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almaiah m utilizing ai network mobile security threat detection 2025

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Textbook in PDF format Artificial Intelligence (AI) revolutionizes how organizations protect their digital information against cyber threats. Traditional security methods are often insufficient when faced with sophisticated attacks. AI-powered systems utilize Machine Learning, Deep Learning, and advanced analytics to detect patterns, identify anomalies, and predict potential threats in real time. By analyzing network traffic and mobile device behavior, AI can recognize and respond to malicious activity before it causes harm. This proactive approach enhances security protocols, reduces human error, and strengthens defenses against a wide range of cyberattacks, from malware to data breaches. Further research may reveal AI as an indispensable tool for securing networks and mobile environments, providing smarter, more adaptive solutions for threat detection and prevention. Utilizing AI in Network and Mobile Security for Threat Detection and Prevention explores the role of AI in enhancing cybersecurity measures. It examines AI techniques in anomaly and intrusion detection, Machine Learning for malware analysis and detection, predictive analytics to cybersecurity scenarios, and ethical considerations in AI. This book covers topics such as ethics and law, Machine Learning, and Data Science, and is a useful resource for computer engineers, data scientists, security professionals, academicians, and researchers. The rapid increase in ransomware attacks has created a need for new cybersecurity methods. This research introduces a multi-faceted approach using Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) to improve the detection and prevention of ransomware attacks. It uses various AI and ML techniques to detect ransomware signatures, analyze system activities, and understand the evolving threat landscape. It can detect phishing attacks by analyzing email content and user behavior. It can also identify insider threats and assess the security posture of third-party vendors. By using these AI-powered solutions, organizations can strengthen their cybersecurity capabilities, detect threats at an earlier stage, and respond more effectively to cyberattacks. In the future, AI and ML will become more advanced in identifying and addressing complex cyber threats, such as Advanced Persistent Threats (APTs) and zero-day attacks. Preface Chapter 1 Fortifying the Digital World and Shielding Smart Infrastructures: Artificial Intelligence (AI)- Enabled Malware and Threat Chapter 2 Proactively Approaching Cybersecurity With AI- Powered Malware Detection Is Essential Chapter 3 Art of Network Monitoring and Security Enhancement Using AI- Driven Tools Chapter 4 Autoencoder- Based Anomaly Detection for Cyber Threat Monitoring Chapter 5 Malicious Anomaly Detection and Prediction Using Prophet Luminol and RNN Chapter 6 A Framework for Insider Threat Detection Using Role- Based Profile Assessment and Threshold Chapter 7 Attacks Detection and Mitigation of IoT Using Machine Learning Model Chapter 8 Analysis for Detection and Mitigation of Version Number Modification Attack in the Internet of Chapter 9 Semantic Search for IoT- Based Object Recognition Using Clustering and Classification Chapter 10 Ethical Issues in Cyber- Security for Autonomous Vehicles (AV) and Automated Driving: A Comprehensive Review Chapter 11 Post- Quantum Cryptography in the Age of Quantum Computing Chapter 12 Data Security With Replication for Cloud Computing Chapter 13 Trust Evaluation Enhancing Security in the Cloud Market Based on Trust Framework Using Metric Parameter Selection Chapter 14 Secure Cloud- Marketplaces: A Trust Framework for Evaluating Security for Client Service Providers Chapter 15 Evolution of Data Security in E- Commerce

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almaiah m utilizing ai network mobile security threat detection 2025

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