Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection / edited by Shilpa Mahajan, Mehak Khurana, and Vania Vieira Estrela.

Format:

Book

Contributor:

Mahajan, Shilpa, editor.

Khurana, Mehak, editor.

Estrela, Vania Vieira, editor.

Language:

English

Subjects (All):

Computer security--Technological innovations.

Computer security.

Artificial intelligence.

Computer networks--Security measures.

Computer networks.

Computer crimes--Prevention.

Computer crimes.

Physical Description:

1 online resource (371 pages)

Edition:

First edition.

Place of Publication:

Hoboken, NJ : John Wiley & Sons, Inc., [2024]

Summary:

"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"-- Provided by publisher.

Contents:

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 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.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes bibliographical references and index.

ISBN:

9781394196470

1394196474

9781394196456

1394196458

OCLC:

1427498230