virtual learning environments. By empowering instructors to design more interactive courses and leveraging technology efficiently, the framework aims to improve student motivation, satisfaction, and learning outcomes. Moreover, it serves as a valuable resource for institutions, fostering collaboration and innovation in cybersecurity education. Implementing this framework will create a more conducive learning environment, directly enhancing student preparation for success in the dynamic field of cybersecurity.

Analysis of the Legislation of the EEU Countries in the Field of Combating Cybercrime

The article presents an analysis of cybersecurity legislation in the member countries of the Eurasian Economic Union (EAEU), focusing on Russia, Kazakhstan, Kyrgyzstan and Belarus. In four parts of the article, the main aspects of the legal regulation of cybercrime in each of these countries are considered, as well as problems and shortcomings in legislation are identified. Outdated norms and ambiguities in legislation that are unable to adequately respond to modern challenges of cybercrime are discussed. The importance of an integrated approach to cybersecurity is emphasized, which includes not only legal regulation, but also technical, organizational and educational measures. It is proposed to conduct a more in-depth analysis and comparison of legislation to identify common trends, differences and the effectiveness of measures taken in the fight against cybercrime.Aim. Analysis of the legislation of the Eurasian Economic Union (EAEU) member states in the field of cybersecurity and cybercrime prevention, identification of common trends and differences, evaluation of the effectiveness of measures taken, and determination of directions for further improvement of cybersecurity in the region.Tasks. Analysis of the legislation of the EAEU member states in the field of cybersecurity, evaluation of measures taken to counter cyber threats, identification of common trends and differences in regulatory approaches, determination of directions for improving the legal framework and practical measures in combating cybercrime.Methods. Analysis of the legal acts of the EAEU member states on cybersecurity and cybercrime, comparative analysis to identify common trends and differences, evaluation of the effectiveness of legislation in countering cyber threats, identification of problems, and proposal of directions for improving the legal framework considering modern challenges in cybersecurity.Results. Expected improvement in information exchange and experience sharing, development of common strategies and standards in cybersecurity, reduction in the number of successful cyberattacks through joint efforts, and enhancement of the security level of information systems and data through joint technical and organizational measures.Conclusions. Cooperation among the EAEU member states in cybersecurity can lead to a reduction in the number of cybercrimes, enhancement of the security level of information systems and data, stimulation of innovative development in the digital sphere, and strengthening trust in electronic services and the development of the digital economy in the region, which are key aspects in the modern information society.

Impact of AI on cybersecurity and security compliance

Artificial Intelligence (AI) is transforming the field of cybersecurity and reshaping security compliance practices. As cyber threats become increasingly complex, AI offers powerful tools for identifying, mitigating, and preventing attacks in real-time. AI-driven systems excel at processing vast amounts of data, detecting anomalies, and identifying patterns that traditional security systems might miss. Machine learning and deep learning algorithms enhance the ability to predict potential threats, reducing response time and improving the accuracy of threat detection. These capabilities make AI a valuable asset in addressing sophisticated threats such as zero-day vulnerabilities, advanced persistent threats (APTs), and ransomware attacks. In the realm of security compliance, AI plays a pivotal role by automating routine tasks such as monitoring, auditing, and reporting. This reduces the burden on organizations to manually enforce regulatory standards, leading to more efficient compliance with frameworks like GDPR, HIPAA, and PCI-DSS. AI can continuously assess systems to ensure that they meet compliance requirements, enhancing the ability to detect and respond to violations. Furthermore, AI contributes to governance by helping organizations develop robust security policies, track compliance metrics, and streamline incident response. However, the integration of AI in cybersecurity also presents challenges, including adversarial AI, data privacy concerns, and transparency in AI decision-making processes. Additionally, AI-driven attacks are an emerging threat that necessitates further research and regulation. Despite these challenges, the future of AI in cybersecurity and compliance looks promising, with advancements in predictive analytics, quantum computing, and autonomous security systems poised to further revolutionize the field. As AI technologies evolve, they will continue to play a critical role in fortifying cybersecurity defenses and ensuring regulatory compliance across industries.

