Deep Learning for Phishing and Social Engineering Attack Prevention

Abstract

Phishing and social engineering attacks are among the most prevalent and damaging cybersecurity threats today, exploiting human vulnerabilities rather than technical weaknesses in systems. These attacks often trick individuals into disclosing sensitive information or executing harmful actions, leading to significant financial and reputational losses for organizations. Traditional security measures, such as antivirus software and firewalls, are inadequate in preventing phishing and social engineering attacks, as these threats typically bypass technical defenses by targeting users directly. Deep learning, a subset of artificial intelligence, has shown significant promise in improving detection and prevention mechanisms for such attacks. By leveraging large datasets and advanced neural networks, deep learning models can analyze patterns in communication, identify malicious behaviors, and predict phishing attempts with remarkable accuracy. This paper explores the role of deep learning in phishing and social engineering attack prevention, emphasizing its effectiveness in recognizing the subtle, evolving nature of these attacks. Furthermore, the paper discusses various deep learning architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), that can enhance the detection of phishing websites, emails, and social engineering tactics.