![]() This has significant implications in cyberconflict, as AI-powered systems increasingly rely on machine learning for different tasks, including malware detection and network security.Ĭyberattacks can have origin in international relations or bring about consequences that could escalate to a political and diplomatic level. ![]() Attackers can use techniques such as data poisoning or adversarial examples to manipulate AI models and trick them into making incorrect predictions or decisions. Adversarial machine learning uses AI to exploit vulnerabilities in the machine learning algorithms themselves. ![]() While AI may help with cybersecurity and defence, it is also be used by bad actors to increase the scope and sophistication of their assaults. AI-based defensive systems can quickly identify and stop attacks, providing organisations with improved protection. Large amounts of network traffic and security records can be analysed by AI systems to spot trends and abnormalities that could be signs of threats or assaults. ![]() AI-powered assaults may be more complex, adaptable, and elusive, making them more difficult to identify and stop.ĪI can support defensive tactics in cyberconflict as well. The generation of malware, vulnerability detection, and other attack phases may be automated and optimised using AI algorithms. One example of the use of AI being used in cyberconflict is a deepfake of Ukrainian President Zelenskyy telling Ukrainian troops to surrender the fight against Russia.ĪI has the potential to improve cyberattackers’ offensive capabilities. Nevertheless, we can presume that AI has been used in cyberconflict operations to some extent given the growing acceptance of AI technologies and their potential for improving both offensive and defensive cyber capabilities. The extent to which AI has been deployed in specific conflicts may not be completely understood because of the covert nature of cyberconflict activities and the sparse public disclosure around them. ![]()
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