The Copenhagen School’s widening security theory in relation to cybersecurity. Applicability and Implications

This paper critically examines the relationship between cybersecurity and Copenhagen School’s widening security theory, with the aim of assessing the possibility of applying this theoretical framework to the cyber realm. As cybersecurity is a relatively recent addition to the security discourse, this research explores whether the Copenhagen School’s traditional framework, which was primarily focused on securitisation processes in the five main sectors (military, political, economic, environmental, and societal), adequately encompasses the unique aspects and dynamics of cyber domain. The research question is: Why the cybersecurity field can’t become a separate constructivist sector? The study begins with a detailed overview of both cybersecurity landscape and Copenhagen School’s fundamental principles to provide the context for a comparative analysis of the Copenhagen theory and contemporary cybersecurity literature. In doing so, the research delves into the features of cybersecurity, with a focus on the evolving nature of cyber threats and vulnerabilities. By performing a qualitative analysis of the existing literature, the paper assesses the limitations of integrating these two concepts, ultimately concluding that cybersecurity lacks the distinct characteristics required to be considered a separate sector under the Copenhagen School’s framework. The findings contribute to the ongoing discussions regarding the adaptation of traditional security theories to address contemporary security challenges in the digital age.

Studi Literatur: Strategi Pengamanan Siber dalam Menghadapi Cyber Crime

In the current digital era, cybersecurity of data and information systems. The threat of attacks, or , is a form of threat that continues to grow along with advances in technology. This research aims to learn about cybersecurity strategies in dealing with . The method used in this research is a literature review. This journal was prepared based on an analysis of research results from various journals accessed through secondary data sources, namely the Google Scholar database, and e-resources from Springer, Cambridge, and Taylor and Francis. The research results show that implementing technology such as encryption, multifactor authentication, and user training, are some of the effective steps that have been proven to reduce risk. However, challenges in implementing this strategy remain, both from a technical and non-technical perspective, which require special attention from researchers and practitioners in the field of cyber security.

To the problem of defining and distinguishing the definitions of "information security" and "cyber security"

The article is devoted to the problem of definition and comparison of definitions, in particular in the context of information and cyber security. The main emphasis is placed on the analysis of existing scientific approaches to the definition of these concepts, with the aim of identifying common and distinctive features between them. Different interpretations of terms in domestic and international scientific literature are studied, paying attention to their development in connection with modern challenges in the information space. Taking into account the different approaches to the definition of the terms «information security» and «cyber security» available both in science and in practice, this study compares domestic and international definitions, analyzes legislative and scientific literature, taking into account the impact of new technologies on the formation of modern definitions in the field information security. The main attention is paid to methodological approaches to the construction of definitions, their accuracy, comprehensibility and applicability in different contexts. Emphasis is placed on the need to agree on a terminology that would adequately reflect the modern challenges and risks that the state faces in the field of information and cyber security. The importance of a clear demarcation of concepts for effective legal regulation and practical application is also emphasized, emphasis is placed on the need to unify terminology and the development of standards for the construction of clear and unambiguous definitions, attention is drawn to the importance of an interdisciplinary approach in the process of creating definitions, which allows taking into account the specifics of various scientific areas. The novelty of the work lies in a comprehensive approach to the analysis of definitions, which makes it possible not only to identify key differences and similarities between different interpretations, but also to offer recommendations for their improvement. Having analyzed examples from various fields, demonstrating how ambiguous or incorrect definitions can lead to misunderstandings and errors in scientific research and practical application, the author offers his own methodology for assessing the quality of these definitions, which can be used to improve scientific communication and increase the accuracy of terminology, in the author’s vision of the definitions of «information security» and «cyber security» is published for the first time in the article, taking into account the security component and interdisciplinary nature. This makes the article an important contribution to the scientific debate on the development of terminology in the field of information and cyber security. The main aspects of the article are the analysis of different approaches to the definition of information and cyber security, the comparison of definitions by national and international researchers, the proposal of new, integrated approaches to understanding these concepts, and the discussion of practical aspects of the application of these definitions in the field of national security.

