From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy

Iqbal H. Sarker

10 Januari 2023

Due to the rising dependency on digital technology, cybersecurity has emerged as a more prominent field of research and application that typically focuses on securing devices, networks, systems, data and other resources from various cyber‐attacks, threats, risks, damages, or unauthorized access. Artificial intelligence (AI), also referred to as a crucial technology of the current Fourth Industrial Revolution (Industry 4.0 or 4IR), could be the key to intelligently dealing with these cyber issues. Various forms of AI methodologies, such as analytical, functional, interactive, textual as well as visual AI can be employed to get the desired cyber solutions according to their computational capabilities. However, the dynamic nature and complexity of real‐world situations and data gathered from various cyber sources make it challenging nowadays to build an effective AI‐based security model. Moreover, defending robustly against adversarial attacks is still an open question in the area. In this article, we provide a comprehensive view on “Cybersecurity Intelligence and Robustness,” emphasizing multi‐aspects AI‐based modeling and adversarial learning that could lead to addressing diverse issues in various cyber applications areas such as detecting malware or intrusions, zero‐day attacks, phishing, data breach, cyberbullying and other cybercrimes. Thus the eventual security modeling process could be automated, intelligent, and robust compared to traditional security systems. We also emphasize and draw attention to the future aspects of cybersecurity intelligence and robustness along with the research direction within the context of our study. Overall, our goal is not only to explore AI‐based modeling and pertinent methodologies but also to focus on the resulting model's applicability for securing our digital systems and society.

Bilel Cherif Tamás Bisztray A. Battah + 5 lainnya

21 Mei 2024

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.

R. Sangwan Y. Badr S. Srinivasan

4 Mei 2023

Recent advances in machine learning have created an opportunity to embed artificial intelligence in software-intensive systems. These artificial intelligence systems, however, come with a new set of vulnerabilities making them potential targets for cyberattacks. This research examines the landscape of these cyber attacks and organizes them into a taxonomy. It further explores potential defense mechanisms to counter such attacks and the use of these mechanisms early during the development life cycle to enhance the safety and security of artificial intelligence systems.

Yeali S. Sun Zhi-Kang Chen Yi-Ting Huang + 1 lainnya

1 Mei 2024

Dissecting low-level malware behaviors into human-readable reports, such as cyber threat intelligence, is time-consuming and requires expertise in systems and cybersecurity. This work combines dynamic analysis and artificial intelligence-generative transformation for malware report generation, providing detailed technical insights and articulating malware intentions.

Mahmoud Abdelsalam Maanak Gupta Sudip Mittal

21 September 2020

The use of Artificial Intelligence (AI) and Machine Learning (ML) to solve cybersecurity problems has been gaining traction within industry and academia, in part as a response to widespread malware attacks on critical systems, such as cloud infrastructures, government offices or hospitals, and the vast amounts of data they generate. AI- and ML-assisted cybersecurity offers data-driven automation that could enable security systems to identify and respond to cyber threats in real time. However, there is currently a shortfall of professionals trained in AI and ML for cybersecurity. Here we address the shortfall by developing lab-intensive modules that enable undergraduate and graduate students to gain fundamental and advanced knowledge in applying AI and ML techniques to real-world datasets to learn about Cyber Threat Intelligence (CTI), malware analysis, and classification, among other important topics in cybersecurity. Here we describe six self-contained and adaptive modules in "AI-assisted Malware Analysis." Topics include: (1) CTI and malware attack stages, (2) malware knowledge representation and CTI sharing, (3) malware data collection and feature identification, (4) AI-assisted malware detection, (5) malware classification and attribution, and (6) advanced malware research topics and case studies such as adversarial learning and Advanced Persistent Threat (APT) detection.