Attack Detection in Cloud Infrastructures Using Artificial Neural Network with Genetic Feature Selection

Guha, Sayantan; Yau, Stephen S.; Buduru, Arun Balaji

doi:10.1109/dasc-picom-datacom-cyberscitec.2016.32

Cited by 23 publications

(13 citation statements)

References 17 publications

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“…Various methods have been proposed in the literature for network anomaly detection including standard machine learning classifiers 4–29 and deep learning techniques 30–47 . Muda et al performed clustering before classification and compared the single classifiers with hybrid classifiers.…”

Section: Related Workmentioning

confidence: 99%

“…Gharaee and Hossein developed genetic based support vector machines and obtained 99.05%, 99.95%, 99.06%, 98.25%, and 100% accuracy for attack types Normal, Probe, DOS, U2R, and R2L, respectively, using KDDCup99 dataset 20 . Guha et al developed an ensemble method that combines artificial neural networks with genetic algorithm and obtained 91.98% accuracy rate on two large datasets 21 . Wheelus et al showed that the pre‐processing of datasets to address class imbalance does indeed provide some benefit using UNSW‐NB15 dataset 23 .…”

Section: Related Workmentioning

confidence: 99%

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A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

Görmez

Aydın

Karademir³

et al. 2020

Int J Communication

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Summary Detecting malicious behavior is important for preventing security threats in a computer network. Denial of Service (DoS) is among the popular cyber attacks targeted at web sites of high‐profile organizations and can potentially have high economic and time costs. In this paper, several machine learning methods including ensemble models and autoencoder‐based deep learning classifiers are compared and tuned using Bayesian optimization. The autoencoder framework enables to extract new features by mapping the original input to a new space. The methods are trained and tested both for binary and multi‐class classification on Digiturk and Labris datasets, which were introduced recently for detecting various types of DDoS attacks. The best performing methods are found to be ensembles though deep learning classifiers achieved comparable level of accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

Görmez

Aydın

Karademir³

et al. 2020

Int J Communication

View full text Add to dashboard Cite

show abstract

“…In [13], a standard neural network (abbreviated as NN) has been used to classify attacks and genetic algorithm for feature selection. The similar features are classified into feature sets, and accuracy of each set is measured by multi-modal neural network (abbreviated as MNN).…”

Section: Related Workmentioning

confidence: 99%

Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation

Sharma

Giri

Granmo

et al. 2019

EURASIP J. on Info. Security

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Recent advances in intrusion detection systems based on machine learning have indeed outperformed other techniques, but struggle with detecting multiple classes of attacks with high accuracy. We propose a method that works in three stages. First, the ExtraTrees classifier is used to select relevant features for each type of attack individually for each (ELM). Then, an ensemble of ELMs is used to detect each type of attack separately. Finally, the results of all ELMs are combined using a softmax layer to refine the results and increase the accuracy further. The intuition behind our system is that multi-class classification is quite difficult compared to binary classification. So, we divide the multi-class problem into multiple binary classifications. We test our method on the UNSW and KDDcup99 datasets. The results clearly show that our proposed method is able to outperform all the other methods, with a high margin. Our system is able to achieve 98.24% and 99.76% accuracy for multi-class classification on the UNSW and KDDcup99 datasets, respectively. Additionally, we use the weighted extreme learning machine to alleviate the problem of imbalance in classification of attacks, which further boosts performance. Lastly, we implement the ensemble of ELMs in parallel using GPUs to perform intrusion detection in real time.

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“…In the literature, feature selection techniques have been utilised to obtain and use just the more significant attributes without affecting their semantics, but this is beyond the scope of this paper. For simplicity, this work assumes the following four features are returned by a certain feature selection process, which have also been utilised in [3], [24]:…”

Section: A Feature Selection and Fuzzificationmentioning

confidence: 99%

Weighted Fuzzy Rules Optimised by Particle Swarm for Network Intrusion Detection

Chen

Shang

et al. 2018

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Network intrusion detection systems (IDSs) dynamically monitor communication events on a network, and decide whether any event is symptomatic of an attack or constitutes a legitimate use of the system. They have become an indispensable component of security infrastructure, e.g., to detect threats before widespread damage takes place. A variety of approaches have been proposed to design IDSs, including fuzzy rule-based techniques that offer advantages such as tolerance of noisy and imprecise data. In particular, fuzzy rules can be highly interpretable and trackable if the underlying fuzzy sets are predefined, directly reflecting domain expertise. This paper proposes such an approach to generate a set of weighted fuzzy rules for building effective IDSs, where the rule weights are optimised by Particle Swarm Optimisation without affecting the underlying predefined fuzzy sets. Experiments are performed on benchmark IDS datasets with comparison to alternative systems built with popular machine learning methods.

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Attack Detection in Cloud Infrastructures Using Artificial Neural Network with Genetic Feature Selection

Cited by 23 publications

References 17 publications

A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation

Weighted Fuzzy Rules Optimised by Particle Swarm for Network Intrusion Detection

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