Kashif: A Chrome Extension for Classifying Arabic Content on Web Pages Using Machine Learning

Search engines are significant tools for finding and retrieving information. Every day, many new web pages in various languages are added. The threats of cyberattacks are expanding rapidly with this massive volume of data. The majority of studies on the detection of malicious websites focus on English-language websites. This necessitates more studies on malicious detection on Arabic-content websites. In this research, we aimed to investigate the security of Arabic-content websites by developing a detection tool that analyzes Arabic content based on artificial intelligence (AI) techniques. We contributed to the field of cybersecurity and AI by building a new dataset of 4048 Arabic-content websites. We created and conducted a comparative performance evaluation for four different machine-learning (ML) models using feature extraction and selection techniques: extreme gradient boosting, support vector machines, decision trees, and random forests. The best-performing model was then integrated into a Chrome plugin, created based on a random forest (RF) model, and utilized the features selected via the chi-square technique. This produced plugin tool attained an accuracy of 92.96% for classifying Arabic-content websites as phishing, suspicious, or benign. To our knowledge, this is the first tool designed specifically for Arabic-content websites.

Cyber Security Awareness Model Based on NIST (National Institute of Standards and Technology) for Secondary School Students in Malaysia

As cybersecurity issues surge, it highlights the pressing need for increased awareness. In our digitally interconnected world, where information technology underpins the lives of individuals, businesses, and even national security, the threat of cyberattacks looms large. To enhance national cybersecurity preparedness, there is a critical call to action: we must promote public awareness of cybersecurity issues and bolster the ranks of trained cybersecurity professionals. This research introduces a novel cybersecurity awareness model based on the National Institute of Standards and Technology (NIST). Its primary goal is to make NIST accessible to secondary school students pursuing ICT courses and their invaluable resource, the school counselling units. The model aims to instil cybersecurity awareness in secondary school students and guide them in choosing the most suitable higher education paths, particularly in the ICT and cybersecurity fields. The study comprises three phases. In the first phase, a systematic literature review (SLR) examines globally applicable cybersecurity education models. The second phase investigates the implementation of cybersecurity education for secondary school students in Malaysia. This includes interviews with school counselling units responsible for guiding students toward higher education and surveys of students in grades four and five enrolled in ICT courses. The third and pivotal phase focuses on developing the proposed model. It involves analysing and implementing the results of the SLR, mapping them to the NIST framework, and incorporating themes from interviews and surveys. As a tangible representation of the model, a mobile application prototype has been developed. The Cybersecurity Awareness Model empowers school counselling units to seamlessly integrate career exploration with career decision-making for secondary students at any stage of their development. Moreover, it offers a means to create strategies for after-school programs, particularly in the dynamic field of cybersecurity. This model is expected to guide students in exploring academic majors and careers, preparing for higher education post-high school, initiating internships or job searches, and adapting to a world in constant flux. In doing so, it not only empowers individuals but also strengthens the nation's resilience in the face of cybersecurity challenges.

Planning and Preparation of Cybersecurity

In today's digital environment characterized by fast-evolving adversaries, expanded attack surfaces, and complex IT environments, having a formidable plan and being prepared could create a world of difference when faced with a potential cyber-attack. Cyber threats are growing risks to everyone and any institution. Cybercriminals target individuals and companies of all sizes. Today's attacks are agile and sophisticated. Ransom demands are elevating and advancing rapidly while attackers drill down their focus to specific victims like critical infrastructure businesses, which can spend millions of dollars in losses from cyber disasters since such companies have less tolerance for downtime. The evolution of artificial intelligence technology, which is more interesting and scarier at the same time, is impacting the field of cybersecurity. The negative side of AI is that it has enabled threat actors to blend new data theft-based extortion techniques into ransomware. Attackers can steal companies' sensitive data or even encrypt the data and demand ransom for the decryption key failure, to which they threaten the company to expose the data to the public or even trade in the dark web. The intelligent techniques and tactics utilized by threat actors are a clarion call for organizations to proactively plan and prepare adequately by developing a cybersecurity strategy as a countermeasure. A cybersecurity strategy is a comprehensive plan that stipulates an organizational approach to secure its IT infrastructure against cyber threats. The common cybersecurity strategy various organizations use to plan and prepare for inevitable attacks is the incidence response plan. Incidence response is the processes and technologies organizations use to detect and respond to cyber threats, security breaches, or cyberattacks within an organization. Developing and implementing a formal incident response plan allows organizations to minimize and prevent damage. This research paper focuses on the primary goal of the incident response of preventing cyberattacks prior to occurring and reducing the cost and business disruption emanating from cyberattacks that happen. Keywords – incident response, threats, Artificial intelligence, cybersecurity, threat attacks, plan, preparation.

