LEADER 04067nam 22005053 450 001 9910843799703321 005 20240321080236.0 010 $a1-394-19647-4 010 $a1-394-19645-8 035 $a(MiAaPQ)EBC31214547 035 $a(Au-PeEL)EBL31214547 035 $a(EXLCZ)9930967972000041 100 $a20240321d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection 205 $a1st ed. 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2024. 210 4$dİ2024. 215 $a1 online resource (371 pages) 311 $a1-394-19644-X 327 $aPART A: Artificial Intelligence (AI) in Cyber Security Analytics: Fundamental and Challenges. Analysis of Malicious executables and detection techniques. Detection and Analysis of Botnet Attacks Using Machine Learning Techniques. Artificial Intelligence Perspective on Digital Forensics. Review on Machine Learning-based Traffic Rules Contravention Detection System. Enhancing Cybersecurity Ratings using Artificial Intelligence and DevOps Technologies -- PART B: Cyber Threat Detection and Analysis Using Artificial Intelligence and Big Data. Malware analysis techniques in Android-based Smartphones Applications. Cyber Threat Detection and Mitigation Using Artificial Intelligence - A Cyber-physical Perspective. Performance Analysis of Intrusion Detection System using ML techniques. Spectral Pattern learning approach-based student sentiment analysis using Dense-net multiperception neural network in E-learning Environment. Big Data and Deep Learning Based Tourism Industry Sentiment Analysis Using Deep Spectral Recurrent Neural Network -- PART C: Applied Artificial Intelligence Approaches in Emerging Cyber Security Domains. Enhancing Security in Cloud Computing using Artificial Intelligence (AI). Utilization of Deep Learning Models for Safe Human-friendly Computing in Cloud, Fog and Mobile Edge Networks. Artificial Intelligence for Threat Anomaly Detection using Graph Databases - A Semantic Outlook. Security in Blockchain-based Smart Applications using Artificial Intelligence (AI). Leveraging Deep Learning Techniques for Securing the Internet of Things in the Age of Big Data Era. 330 $a"Today, it's impossible to deploy effective cybersecurity technology without relying heavily on machine learning. With machine learning, cybersecurity systems can be analyzed using patterns and learn from them to help prevent similar attacks and respond to changing behavior. It can help cybersecurity teams to be more proactive in preventing threats and responding to active attacks in real time. In short, machine learning can make cybersecurity simpler, more proactive, less expensive, and far more effective. AI cybersecurity, with the support of machine learning, is set to be a powerful tool in the looming future. As with other industries, human interaction has long been essential and irreplaceable in security. While cybersecurity currently relies heavily on human input, we are gradually seeing technology become better at specific tasks than we are"--$cProvided by publisher. 606 $aComputer security$xTechnological innovations 606 $aArtificial intelligence 606 $aComputer networks$xSecurity measures 606 $aComputer crimes$xPrevention 615 0$aComputer security$xTechnological innovations. 615 0$aArtificial intelligence. 615 0$aComputer networks$xSecurity measures. 615 0$aComputer crimes$xPrevention. 676 $a006.3 700 $aMahajan$b Shilpa$01733871 701 $aKhurana$b Mehak$01733872 701 $aEstrela$b Vania Vieira$01733873 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910843799703321 996 $aApplying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection$94149875 997 $aUNINA