Cybersecurity data science: an overview from machine learning perspective

IH Sarker, ASM Kayes, S Badsha, H Alqahtani… - Journal of Big …, 2020 - Springer
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 …

Ai-driven cybersecurity: an overview, security intelligence modeling and research directions

IH Sarker, MH Furhad, R Nowrozy - SN Computer Science, 2021 - Springer
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 …

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 …

A survey on machine learning techniques for cyber security in the last decade

K Shaukat, S Luo, V Varadharajan, IA Hameed… - IEEE …, 2020 - ieeexplore.ieee.org
Pervasive growth and usage of the Internet and mobile applications have expanded
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 …

[HTML][HTML] MapReduce based intelligent model for intrusion detection using machine learning technique

M Asif, S Abbas, MA Khan, A Fatima, MA Khan… - Journal of King Saud …, 2022 - Elsevier
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 …

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 …

CICIDS-2017 dataset feature analysis with information gain for anomaly detection

D Stiawan, MYB Idris, AM Bamhdi, R Budiarto - IEEE Access, 2020 - ieeexplore.ieee.org
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 …

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 …

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 …