A privacy-preserving Self-Supervised Learning-based intrusion detection system for 5G-V2X networks

In light of the ongoing transformation in the automotive industry, driven by the adoption of 5G and the proliferation of connected vehicles, network security has emerged as a critical concern. This is particularly true for the implementation of cutting-edge 5G services such as Network Slicing (NS), Software Defined Networking (SDN), and Multi-access Edge Computing (MEC). As these advanced services become more prevalent, they introduce new vulnerabilities that can be exploited by cyber attackers. Consequently, Network Intrusion Detection Systems (NIDSs) are pivotal in safeguarding vehicular networks against cyber threats. Still, their efficacy hinges on extensive data, which often contains sensitive and confidential information such as vehicle positions and owner’s behaviors, raising privacy concerns. To address this issue, we propose a Privacy-Preserving Self-Supervised Learning (SSL) based Intrusion Detection System for 5G-V2X networks. The majority of works in the literature relying on Federated Learning (FL) and often overlook data labeling on the end devices. Our methodology leverages SSL to pre-train NIDSs using unlabeled data. Post-training is then performed with a minimal amount of labeled data, which can be carefully crafted by an expert. This novel technique allows the training of NIDSs with huge datasets without compromising privacy, consequently enhancing the efficacy of cyber-attack protection. Our innovative SSL pre-training methodology has yielded remarkable results, demonstrating a substantial improvement of up to 9% in accuracy across a diverse range of training dataset sizes, including scenarios with as few as 200 data samples. Our approach highlights the potential to enhance automotive network security significantly, showcasing groundbreaking achievements that set a new standard in the field of automotive cybersecurity.

Information protection and cyber security in the public and financial sectors

The public sector faces increasing challenges in the field of cyber security and information protection, stimulated by the rapid development of digital technologies. A significant increase in the need for reliable protection of state data and information systems requires state institutions to implement complex information protection measures. The purpose of the study is to analyze modern approaches and technologies in the field of cyber security and the use of encryption, cryptography, and the use of cloud technologies that can be used in the public sector for effective information protection. The research methodology involves consideration of strategies that allow to increase the level of security and ensure the protection of critical infrastructure. The results of the study emphasize the need to adapt to the rapidly changing digital environment and involve modern technological solutions to create a reliable information network system in the public sector. The findings of the study draw attention to the importance of global cooperation and increased investment in the field of countering digital attacks, demonstrating examples of effective international cooperation and support in this direction. The need for the integration of international standards and practices in order to create a global digital space has been revealed. A promising area of research is the analysis of the impact of the latest technologies on data storage strategies, which contributes to the identification of ways for further innovations in the industry and the creation of flexible information network systems capable of effectively responding to changing conditions and challenges of modern cyberspace.

Взгляды военно-политического руководства США на защиту критически важных объектов от угроз кибернетического воздействия

The study examines the potential threats of cybernetic impact on critical U.S. facilities that support the activities of the armed forces, and the measures of the American administration to identify and neutralize them. Based on the results of the analysis, it was concluded that the destructive impact on critical infrastructure undermines the military potential of the state and the combat capability of the armed forces. The multidimensional and cross-border nature of threats objectively necessitates the search for ways to prevent and eliminate such threats, as well as the development of interdepartmental and international cooperation in combating cyber threats. This requirement places a burden on state and military leaders to possess knowledge not only in the realm of military security but also in the field of national cybersecurity. Significant cross-industry risks are associated with energy infrastructure, particularly with the supply of gas to electricity producers. The most vulnerable parts of infrastructure are the energy systems that are utilized to supply electricity to military bases and other U.S. military facilities located abroad. A crucial aspect of ensuring the cyber security of energy infrastructure is the regulation of the supply chain for information technology products. Awareness of vulnerabilities within the cybersecurity framework of the U.S. defense infrastructure can be leveraged to refine the list of critical targets of a potential adversary and inform the planning and execution of information operations by the General Staff of the Armed Forces of the Russian Federation.

