High-quality data scarcity hinders malware detection, limiting ML performance. We introduce MalDataGen, an open-source modular framework for generating high-fidelity synthetic tabular data using modular deep learning models (e.g., WGAN-GP, VQ-V AE). Evaluated via dual validation (TR-TS/TS-TR), seven classifiers, and utility metrics, MalDataGen outperforms benchmarks like SDV while preserving data utility. Its flexible design enables seamless integration into detection pipelines, offering a practical solution for cybersecurity applications. I. Introduction Modern machine learning algorithms, particularly deep learning architectures, depend on large-scale datasets with reliable annotations to achieve optimal performance.

This paper explores the relatively underexplored application of Positive Unlabeled (PU) Learning and Negative Unlabeled (NU) Learning in the cybersecurity domain. While these semi-supervised learning methods have been applied successfully in fields like medicine and marketing, their potential in cybersecurity remains largely untapped. The paper identifies key areas of cybersecurity--such as intrusion detection, vulnerability management, malware detection, and threat intelligence--where PU/NU learning can offer significant improvements, particularly in scenarios with imbalanced or limited labeled data. We provide a detailed problem formulation for each subfield, supported by mathematical reasoning, and highlight the specific challenges and research gaps in scaling these methods to real-time systems, addressing class imbalance, and adapting to evolving threats. Finally, we propose future directions to advance the integration of PU/NU learning in cybersecurity, offering solutions that can better detect, manage, and mitigate emerging cyber threats.

This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs). We explore LLM applications across various domains, including hardware design security, intrusion detection, software engineering, design verification, cyber threat intelligence, malware detection, and phishing detection. We present an overview of LLM evolution and its current state, focusing on advancements in models such as GPT-4, GPT-3.5, Mixtral-8x7B, BERT, Falcon2, and LLaMA. Our analysis extends to LLM vulnerabilities, such as prompt injection, insecure output handling, data poisoning, DDoS attacks, and adversarial instructions. We delve into mitigation strategies to protect these models, providing a comprehensive look at potential attack scenarios and prevention techniques. Furthermore, we evaluate the performance of 42 LLM models in cybersecurity knowledge and hardware security, highlighting their strengths and weaknesses. We thoroughly evaluate cybersecurity datasets for LLM training and testing, covering the lifecycle from data creation to usage and identifying gaps for future research. In addition, we review new strategies for leveraging LLMs, including techniques like Half-Quadratic Quantization (HQQ), Reinforcement Learning with Human Feedback (RLHF), Direct Preference Optimization (DPO), Quantized Low-Rank Adapters (QLoRA), and Retrieval-Augmented Generation (RAG). These insights aim to enhance real-time cybersecurity defenses and improve the sophistication of LLM applications in threat detection and response. Our paper provides a foundational understanding and strategic direction for integrating LLMs into future cybersecurity frameworks, emphasizing innovation and robust model deployment to safeguard against evolving cyber threats.

On 7 July, the CLAIRE (Confederation of Laboratories for Artificial Intelligence Research in Europe) All Questions Answered (AQuA) series continued, with a one-hour session focussing on cybersecurity. A panel of experts covered current European initiatives, how AI can be used to make cybersecurity applications more resilient, and how cybersecurity applications can ensure the safety of AI technologies.

Graph neural networks (GNNs) have been utilized to create multi-layer graph models for a number of cybersecurity applications from fraud detection to software vulnerability analysis. Unfortunately, like traditional neural networks, GNNs also suffer from a lack of transparency, that is, it is challenging to interpret the model predictions. Prior works focused on specific factor explanations for a GNN model. In this work, we have designed and implemented Illuminati, a comprehensive and accurate explanation framework for cybersecurity applications using GNN models. Given a graph and a pre-trained GNN model, Illuminati is able to identify the important nodes, edges, and attributes that are contributing to the prediction while requiring no prior knowledge of GNN models. We evaluate Illuminati in two cybersecurity applications, i.e., code vulnerability detection and smart contract vulnerability detection. The experiments show that Illuminati achieves more accurate explanation results than state-of-the-art methods, specifically, 87.6% of subgraphs identified by Illuminati are able to retain their original prediction, an improvement of 10.3% over others at 77.3%. Furthermore, the explanation of Illuminati can be easily understood by the domain experts, suggesting the significant usefulness for the development of cybersecurity applications.

The annual cost of cybercrime is estimated to rise to $6 trillion by 2021.[1] Artificial intelligence (AI), frequently mentioned for its potential to accelerate innovation, boost performance and improve decision-making, is already being applied to defend against cybercrime. Because AI works well with functions that use massive amounts of data and require analysis and judgment, integrating AI-based cybersecurity technology with other defenses is a natural choice for cybersecurity professionals. Today, AI is used more extensively in cybersecurity than in any other function, with 75% of companies using AI technology to detect and ward off cyberthreats, according to the results of a recent global executive survey on AI conducted by Protiviti. Cybersecurity usage of AI is expected to grow nearly 20% by 2021.[2] AI's significant and compelling benefits come with new risks that need to be managed.