The Emerging Threat of Ai-driven Cyber Attacks: A Review

Cyberattacks are becoming more sophisticated and ubiquitous. Cybercriminals are inevitably adopting Artificial Intelligence (AI) techniques to evade the cyberspace and cause greater damages without being noticed. Researchers in cybersecurity domain have not researched the concept behind AI-powered cyberattacks enough to understand the level of sophistication this type of attack possesses. This paper aims to investigate the emerging threat of AI-powered cyberattacks and provide insights into malicious used of AI in cyberattacks. The study was performed through a three-step process by selecting only articles based on quality, exclusion, and inclusion criteria that focus on AI-driven cyberattacks. Searches in ACM, arXiv Blackhat, Scopus, Springer, MDPI, IEEE Xplore and other sources were executed to retrieve relevant articles. Out of the 936 papers that met our search criteria, a total of 46 articles were finally selected for this study. The result shows that 56% of the AI-Driven cyberattack technique identified was demonstrated in the access and penetration phase, 12% was demonstrated in exploitation, and command and control phase, respectively; 11% was demonstrated in the reconnaissance phase; 9% was demonstrated in the delivery phase of the cybersecurity kill chain. The findings in this study shows that existing cyber defence infrastructures will become inadequate to address the increasing speed, and complex decision logic of AI-driven attacks. Hence, organizations need to invest in AI cybersecurity infrastructures to combat these emerging threats

Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

Digital Deception: Generative Artificial Intelligence in Social Engineering and Phishing

The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has profound implications for both the utility and security of our digital interactions. This paper investigates the transformative role of Generative AI in Social Engineering (SE) attacks. We conduct a systematic review of social engineering and AI capabilities and use a theory of social engineering to identify three pillars where Generative AI amplifies the impact of SE attacks: Realistic Content Creation, Advanced Targeting and Personalization, and Automated Attack Infrastructure. We integrate these elements into a conceptual model designed to investigate the complex nature of AI-driven SE attacks - the Generative AI Social Engineering Framework. We further explore human implications and potential countermeasures to mitigate these risks. Our study aims to foster a deeper understanding of the risks, human implications, and countermeasures associated with this emerging paradigm, thereby contributing to a more secure and trustworthy human-computer interaction.Comment: Submitted to CHI 202

The Emerging Threat of Ai-driven Cyber Attacks: A Review

Cyberattacks are becoming more sophisticated and ubiquitous. Cybercriminals are inevitably adopting Artificial Intelligence (AI) techniques to evade the cyberspace and cause greater damages without being noticed. Researchers in cybersecurity domain have not researched the concept behind AI-powered cyberattacks enough to understand the level of sophistication this type of attack possesses. This paper aims to investigate the emerging threat of AI-powered cyberattacks and provide insights into malicious used of AI in cyberattacks. The study was performed through a three-step process by selecting only articles based on quality, exclusion, and inclusion criteria that focus on AI-driven cyberattacks. Searches in ACM, arXiv Blackhat, Scopus, Springer, MDPI, IEEE Xplore and other sources were executed to retrieve relevant articles. Out of the 936 papers that met our search criteria, a total of 46 articles were finally selected for this study. The result shows that 56% of the AI-Driven cyberattack technique identified was demonstrated in the access and penetration phase, 12% was demonstrated in exploitation, and command and control phase, respectively; 11% was demonstrated in the reconnaissance phase; 9% was demonstrated in the delivery phase of the cybersecurity kill chain. The findings in this study shows that existing cyber defence infrastructures will become inadequate to address the increasing speed, and complex decision logic of AI-driven attacks. Hence, organizations need to invest in AI cybersecurity infrastructures to combat these emerging threats.publishedVersio

From Convergence to Compromise: Understanding the Interplay of Digital Transformation and Mergers on Data Breach Risks in Local and Cross-Border Mergers