Artificial Intelligence in Cybersecurity Threat Detection

With the increasing frequency and complexity of cyberattacks, traditional cybersecurity threat detection methods have been difficult to cope with new types of threats. Artificial Intelligence (AI) technology, with its powerful data processing and pattern recognition capabilities, has gradually become an important tool for enhancing cyber security. This paper aims to explore the application of AI in cybersecurity threat detection, firstly outlining the current status of the development of AI technology in cybersecurity, and then focusing on analyzing the application of core methods such as machine learning and deep learning in threat detection, and discussing the advantages of integrated learning and multimodal methods. Finally, this paper summarizes the current challenges faced by AI technology in the field of cyber security and looks forward to the future development direction. Through the research in this paper, it is expected to provide reference for improving the accuracy and efficiency of cybersecurity threat detection.

Incident Response and Threat Intelligence in Healthcare: A Study on Cybersecurity Incident Management

India and other Asian nations are seeing an unparalleled pace of advancement in the modernisation of their healthcare systems. In this endeavour, information technology is crucial. Though the healthcare industry has made great progress, information security is still lagging behind the protection requirements attained in technologically developed nations such as the United States and the United Kingdom. This study is an honest attempt to pinpoint vulnerabilities and dangers in the field of cybersecurity and offers a few targeted remedies in three main domains: risks, vulnerabilities, and IoMT. Using a qualitative research methodology, this research culminates in the creation of a security maturity model for Indian healthcare. Furthermore, in light of these attacks, the well-known National Institute of Standards and Technology (NIST) risk assessment system and its guiding principles are examined. Evaluating the risks intrinsic to these hacks analytically becomes crucial given the comparatively low information risk management maturity levels in Asian healthcare organisations. While several nations in Asia and throughout the world are battling the COVID-19 outbreak, cybercriminals have been attempting to spread misinformation about vaccines. Additionally, some people and organisations are attempting to undermine specific vaccines in order to promote their COVID-19 treatments. Hacking research data, virus testing, and clinical studies that reveal side effects or possible issues are of particular interest to profit-seeking businesses. The secure methodology for identifying network attacks is suggested by this study.

Source inference for misinformation spreading on hypergraphs

Source inference aims at revealing the seed of the misinformation spreading on social networks, and attracted great attention in the field of network science and cybersecurity. Extensive real-world data analyses have certificated that individual interactions exist pairwise and higher-order interactions, and thus should be described using the hypergraph. Previous studies about the source inference algorithms are mainly focused on simple graphs (i.e., a graph only has pairwise interactions) while neglecting the higher-order interactions. In this article, we propose a dynamical message-passing (DMP) algorithm to infer the misinformation spreading on hypergraphs. As a comparison, we also extend jordan centrality (JC), betweenness centrality (BC), closeness centrality (CC), and eigenvector centrality (EIG) algorithm to hypergraphs. The results show that our proposed DMP algorithm can accurately and effectively infer the propagation source on both artificial and real-world hypergraphs compared to the centrality algorithms. Even if the increase in propagation scale renders the other centrality algorithms completely ineffective, the DMP algorithm can still distinguish the real propagation sources in the form of a rank smaller than 10% of the network size. In general, our DMP algorithm provides an effective solution for inferring the misinformation source on high-order networks.

