DESIGN OF ADVANCEMENTS IN AI FOR CYBER THREAT DETECTION
Abstract
The field of artificial intelligence (AI) has the potential to fundamentally change how society uses technology, particularly in terms of how personal information is connected and how hackers can access people's private lives. Future AI software systems are anticipated to be customized by profit-driven criminals to their operations, further complicating ongoing cybercrime investigations. This thesis examines the potential use of developing AI technology by cybercriminals and multinational criminal organizations to carry out ever-more complicated criminal activities, as well as the preparations that the homeland security sector should make. Using a future scenario methodology, four scenarios were developed to forecast how hackers would take advantage of AI systems and what steps should be taken immediately to protect the US from dangerous AI use. Automotive technology has advanced in many ways. These developments have led to the development of a complex automotive technology ecosystem and have brought autonomous cars closer to commercialization. Like any other technology, these developments have benefits but also provide a number of risks. One of these issues in the automotive sector is cybersecurity risks. These security flaws in cars have the potential to cause fatalities as well as enormous costs and disastrous outcomes. Therefore, some of the cybersecurity problems in the automotive ecosystem are resolved by carrying out a comprehensive threat analysis, assessment, and detection. This dissertation will accomplish this by developing a three-step framework for assessing, identifying, and evaluating risks using machine learning techniques.
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