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[null,"1.","Record Nr.",null,"UNINA9910843799703321"]
[null,null,"Autore",null,"Mahajan Shilpa"]
[null,null,"Titolo",null,"Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"1-394-19647-4"]
[{"$nulls":4},"1-394-19645-8"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (371 pages)"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Computer security - Technological innovations"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computer networks - Security measures"]
[{"$nulls":4},"Computer crimes - Prevention"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"PART A: Artificial Intelligence (AI) in Cyber Security Analytics: Fundamental and Challenges. Analysis of Malicious executables and detection techniques. Detection and Analysis of Botnet Attacks Using Machine Learning Techniques. Artificial Intelligence Perspective on Digital Forensics. Review on Machine Learning-based Traffic Rules Contravention Detection System. Enhancing Cybersecurity Ratings using Artificial Intelligence and DevOps Technologies -- PART B: Cyber Threat Detection and Analysis Using Artificial Intelligence and Big Data. Malware analysis techniques in Android-based Smartphones Applications. Cyber Threat Detection and Mitigation Using Artificial Intelligence - A Cyber-physical Perspective. Performance Analysis of Intrusion Detection System using ML techniques. Spectral Pattern learning approach-based student sentiment analysis using Dense-net multiperception neural network in E-learning Environment. Big Data and Deep Learning Based Tourism Industry Sentiment Analysis Using Deep Spectral Recurrent Neural Network -- PART C: Applied Artificial Intelligence Approaches in Emerging Cyber Security Domains. Enhancing Security in Cloud Computing using Artificial Intelligence (AI). Utilization of Deep Learning Models for Safe Human-friendly Computing in Cloud, Fog and Mobile Edge Networks. Artificial Intelligence for Threat Anomaly Detection using Graph Databases - A "]
[{"$nulls":4},"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."]
[null,null,null,"Sommario/riassunto","\"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\"--"]
[null,"2.","Record Nr.",null,"UNINA9910376368203321"]
[null,null,"Autore",null,"Rabbah Rodric"]
[null,null,"Titolo",null,"2013 International Conference on Compilers, Architecture and Synthesis for Embedded Systems (CASES)"]
[null,null,"Pubbl/distr/stampa",null,"[Place of publication not identified], : IEEE, 2013"]
[null,null,"ISBN",null,"1-4799-1400-2"]
[null,null,"Descrizione fisica",null,"1 online resource"]
[null,null,"Collana",null,"ACM Conferences"]
[null,null,"Disciplina",null,"005.453"]
[null,null,"Soggetti",null,"Computer architecture"]
[{"$nulls":4},"Compilers (Computer programs)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Sommario/riassunto","Welcome to the 2013 Conference on Compilers, Architectures and Synthesis for Embedded Systems - CASES'13! We are very pleased to continue the CASES tradition of bringing a unique focus on the intersection of compilers and architectures to Embedded Systems Week (ESWEEK). CASES has served this purpose since 2006 when it first formed part of the three core conferences that comprise ESWEEK."]