Extreme learning machines for intrusion detection

Cheng, Chi Ho; Tay, Wee Peng; Huang, Guang-Bin

doi:10.1109/ijcnn.2012.6252449

Cited by 68 publications

(40 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ELMs have been widely used in many fields since their proposal, including intrusion detection. An ELM was used for intrusion detection and showed greater accuracy in intrusion detection than that of an SVM [21]. Ye and Yu proposed an intrusion detection method in which each class was combined into an ensemble classifier using a one-to-all strategy [22].…”

Section: Related Workmentioning

confidence: 99%

Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC

Zhou

Xing

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Fog computing, as a new paradigm, has many characteristics that are different from cloud computing. Due to the resources being limited, fog nodes/MEC hosts are vulnerable to cyberattacks. Lightweight intrusion detection system (IDS) is a key technique to solve the problem. Because extreme learning machine (ELM) has the characteristics of fast training speed and good generalization ability, we present a new lightweight IDS called sample selected extreme learning machine (SS-ELM). The reason why we propose "sample selected extreme learning machine" is that fog nodes/MEC hosts do not have the ability to store extremely large amounts of training data sets. Accordingly, they are stored, computed, and sampled by the cloud servers. Then, the selected sample is given to the fog nodes/MEC hosts for training. This design can bring down the training time and increase the detection accuracy. Experimental simulation verifies that SS-ELM performs well in intrusion detection in terms of accuracy, training time, and the receiver operating characteristic (ROC) value.

show abstract

Section: Related Workmentioning

confidence: 99%

Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC

Zhou

Xing

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…On the other hand, Cheng et al [30] proposed the use of ELM methods to classify binary and multi-class network traffic for intrusion detection with high accuracy. Hsu et al [31] proposed a real-time system for detecting botnets based on anomalous delays in HTTP/HTTPS requests from a given client with very promising results.…”

Section: Related Workmentioning

confidence: 99%

Computational intelligence anti-malware framework for android OS

Demertzis¹,

Iliadis²

2017

Vietnam J Comput Sci

View full text Add to dashboard Cite

It is a fact that more and more users are adopting the online digital payment systems via mobile devices for everyday use. This attracts powerful gangs of cybercriminals, which use sophisticated and highly intelligent types of malware to broaden their attacks. Malicious software is designed to run quietly and to remain unsolved for a long time. It manages to take full control of the device and to communicate (via the Tor network) with its Command & Control servers of fastflux botnets' networks to which it belongs. This is done to achieve the malicious objectives of the botmasters. This paper proposes the development of the computational intelligence anti-malware framework (CIantiMF) which is innovative, ultra-fast and has low requirements. It runs under the android operating system (OS) and its reasoning is based on advanced computational intelligence approaches. The selection of the android OS was based on its popularity and on the number of critical applications available for it. The CIantiMF uses two advanced technology extensions for the ART java virtual machine which is the default in the recent versions of android. The first is the smart anti-malware extension, which can recognize whether the java classes of an android application are benign or malicious using an optimized multi-layer perceptron. The optimization is done by the employment of the biogeography-based optimizer algorithm. The second is the Tor online traffic identification extension, which is capable of achieving malware localization, Tor traffic identification B Konstantinos Demertzis kdemertz@fmenr.duth.gr Lazaros Iliadis

show abstract

“…SVM can only be used for binary data. Cheng et al [28] said that, performance of SVM degrades when it is adapted for multi-class classification. Chitrakar and Chuanhe [29] have given an approach which combines k-Medoids clustering with SVM.…”

Section: Support Vector Machine (Svm) Based Intrusion Detectionmentioning

confidence: 99%

A Survey on Intrusion Detection Systems for Cloud Computing Environment

Kumar¹,

Gohil²

2015

IJCA

View full text Add to dashboard Cite

Cloud Computing is a newly emerged technology. It is getting popularity day by day due to its amazing services. The applications and services based on the cloud are emerging day by day. Due to networked nature of the cloud, resources, data and applications are vulnerable to the attack in cloud environment. So Intrusion Detection Systems (IDS) are employed in the cloud to detect malicious behaviour in the network and in the host. IDS monitors network or host system activities by collecting network information, and analyzes this information for malicious activities and generate alarms, if intrusion takes place. In this paper we surveyed various types of Intrusion Detection Systems proposed over the years for Cloud Computing environment.

show abstract

Extreme learning machines for intrusion detection

Cited by 68 publications

References 15 publications

Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC

Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC

Computational intelligence anti-malware framework for android OS

A Survey on Intrusion Detection Systems for Cloud Computing Environment

Contact Info

Product

Resources

About