Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Z Ahmad, A Shahid Khan, C Wai Shiang… - Transactions on …, 2021 - Wiley Online Library
The rapid advances in the internet and communication fields have resulted in a huge
increase in the network size and the corresponding data. As a result, many novel attacks are …

[HTML][HTML] Machine learning and deep learning methods for intrusion detection systems: A survey

H Liu, B Lang - applied sciences, 2019 - mdpi.com
Networks play important roles in modern life, and cyber security has become a vital research
area. An intrusion detection system (IDS) which is an important cyber security technique …

Dual-IDS: A bagging-based gradient boosting decision tree model for network anomaly intrusion detection system

MHL Louk, BA Tama - Expert Systems with Applications, 2023 - Elsevier
The mission of an intrusion detection system (IDS) is to monitor network activities and
assess whether or not they are malevolent. Specifically, anomaly-based IDS can discover …

[PDF][PDF] 网络安全入侵检测: 研究综述

蒋建春, 马恒太, 任党恩, 卿斯汉 - 软件学报, 2000 - jos.org.cn
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Dos and don'ts of machine learning in computer security

D Arp, E Quiring, F Pendlebury, A Warnecke… - 31st USENIX Security …, 2022 - usenix.org
With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …

CorrAUC: a malicious bot-IoT traffic detection method in IoT network using machine-learning techniques

M Shafiq, Z Tian, AK Bashir, X Du… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
Identification of anomaly and malicious traffic in the Internet-of-Things (IoT) network is
essential for the IoT security to keep eyes and block unwanted traffic flows in the IoT …

[HTML][HTML] IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset

Y Yin, J Jang-Jaccard, W Xu, A Singh, J Zhu… - Journal of Big Data, 2023 - Springer
The effectiveness of machine learning models can be significantly averse to redundant and
irrelevant features present in the large dataset which can cause drastic performance …

[HTML][HTML] An explainable deep learning-enabled intrusion detection framework in IoT networks

M Keshk, N Koroniotis, N Pham, N Moustafa… - Information …, 2023 - Elsevier
Although the field of eXplainable Artificial Intelligence (XAI) has a significant interest these
days, its implementation within cyber security applications still needs further investigation to …

[HTML][HTML] Machine learning for Internet of Things data analysis: A survey

MS Mahdavinejad, M Rezvan, M Barekatain… - Digital Communications …, 2018 - Elsevier
Rapid developments in hardware, software, and communication technologies have
facilitated the emergence of Internet-connected sensory devices that provide observations …

A hybrid deep learning model for efficient intrusion detection in big data environment

MM Hassan, A Gumaei, A Alsanad, M Alrubaian… - Information …, 2020 - Elsevier
The volume of network and Internet traffic is expanding daily, with data being created at the
zettabyte to petabyte scale at an exceptionally high rate. These can be characterized as big …