A detailed investigation and analysis of using machine learning techniques for intrusion detection

P Mishra, V Varadharajan… - … surveys & tutorials, 2018 - ieeexplore.ieee.org
Intrusion detection is one of the important security problems in todays cyber world. A
significant number of techniques have been developed which are based on machine …

An effective intrusion detection approach using SVM with naïve Bayes feature embedding

J Gu, S Lu - Computers & Security, 2021 - Elsevier
Network security has become increasingly important in recent decades, while intrusion
detection system plays a critical role in protecting it. Various machine learning techniques …

Distributed denial-of-service (DDoS) attacks and defense mechanisms in various web-enabled computing platforms: issues, challenges, and future research directions

A Singh, BB Gupta - International Journal on Semantic Web and …, 2022 - igi-global.com
The demand for Internet security has escalated in the last two decades because the rapid
proliferation in the number of Internet users has presented attackers with new detrimental …

[HTML][HTML] MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review

G Kumar, K Thakur, MR Ayyagari - The Journal of Supercomputing, 2020 - Springer
Network security plays an essential role in secure communication and avoids financial loss
and crippled services due to network intrusions. Intruders generally exploit the flaws of …

HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection

W Wang, Y Sheng, J Wang, X Zeng, X Ye… - IEEE …, 2017 - ieeexplore.ieee.org
The development of an anomaly-based intrusion detection system (IDS) is a primary
research direction in the field of intrusion detection. An IDS learns normal and anomalous …

Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system

WL Al-Yaseen, ZA Othman, MZA Nazri - Expert Systems with Applications, 2017 - Elsevier
Intrusion detection has become essential to network security because of the increasing
connectivity between computers. Several intrusion detection systems have been developed …

CANN: An intrusion detection system based on combining cluster centers and nearest neighbors

WC Lin, SW Ke, CF Tsai - Knowledge-based systems, 2015 - Elsevier
The aim of an intrusion detection systems (IDS) is to detect various types of malicious
network traffic and computer usage, which cannot be detected by a conventional firewall …

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

O Osanaiye, H Cai, KKR Choo… - EURASIP Journal on …, 2016 - Springer
Widespread adoption of cloud computing has increased the attractiveness of such services
to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud's …

A taxonomy of network threats and the effect of current datasets on intrusion detection systems

H Hindy, D Brosset, E Bayne, AK Seeam… - IEEE …, 2020 - ieeexplore.ieee.org
As the world moves towards being increasingly dependent on computers and automation,
building secure applications, systems and networks are some of the main challenges faced …

An efficient intrusion detection system based on support vector machines and gradually feature removal method

Y Li, J Xia, S Zhang, J Yan, X Ai, K Dai - Expert systems with applications, 2012 - Elsevier
The efficiency of the intrusion detection is mainly depended on the dimension of data
features. By using the gradually feature removal method, 19 critical features are chosen to …