Cybersecurity data science: an overview from machine learning perspective
In a computing context, cybersecurity is undergoing massive shifts in technology and its
operations in recent days, and data science is driving the change. Extracting security …
operations in recent days, and data science is driving the change. Extracting security …
Ai-driven cybersecurity: an overview, security intelligence modeling and research directions
Artificial intelligence (AI) is one of the key technologies of the Fourth Industrial Revolution (or
Industry 4.0), which can be used for the protection of Internet-connected systems from cyber …
Industry 4.0), which can be used for the protection of Internet-connected systems from cyber …
Machine learning and deep learning methods for cybersecurity
Y **n, L Kong, Z Liu, Y Chen, Y Li, H Zhu, M Gao… - Ieee …, 2018 - ieeexplore.ieee.org
With the development of the Internet, cyber-attacks are changing rapidly and the cyber
security situation is not optimistic. This survey report describes key literature surveys on …
security situation is not optimistic. This survey report describes key literature surveys on …
A survey on machine learning techniques for cyber security in the last decade
Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …
Building an efficient intrusion detection system based on feature selection and ensemble classifier
Y Zhou, G Cheng, S Jiang, M Dai - Computer networks, 2020 - Elsevier
Intrusion detection system (IDS) is one of extensively used techniques in a network topology
to safeguard the integrity and availability of sensitive assets in the protected systems …
to safeguard the integrity and availability of sensitive assets in the protected systems …
[HTML][HTML] MapReduce based intelligent model for intrusion detection using machine learning technique
With the emergence of the Internet of Things (IoT), the computer networks' phenomenal
expansion, and enormous relevant applications, data is continuously increasing. In this way …
expansion, and enormous relevant applications, data is continuously increasing. In this way …
Machine learning for intelligent data analysis and automation in cybersecurity: current and future prospects
IH Sarker - Annals of Data Science, 2023 - Springer
Due to the digitization and Internet of Things revolutions, the present electronic world has a
wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a …
wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a …
CICIDS-2017 dataset feature analysis with information gain for anomaly detection
Feature selection (FS) is one of the important tasks of data preprocessing in data analytics.
The data with a large number of features will affect the computational complexity, increase a …
The data with a large number of features will affect the computational complexity, increase a …
A deep learning model for network intrusion detection with imbalanced data
Y Fu, Y Du, Z Cao, Q Li, W **ang - Electronics, 2022 - mdpi.com
With an increase in the number and types of network attacks, traditional firewalls and data
encryption methods can no longer meet the needs of current network security. As a result …
encryption methods can no longer meet the needs of current network security. As a result …
BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset
T Su, H Sun, J Zhu, S Wang, Y Li - IEEE Access, 2020 - ieeexplore.ieee.org
Intrusion detection can identify unknown attacks from network traffics and has been an
effective means of network security. Nowadays, existing methods for network anomaly …
effective means of network security. Nowadays, existing methods for network anomaly …