Recognition of Malicious URLs Using Machine Learning

The surge in online activities has heightened the risk of cyber-attacks, with phishing emerging as a notably widespread threat. Conventional phishing detection methods, such as blacklists and heuristic analysis, fall short when it comes to identifying novel and complex phishing schemes. To tackle this issue, we propose a machine learning-based solution for detecting phishing websites. Our model utilizes a combination of features—such as URL structure, domain attributes, and content characteristics—to categorize websites as either phishing or legitimate. The model's outstanding overall accuracy of 96.9% underscores the need to evaluate both precision and recall when determining the effectiveness of machine learning models. The results of this study are significant for developing robust URL classification models and advancing the field of cybersecurity. Future research could aim to improve the model's performance by integrating more features, developing real-time phishing detection systems, and exploring new attributes that can be derived from URLs. Keywords: Phishing, URL, Machine Learning, Classification, Detection

CICIoMT2024: A benchmark dataset for multi-protocol security assessment in IoMT

The Internet of Things (IoT) is increasingly integrated into daily life, particularly in healthcare, through the Internet of Medical Things (IoMT). IoMT devices support services like continuous health monitoring but raise significant cybersecurity concerns due to their vulnerability to various attacks. The complexity and data volume of IoMT network traffic requires advanced methods to enhance security and reliability. Machine Learning (ML) offers techniques to detect, prevent, and mitigate cyberattacks. However, existing benchmark datasets lack essential features for robust IoMT security solutions, such as a reduced number of real devices, a limited variety of attacks, and a lack of extensive profiling. We propose a realistic benchmark dataset for IoMT security solutions development and evaluation to address these gaps. We executed 18 attacks on an IoMT testbed with 40 devices (25 real and 15 simulated), using protocols like Wi-Fi, MQTT, and Bluetooth. Supporting technologies, including dedicated network traffic collectors and a Faraday Cage, ensured data quality. The attacks fall into five categories: DDoS, DoS, Recon, MQTT, and spoofing. We aim to establish a baseline that complements existing datasets, aiding researchers in creating secure healthcare systems using ML. Beyond simulating attacks, we capture the lifecycle of IoMT devices from network entry to exit through profiling, allowing classifiers to identify device anomalies. The resulting CICIoMT2024 dataset, published on the CIC dataset page, demonstrates that various methods can classify IoMT cyberattacks. This effort supports new IoMT security solutions and contributes to the broader field of cybersecurity in healthcare, ensuring more reliable IoMT device deployment.

Dimensionality reduction using neural networks for lattice-based cryptographic keys

Post Quantum Cryptography has received an increasing amount of active research. This has been made prominent by the ever-growing field of quantum computing which poses a formidable threat to the modern cybersecurity landscape. Recently, post quantum schemes have been standardized for adoption into existing security services and protocols. In comparison to contemporary cryptographic schemes, these approaches are lagging behind in terms of performance with regards to functional speed and package sizes. A study into the various applications of neural networks used in classical and post quantum cryptographic schemes has been explored to demonstrate their different possible applications within the various fields of cybersecurity. The main contribution of this work is to investigate the feasibility of dimensionality reduction using the autoencoder neural network on lattice-based keys generated by the Kyber key encapsulation mechanism scheme. Moreover, this work also presents a comparative analysis of the different implemented autoencoder models to showcase their relative performance.

BVTED: A Specialized Bilingual (Chinese–English) Dataset for Vulnerability Triple Extraction Tasks

Extracting knowledge from cyber threat intelligence is essential for understanding cyber threats and implementing proactive defense measures. However, there is a lack of open datasets in the Chinese cybersecurity field that support both entity and relation extraction tasks. This paper addresses this gap by analyzing vulnerability description texts, which are standardized and knowledge-dense, to create a vulnerability knowledge ontology comprising 13 entities and 15 relations. We annotated 27,311 unique vulnerability description sentences from the China National Vulnerability Database, resulting in a dataset named BVTED for cybersecurity knowledge triple extraction tasks. BVTED contains 97,391 entities and 69,614 relations, with entities expressed in a mix of Chinese and English. To evaluate the dataset’s value, we trained five deep learning-based named entity recognition models, two relation extraction models, and two joint entity–relation extraction models on BVTED. Experimental results demonstrate that models trained on this dataset achieve excellent performance in vulnerability knowledge extraction tasks. This work enhances the extraction of cybersecurity knowledge triples from mixed Chinese and English threat intelligence corpora by providing a comprehensive ontology and a new dataset, significantly aiding in the mining, analysis and utilization of the knowledge embedded in cyber threat intelligence.