Due to the ever-increasing threat of cyber-attacks to critical cyber infrastructure, organizations are focusing on building their cybersecurity knowledge base. A salient list of cybersecurity knowledge is the Common Vulnerabilities and Exposures (CVE) list, which details vulnerabilities found in a wide range of software and hardware. However, these vulnerabilities often do not have a mitigation strategy to prevent an attacker from exploiting them. A well-known cybersecurity risk management framework, MITRE ATT&CK, offers mitigation techniques for many malicious tactics. Despite the tremendous benefits that both CVEs and the ATT&CK framework can provide for key cybersecurity stakeholders (e.g., analysts, educators, and managers), the two entities are currently separate. We propose a model, named the CVE Transformer (CVET), to label CVEs with one of ten MITRE ATT&CK tactics. The CVET model contains a fine-tuning and self-knowledge distillation design applied to the state-of-the-art pre-trained language model RoBERTa. Empirical results on a gold-standard dataset suggest that our proposed novelties can increase model performance in F1-score. The results of this research can allow cybersecurity stakeholders to add preliminary MITRE ATT&CK information to their collected CVEs.
Decoys Cannot Go Everywhere: Mapping the Deception Surface in MITRE ATT&CK
Faculty of Electrical Engineering, Czech Technical University in Prague（布拉格捷克理工大学电气工程学院）；School of Engineering, Universidad Nacional de Cuyo（门多萨国立大学工程学院）；Cyber Threat Intelligence Lab, Delft University of Technology（代尔夫特理工大学网络威胁情报实验室）
CyberLLM-FINDS 2025: Instruction-Tuned Fine-tuning of Domain-Specific LLMs with Retrieval-Augmented Generation and Graph Integration for MITRE Evaluation
Grambling State University（格兰布林州立大学）；Florida International University（佛罗里达国际大学）
Context-Aware Web Attack Detection in Open-Source SIEM Systems via MITRE ATT&CK-Enriched Behavioral Profiling
Higher Institute for Applied Sciences and Technology（应用科学与技术高级学院）；Syrian Private University（叙利亚私立大学）；Arab International University（阿拉伯国际大学）；Latakia University（拉塔基亚大学）
CVE-TTP KG: Knowledge Graph Linking Software Vulnerabilities to Attack Behaviors
Central University of Rajasthan（拉贾斯坦中央大学）；Christ College of Engineering (Autonomous)（基督工程学院（自治））；Swami Keshvanand Institute of Technology, Management & Gramothan（斯瓦米·凯什瓦南德技术、管理与格拉莫坦学院）；University of Pavia（帕维亚大学）；Cochin University of Science and Technology（科钦科技大学）
AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports
Bosch Center for Artificial Intelligence（博世人工智能中心）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Hochschule der Medien（斯图加特传媒大学）；Robert Bosch GmbH（罗伯特·博世有限公司）；Universität Augsburg（奥格斯堡大学）
ISADM: An Integrated STRIDE, ATT&CK, and D3FEND Model for Threat Modeling Against Real-world Adversaries
University of New South Wales (UNSW)（新南威尔士大学）；Bangladesh University of Business and Technology (BUBT)（孟加拉商业与技术大学）；Anchoram’s Cyber Security Practice（Anchoram网络安全实践）；RMIT University（皇家墨尔本理工大学）
LLM-HyPZ: Hardware Vulnerability Discovery using an LLM-Assisted Hybrid Platform for Zero-Shot Knowledge Extraction and Refinement
Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, USA（电气与计算机工程系，亚利桑那大学）；Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ, USA（系统与工业工程系，亚利桑那大学）；College of Information Science, University of Arizona, Tucson, AZ, USA（信息科学学院，亚利桑那大学）；School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND, USA（电气工程与计算机科学学院，北达科他大学）；Department of Computer Science, University of California Davis, CA, USA（计算机科学系，加州大学戴维斯分校）
Mitigating Deep Learning Vulnerabilities from Adversarial Examples Attack in the Cybersecurity Domain
Institute of Electrical and Electronic Engineers（电气与电子工程师协会）
已知深度学习模型通过在具有最佳准确度的大型数据集上采用多个历元和训练样本来解决分类和回归问题。然而，这并不意味着它们是防攻击的或不暴露于漏洞。新部署的系统，特别是在公共环境（即公共网络）上的系统，容易受到来自各种实体的攻击。此外，已发表的关于深度学习系统的研究（Goodfellow et al.，2014）已经确定了大量的攻击点和广泛的攻击面，这些攻击面有证据表明存在来自对抗性示例的利用。成功利用这些系统可能会导致严重的现实世界的影响。例如，（1）对运行深度强化学习系统的自动驾驶汽车的对抗攻击会对人类产生直接的错误分类，从而导致不幸的事故。(2)自动驾驶车辆误读红灯信号可能导致汽车撞上另一辆汽车（3）将行人车道误分类为交叉路口车道，可能导致车祸。这只是冰山一角，计算机视觉的部署并不完全集中在自动驾驶汽车上，还包括许多其他领域，这将对现实世界产生决定性的影响。必须在发展的早期阶段减轻这些脆弱性。在实际部署之前，必须在全球范围内制定和实施基线安全标准。
Chatbots to ChatGPT in a Cybersecurity Space: Evolution, Vulnerabilities, Attacks, Challenges, and Future Recommendations
University of Science and Technology Beijing（北京科技大学）；Xinjiang Institute of Engineering（新疆工程学院）；Blekinge Institute of Technology（布莱金厄理工学院）；Stevens Institute of Technology（史蒂文斯理工学院）
