Machine Learning for Cybersecurity
Springer International Publishing (Verlag)
978-3-031-15892-6 (ISBN)
By implementing innovative deep learning solutions, cybersecurity researchers, students and practitioners can analyze patterns and learn how to prevent cyber-attacks and respond to changing malware behavior.
The knowledge and tools introduced in this brief can also assist cybersecurity teams to become more proactive in preventing threats and responding to active attacks in real time. It can reduce the amount of time spent on routine tasks and enable organizations to use their resources more strategically. In short, the knowledge and techniques provided in this brief can help make cybersecurity simpler, more proactive, less expensive and far more effective
Advanced-level students in computer science studying machine learning with a cybersecurity focus will find this SpringerBrief useful as a study guide. Researchers and cybersecurity professionals focusing on the application of machine learning tools and techniques to the cybersecurity domain will also want to purchase this SpringerBrief.
lt;b>Dr. Marwan Omar is an Associate Professor of Cybersecurity at Illinois Institute of Technology since August, 2022. Dr. Omar received a Master's degree in Information Systems and Technology from the University of Phoenix, 2009 and a Doctorate Degree in Digital Systems Security from Colorado Technical University, 2012. Dr. Omar has a track record of publications in the area of cyber security along with extensive teaching experience as well as industry experience. Dr. Omar recently earned a Post-Doctoral certificate from the University of Fernando Pessoa, Portugal and holds numerous industry certifications including CEH, Sec+, GASF, and CDPSE, among others.
1. Application of Machine Learning (ML) to Address Cyber Security Threats.- 2. New Approach to Malware Detection Using Optimized Convolutional Neural Network.- 3. Malware Anomaly Detection Using Local Outlier Factor Technique.
| Erscheinungsdatum | 27.09.2022 |
|---|---|
| Reihe/Serie | SpringerBriefs in Computer Science |
| Zusatzinfo | VIII, 48 p. 32 illus., 22 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 101 g |
| Themenwelt | Informatik ► Netzwerke ► Sicherheit / Firewall |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | algorithm • Anomaly Detection • convolutional neural networks • cyber attacks • cybersecurity • Dataset • decision trees • Deep learning • local outlier factor • machine learning • Malware Classification • malware detection • Outlier Detection • training data |
| ISBN-10 | 3-031-15892-X / 303115892X |
| ISBN-13 | 978-3-031-15892-6 / 9783031158926 |
| Zustand | Neuware |
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