Securing Machine Learning Against Data Poisoning Attacks

Abstract

The emergence of intelligent networks has revolutionized the use of machine learning (ML), allowing it to be applied in various domains of human life. This literature review paper provides in-depth analysis of the existing research on data poisoning attacks and examines how intelligent networks can mitigate these threats. Specifically, the author explores how malicious users inject fake training data into adversarial networks, a technique known as a data poisoning attack, which can severely compromise the model's integrity. Through a comparative evaluation of the attack strategies and defense mechanisms, such as robust optimization and model-based detection, the author assesses the strengths and limitations of current defenses. Real-world applications are discussed, including the use of these networks in cybersecurity, healthcare, and smart city systems. The author concludes by outlining the challenges and future directions in developing more effective defense strategies to detect and mitigate data poisoning attacks in real time, ensuring the security and privacy of intelligent networks.