A survey on deep learning for cybersecurity: Progress, challenges, and opportunities
As the number of Internet-connected systems rises, cyber analysts find it increasingly difficult
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …
The cross-evaluation of machine learning-based network intrusion detection systems
Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning
(ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where …
(ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where …
An artificial intelligence-based collaboration approach in industrial iot manufacturing: Key concepts, architectural extensions and potential applications
The digitization of manufacturing industry has led to leaner and more efficient production,
under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and …
under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and …
Current trends in AI and ML for cybersecurity: A state-of-the-art survey
N Mohamed - Cogent Engineering, 2023 - Taylor & Francis
This paper provides a comprehensive survey of the state-of-the-art use of Artificial
Intelligence (AI) and Machine Learning (ML) in the field of cybersecurity. The paper …
Intelligence (AI) and Machine Learning (ML) in the field of cybersecurity. The paper …
Optical network security management: requirements, architecture, and efficient machine learning models for detection of evolving threats
As the communication infrastructure that sustains critical societal services, optical networks
need to function in a secure and agile way. Thus, cognitive and automated security …
need to function in a secure and agile way. Thus, cognitive and automated security …
Combining sociocultural intelligence with Artificial Intelligence to increase organizational cyber security provision through enhanced resilience
PRJ Trim, YI Lee - Big Data and Cognitive Computing, 2022 - mdpi.com
Although artificial intelligence (AI) and machine learning (ML) can be deployed to improve
cyber security management, not all managers understand the different types of AI/ML and …
cyber security management, not all managers understand the different types of AI/ML and …
Knowledge in the grey zone: AI and cybersecurity
T Stevens - Digital War, 2020 - Springer
Cybersecurity protects citizens and society from harm perpetrated through computer
networks. Its task is made ever more complex by the diversity of actors—criminals, spies …
networks. Its task is made ever more complex by the diversity of actors—criminals, spies …
[PDF][PDF] Machine learning-based intrusion detection system for detecting web attacks
FA Vadhil, ML Salihi, MF Nanne - IAES International Journal of …, 2024 - academia.edu
The increasing use of smart devices results in a huge amount of data, which raises concerns
about personal data, including health data and financial data. This data circulates on the …
about personal data, including health data and financial data. This data circulates on the …
Artificial intelligence and critical systems: From hype to reality
Artificial intelligence will be deployed increasingly in more systems that affect public health,
safety, and welfare. These systems will better utilize scarce resources; prevent disasters; …
safety, and welfare. These systems will better utilize scarce resources; prevent disasters; …
Machine learning on knowledge graphs for context-aware security monitoring
Machine learning techniques are gaining attention in the context of intrusion detection due
to the increasing amounts of data generated by monitoring tools, as well as the …
to the increasing amounts of data generated by monitoring tools, as well as the …