Article Per Year (5 Year)
p-Index From 2021 - 2026
0.702
P-Index
This Author published in this journals
All Journal International Journal of Multidisciplinary Sciences and Arts Jurihum
Muhammad Ismaeel Khan
MSIT at Washington university of science and technology‬ - ‪information technology‬ - ‪database management‬
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
AI's Revolutionary Role in Cyber Defense and Social Engineering Muhammad Ismaeel Khan; Aftab Arif; Ali Khan
International Journal of Multidisciplinary Sciences and Arts Vol. 3 No. 4 (2024): International Journal of Multidisciplinary Sciences and Arts, Article October 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Creative methods of cybersecurity are required due to the growing complexity of cyber threats, especially those originating from social engineering techniques. The revolutionary role that artificial intelligence (AI) is playing in transforming cybersecurity practices is examined in this review article. It starts by looking at social engineering assaults and how AI technologies improve the ability to identify threats and take appropriate action. The study goes on to address the particular uses of AI in a number of cybersecurity fields, such as automated incident response, fraud detection, and anomaly detection. The application of AI in cybersecurity is not without difficulties, despite its many advantages. Significant challenges are presented by problems with data quality and bias, adversarial attacks, ethical issues, and resource requirements. In order to create complete cybersecurity plans, it is crucial to integrate AI with human expertise and emphasize the necessity for human oversight and collaboration. Future developments in AI technology are expected to continue, especially in the areas of machine learning algorithms and their integration with newly developed platforms like block chain and the Internet of Things (IoT). Case studies reveal how AI has been successfully implemented in businesses in a variety of industries, demonstrating how AI may enhance danger detection and reaction times. Artificial intelligence has enormous potential to improve cybersecurity protocols. In order to secure a safer digital future, organizations that adopt AI technology while addressing ethical issues and promoting a culture of continuous learning will be in a better position to manage the always changing terrain of cyber dangers.
An overview of cyber threats generated by AI Aftab Arif; Muhammad Ismaeel Khan; Ali Khan
International Journal of Multidisciplinary Sciences and Arts Vol. 3 No. 4 (2024): International Journal of Multidisciplinary Sciences and Arts, Article October 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Artificial intelligence's (AI) quick development has drastically changed cyber security by bringing in both sophisticated cyber threats and cutting-edge defenses. This research paper offers a thorough analysis of AI-generated cyber threats, including their mechanics, noteworthy case examples, and countermeasures. The report demonstrates how attackers carry out high-impact assaults, such as automated phishing, ransom ware, and misinformation campaigns, by utilizing AI tools like machine learning, natural language processing, and deep fake technologies. Important case studies highlight the necessity for enterprises to implement proactive and comprehensive security measures by illuminating the practical effects of these risks. Trends suggest that as AI-generated threats develop, they will become more sophisticated and automated due to the rise of autonomous systems that can carry out assaults without the need for human interaction. Organizations are urged to make investments in cutting-edge AI-powered security solutions, promote a cyber-security-aware culture among staff members, and create strong incident response strategies in order to address these changing issues. Enhancing collective defenses against AI-generated cyber threats requires stakeholder collaboration and information sharing, as well as frequent security assessments and adherence to ethical AI principles. This research indicates that a proactive and adaptive strategy to cyber security will be critical in guaranteeing resilience against the increasingly complex threat landscape posed by AI as enterprises traverse the intricacies of the digital era. In an interconnected world, stakeholders can cooperate to protect their assets and uphold public trust by cultivating a culture of continual development and cooperation.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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 Aftab Arif Ali Khan Ali Raza A Khan