AI-Driven Security Solutions: Combating Cyber Threats with Machine Learning Models

AbstractThis paper describes how artificial intelligence (AI) and machine learning (ML) models can help improve cybersecurity by identifying and preventing different cyber threats. Standard security solutions are usually ineffective, especially with the rising incidences of phishing, malware, and DDoS attacks. AI models are more proactive with the help of better algorithms that first find their way to detect the patterns and threats and then take the necessary action within the least time possible. The purpose of this research is to assess the effectiveness of these models for guarding digital domains and it will also assess the integration of these tools in different fields including the financial sector, health care and e-business. The paper also discusses the methods used in these systems: It has branch or subcategories as supervised learning, unsupervised learning, deep learning, and neural networks. Using case studies and data analysis, the paper defines key advantages of AI solutions, such as faster and more accurate detection and solution scalability. However, there are limitations as well while using the algorithm. The approach is vulnerable to adversarial attacks, is associated with high false positive rates, and requires a large amount of data. Therefore, this work explores the extent and possibilities of how AI and ML are relevant to today’s world insecurity parlance and subsequent advancements that may be seen in future innovations within these fields concerning threat identification and mitigation.