In today\u27s digital age, the potential risks and challenges associated with digital transformation (DT) and cybersecurity have received limited research attention. This dissertation consists of three interconnected studies that aim to address this gap. The first study employs paradox theory to demonstrate that DT initiatives can increase a firm\u27s susceptibility to data breaches. Using a unique dataset spanning 10 years and involving 3604 brands, our analysis reveals that DT efforts in mobile and digital marketing are associated with a higher incidence of data breaches. However, firms can mitigate this impact by enhancing their innovative capacities. These findings contribute to a better understanding of the complex relationship between DT, data breaches, and innovation. Our second investigation, rooted in complexity theory and matching theory, examines the impact of mergers and acquisitions (M&As) on the frequency of data breaches. By analyzing 18 years of data from 5072 US firms, we find that M&As increase the likelihood of data breaches, particularly when the merging firms operate in different business domains. Furthermore, we observe that M&As that receive more media attention are more prone to data breaches, while those involving a more vulnerable target firm have fewer breaches. In our third study, guided by Institutional theory, we explore the relationship between cross-border mergers and acquisitions (CBMA) and data breaches. Our findings indicate that CBMAs, especially those accompanied by significant media publicity and involving firms from divergent institutional contexts, heighten the risk of data breaches. Overall, these studies provide valuable insights for firms aiming to mitigate data breach risks during their digital transformation (DT) efforts and M&A activities. They emphasize the importance of adopting a balanced communication strategy and considering the security implications of strategic actions. Moreover, our findings contribute to the academic discourse in information systems by illuminating the intricate interplay between DT, M&As, and data breaches

Umělá inteligence v kybernetické bezpečnosti

Artifcial intelligence (AI) and machine learning (ML) have grown rapidly in recent years, and their applications in practice can be seen in many felds, ranging from facial recognition to image analysis. Recent developments in Artificial intelligence have a vast transformative potential for both cybersecurity defenders and cybercriminals. Anti-malware solutions adopt intelligent techniques to detect and prevent threats to the digital space. In contrast, cybercriminals are aware of the new prospects too and likely to adapt AI techniques to their operations. This thesis presents advances made so far in the field of applying AI techniques in cybersecurity for combating against cyber threats, to demonstrate how this promising technology can be a useful tool for detection and prevention of cyberattacks. Furthermore, the research examines how transnational criminal organizations and cybercriminals may leverage developing AI technology to conduct more sophisticated criminal activities. Next, the research outlines the possible dynamic new kind of malware, called X-Ware and X-sWarm, which simulates the swarm system behaviour and integrates the neural network to operate more efficiently as a background for the forthcoming anti-malware solution. This research proposes how to record and visualize the behaviour of these type of malware when it propagates through the file system, computer network (virus process is known) or by observed data analysis (virus process is not known and we observe only the data from the system). Finally, a paradigm of an anti-malware solution, named Multi agent antivirus system has been proposed in the thesis that gives the insight to develop a more robust, adaptive and flexible defence system.Význam umělé inteligence (AI) a strojového učení (ML) v posledních letech rychle rostl a na jejich aplikacích lze vidět, že v mnoha oblastech, od rozpoznávání obličeje až po analýzu obrazu, byl učiněn velký pokrok. Poslední vývoj v oblasti umělé inteligence má obrovský potenciál jak pro obránce v oblasti kybernetické bezpečnosti, tak pro ůtočníky. AI se stává řešením v otázce obrany proti modernímu malware a hraje tak důležitou roli v detekci a prevenci hrozeb v digitálním prostoru. Naproti tomu kyberzločinci jsou si vědomi nových vyhlídek ve spojení s AI a pravděpodobně přizpůsobí tyto techniky novým generacím malware, vektorům útoku a celkově jejich operacím. Tato práce představuje dosavadní pokroky aplikace technik AI v oblasti kybernetické bezpečnosti. V této oblasti tzn. v boji proti kybernetickým hrozbám se ukázuje jako slibná technologie a užitečný nástroj pro detekci a prevenci kybernetických útoků. V práci si rovněž pokládme otázku, jak mohou nadnárodní zločinecké organizace a počítačoví zločinci využít vyvíjející se technologii umělé inteligence k provádění sofistikovanějších trestných činností. Konečně, výzkum nastíní možný nový druh malware, nazvaný X-Ware, který simuluje chování hejnového systému a integruje neuronovou síť tak, aby fungovala efektivněji a tak se celý X-Ware a X-sWarm dal použít nejen jako kybernetická zbraň na útok, ale i jako antivirové obranné řešení. Tento výzkum navrhuje, jak zaznamenat a vizualizovat chování X-Ware, když se šíří prostřednictvím systému souborů, sítí a to jak analýzou jeho dynamiky (proces je znám), tak analýzou dat (proces není znám, pozorujeme jen data). Nakonec bylo v disertační práci navrženo paradigma řešení proti malwaru, jež bylo nazváno „Multi agent antivirus system“. Tato práce tedy poskytuje pohled na vývoj robustnějšího, adaptivnějšího a flexibilnějšího obranného systému.460 - Katedra informatikyvyhově