Design network intrusion detection system using support vector machine

Ajdani, Mahdi; Ghaffary, Hamid Reza

doi:10.1002/dac.4689

Cited by 9 publications

(8 citation statements)

References 21 publications

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“…The experimental results proved that the proposed RPSO-logistic regression technique outperforms the standard regression and backpropagation (BP) neural network models. Ajdani et al [ 25 ] in another research proposed an analytical framework that is suitable for high-scale data. The VirusTotal dataset has been applied including 2.5 M scans of 30 months (from 2012 to 2015), which represents a huge amount of data.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel hybrid hunger games algorithm for intrusion detection systems based on nonlinear regression modeling

Mohammadi

Babagoli

2023

Int. J. Inf. Secur.

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Along with the advancement of online platforms and significant growth in Internet usage, various threats and cyber-attacks have been emerging and become more complicated and perilous in a day-by-day base. Anomaly-based intrusion detection systems (AIDSs) are lucrative techniques for dealing with cybercrimes. As a relief, AIDS can be equipped with artificial intelligence techniques to validate traffic contents and tackle diverse illicit activities. A variety of methods have been proposed in the literature in recent years. Nevertheless, several important challenges like high false alarm rates, antiquated datasets, imbalanced data, insufficient preprocessing, lack of optimal feature subset, and low detection accuracy in different types of attacks have still remained to be solved. In order to alleviate these shortcomings, in this research a novel intrusion detection system that efficiently detects various types of attacks is proposed. In preprocessing, Smote-Tomek link algorithm is utilized to create balanced classes and produce a standard CICIDS dataset. The proposed system is based on gray wolf and Hunger Games Search (HGS) meta-heuristic algorithms to select feature subsets and detect different attacks such as distributed denial of services, Brute force, Infiltration, Botnet, and Port Scan. Also, to improve exploration and exploitation and boost the convergence speed, genetic algorithm operators are combined with standard algorithms. Using the proposed feature selection technique, more than 80 percent of irrelevant features are removed from the dataset. The behavior of the network is modeled using nonlinear quadratic regression and optimized utilizing the proposed hybrid HGS algorithm. The results show the superior performance of the hybrid algorithm of HGS compared to the baseline algorithms and the well-known research. As shown in the analogy, the proposed model obtained an average test accuracy rate of 99.17%, which has better performance than the baseline algorithm with 94.61% average accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

A novel hybrid hunger games algorithm for intrusion detection systems based on nonlinear regression modeling

Mohammadi

Babagoli

2023

Int. J. Inf. Secur.

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“…In the second phase, the hybrid approach is done with data processing, cleaned, and given input to machine learning classifiers such as logistic regression and target is considered when both approaches are termed as malicious. It will be less inaccurate in the dynamic analysis [19]. In StormDroid [20] features collected from sensitive permissions like Short Messaging Service (SMS), intent filters tec.…”

Section: Hybrid Analysismentioning

confidence: 99%

Investigation of Android Malware with Machine Learning Classifiers using Enhanced PCA Algorithm

Raymond¹,

Raj²

2023

Computer Systems Science and Engineering

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Android devices are popularly available in the commercial market at different price levels for various levels of customers. The Android stack is more vulnerable compared to other platforms because of its open-source nature. There are many android malware detection techniques available to exploit the source code and find associated components during execution time. To obtain a better result we create a hybrid technique merging static and dynamic processes. In this paper, in the first part, we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Multicollinearity problem is one of the drawbacks in the existing system. In the proposed work, a novel PCA (Principal Component Analysis) based feature reduction technique is implemented with conditional dependency features by gathering the functionalities of the application which adds novelty for the given approach. The Android Sensitive Permission is one major key point to be considered while detecting malware. We select vulnerable columns based on features like sensitive permissions, application program interface calls, services requested through the kernel, and the relationship between the variables henceforth build the model using machine learning classifiers and identify whether the given application is malicious or benign. The final goal of this paper is to check benchmarking datasets collected from various repositories like virus share, Github, and the Canadian Institute of cyber security, compare with models ensuring zero-day exploits can be monitored and detected with better accuracy rate.

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“…Su [15] applied the Attention method in the Bidirectional LSTM model to perform forward and backward LSTM to extract features for the intrusion detection system. However, the BAT-MC algorithm in the developed model failed to utilize the tools effectively resulting in poorer results.…”

Section: Literature Reviewmentioning

confidence: 99%

Generative Adversarial Network and Bayesian Optimization in Multi-class Support Vector Machine for Intrusion Detection System

2023

IJIES

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Network Intrusion detection performances are highly affected by imbalance data problems due to the presence of less number of attack information in the dataset. Deep learning models are applied in existing methods to improve the efficiency that has limitations of overfitting problems. The Generative Adversarial Network (GAN) -Bayesian optimization Multi-class Support Vector Machine (BMSVM) is proposed to overcome imbalance and overfitting problems in intrusion detection systems. The Min-Max Normalization method is applied to normalize the input data to reduce the differences in features. GAN model is applied to generate minority class to balance the data instances to train the model. The proposed GAN-BMSVM model is compared with the classical sampling method, optimization, and classifier in the intrusion detection model in terms of Accuracy, Detection Rate (DR), and False Alarm Rate (FAR). The classical sampling methods are Near-miss, SMOTE and Autoencoder; traditional classifiers are KNN, RF, SVM, DNN and LSTM, and classical optimizations are PSO and WOA. The existing researches such as HCRNN, HLD, DONN, FL-NIDS and CNN-LSTM are used to evaluate the efficiency of GAN-BMSVM model. The GAN-BMSVM model has achieved 99.58% and 85.38 % accuracy for NSL-KDD and UNSW-NB15 dataset respectively, which is higher than the existing CNN-LSTM model.

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Design network intrusion detection system using support vector machine

Cited by 9 publications

References 21 publications

A novel hybrid hunger games algorithm for intrusion detection systems based on nonlinear regression modeling

A novel hybrid hunger games algorithm for intrusion detection systems based on nonlinear regression modeling

Investigation of Android Malware with Machine Learning Classifiers using Enhanced PCA Algorithm

Generative Adversarial Network and Bayesian Optimization in Multi-class Support Vector Machine for Intrusion Detection System

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