Deep Learning Approaches to Cybersecurity: Detecting and Mitigating Threats with AI
Abstract
As cyber threats continue to evolve in sophistication and frequency, the need for robust and adaptive cybersecurity measures becomes increasingly imperative. Deep learning, a subset of artificial intelligence (AI), has emerged as a promising approach for enhancing cybersecurity by enabling automated detection and mitigation of various threats. This paper explores the application of deep learning techniques in cybersecurity, focusing on its capability to detect and mitigate threats in real-time. We examine various deep learning models and architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), and their effectiveness in identifying malicious activities such as malware, phishing attacks, and anomalous behavior. Additionally, we discuss the challenges and limitations associated with deploying deep learning approaches in cybersecurity, such as dataset scarcity, model interpretability, and adversarial attacks. By providing a comprehensive overview of deep learning techniques in cybersecurity, this paper aims to contribute to the advancement of AI-driven solutions for safeguarding digital assets and infrastructure against cyber threats.
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