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Ali Raza A Khan
Virginia University of Science & Technology

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Deep Learning in Cybersecurity in the Modern Era Ali Raza A Khan; Muhammad Ismaeel Khan; Aftab Arif
JURIHUM : Jurnal Inovasi dan Humaniora Vol. 2 No. 2 (2024): JURIHUM : Jurnal Inovasi dan Humaniora
Publisher : CV. Shofanah Media Berkah

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The integration of deep learning into cybersecurity has marked a transformative shift in the way organizations approach threat detection and mitigation. This review article explores the modern era of deep learning in cybersecurity, detailing its significant advantages over traditional security measures, particularly in enhancing threat detection and response mechanisms. Deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have demonstrated remarkable proficiency in identifying anomalies and adapting to evolving cyber threats, enabling real-time responses that mitigate potential damage. Despite its promise, the implementation of deep learning in cybersecurity faces several challenges, including data privacy concerns, model interpretability issues, adversarial vulnerabilities, and the resource-intensive nature of training these models. The emergence of explainable AI (XAI) aims to enhance trust in automated systems by providing interpretable outputs, while federated learning addresses privacy risks by enabling collaborative training without data centralization. Future directions in this field include advancements in adversarial training techniques, the integration of multi-modal data sources, and the deployment of edge computing for real-time threat detection. As organizations continue to embrace deep learning technologies, they will enhance their ability to navigate the complexities of the digital landscape and strengthen their defenses against a continuously evolving array of cyber threats. Overall, deep learning is set to play a crucial role in reshaping cybersecurity practices, driving innovations that improve security postures and operational efficiencies in the face of rising cyber risks.
Role of AI in Predicting and Mitigating Threats: A Comprehensive Review Aftab Arif; Ali Raza A Khan; Muhammad Ismaeel Khan
JURIHUM : Jurnal Inovasi dan Humaniora Vol. 2 No. 3 (2024): JURIHUM : Jurnal Inovasi dan Humaniora
Publisher : CV. Shofanah Media Berkah

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The field of danger prediction and mitigation is changing due to artificial intelligence (AI) in a number of areas, including national security, cybersecurity, public health, and finance. This paper examines how artificial intelligence (AI) might improve threat detection, response, and prevention. It emphasizes AI's capacity to scan large datasets and spot patterns that speed up decision-making. Anomaly detection in cybersecurity, disease outbreak and natural disaster prediction using predictive modeling, and financial system fraud detection are some of the key uses. The application of AI technologies, however, brings up important ethical issues, such as algorithmic bias, data privacy, responsibility, and the requirement for openness. In order to responsibly manage AI implementation, the essay highlights the significance of ethical AI practices and the creation of strong regulatory frameworks. Future trends point to a move toward more sophisticated machine learning methods, the incorporation of AI with cutting-edge platforms like block chain and the Internet of Things (IoT), and an emphasis on human-AI cooperation. The article's conclusion is that, despite AI's enormous potential to improve security and resilience, responsible use of this disruptive technology will need proactive interaction with a variety of stakeholders and ethical considerations. Society can successfully handle the complexities of AI and make sure it works as a positive force to counteract emerging risks by encouraging a collaborative approach.
Co-Authors Aftab Arif Muhammad Ismaeel Khan