Network optimization using defender system in cloud computing security based intrusion detection system withgame theory deep neural network (IDSGT-DNN)

Balamurugan, E.; Mehbodniya, Abolfazl; Kariri, Elham; Yadav, Kusum; Kumar, Anil; AnulHaq, Mohd

doi:10.1016/j.patrec.2022.02.013

Cited by 41 publications

(12 citation statements)

References 16 publications

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“…Furthermore, the IDSGT-DNN framework, presented by [37], elevates cloud security by seamlessly integrating an attacker-defender mechanism using game theory and deep neural networks. This framework outperforms traditional methods in accuracy, detection rate, and various metrics on the CICIDS-2017 dataset.…”

Section: A Discussionmentioning

confidence: 99%

A Comprehensive Analysis of Network Security Attack Classification using Machine Learning Algorithms

Alqahtani,

Altammami,

Haq

2024

IJACSA

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As internet usage and connected devices continue to proliferate, the concern for network security among individuals, businesses, and governments has intensified. Cybercriminals exploit these opportunities through various attacks, including phishing emails, malware, and DDoS attacks, leading to disruptions, data exposure, and financial losses. In response, this study investigates the effectiveness of machine learning algorithms for enhancing intrusion detection systems in network security. Our findings reveal that Random Forest demonstrates superior performance, achieving 90% accuracy and balanced precision-recall scores. KNN exhibits robust predictive capabilities, while Logistic Regression delivers commendable accuracy, precision, and recall. However, Naive Bayes exhibits slightly lower performance compared to other algorithms. The study underscores the significance of leveraging advanced machine learning techniques for accurate intrusion detection, with Random Forest emerging as a promising choice. Future research directions include refining models and exploring novel approaches to further enhance network security.

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Section: A Discussionmentioning

confidence: 99%

A Comprehensive Analysis of Network Security Attack Classification using Machine Learning Algorithms

Alqahtani,

Altammami,

Haq

2024

IJACSA

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“…Additionally, the study discusses using bio-inspired models and hybrid deep learning techniques to make network security more robust [35]. Furthermore, the research explores the use of Long Short-Term Memory [37] based Convolutional Neural Networks for developing a reliable intrusion detection scheme [36]. Overall, the study presents various innovative approaches to enhance network security and protect against cyber threats.…”

Section: Related Workmentioning

confidence: 99%

“…In this study, researchers propose a new way to protect computer networks from unwanted intruders, like hackers. They suggest using a special algorithm called Simple Genetic Algorithm in the cloud to make intrusion detection systems more efficient [37]. Another study focuses on using deep learning, which is a type of computer intelligence, to build a smart system that can detect and respond to network attacks [38].…”

Section: Related Workmentioning

confidence: 99%

Detecting Unbalanced Network Traffic Intrusions With Deep Learning

Pavithra,

Venkata Vikas

2024

IEEE Access

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The growth of cyber threats demands a robust and adaptive intrusion detection system (IDS) capable of effectively recognizing malicious activities from network traffic. However, the existing imbalance of class in network data possess a significant challenge to traditional IDS. To overcome these challenges, this project proposes a novel hybrid Intrusion Detection System using machine learning algorithms, which includes XGBoost, Long Short-Term Memory (LSTM), Mini-VGGNet, and AlexNet, which is used to handle the unbalanced network traffic data. Furthermore, the Random Forest Regressor is used to ascertain the importance of features for enhancing detection accuracy and interpretability. Addressing the inherent class imbalance in network data is crucial for ensuring the IDS's effectiveness. The proposed system employs a combination of oversampling techniques for minority classes and under sampling techniques for majority classes during data preprocessing. This balanced representation of network traffic data helps prevent the IDS from being biased towards the majority class and improves its ability to detect rare or novel intrusions. The utilization of Random Forest Regressor for feature extraction serves a dual purpose. It helps identify the most relevant features within the network traffic data that contribute significantly to detecting intrusions. It enables the system to prioritize and focus on these important features during model training, thereby enhancing detection accuracy while reducing computational complexity. This research contributes to the ongoing efforts to mitigate cyber threats and safeguard critical network infrastructures.

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“…The experimental results showed that the proposal achieved a high accuracy of 99.52% and a low training time of only 362 seconds on the NSL-KDD dataset. Balamurugan et al [29] developed an IDS that incorporates game theory (GT) and a deep neural network (DNN). The GT is integrated into the DNN network to select optimal solutions and improve the classification performance of DNN.…”

Section: Related Workmentioning

confidence: 99%

Building a Cloud-IDS by Hybrid Bio-Inspired Feature Selection Algorithms Along With Random Forest Model

Bakro,

Kumar,

Husain

et al. 2024

IEEE Access

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The adoption of cloud computing has become increasingly widespread across various domains. However, the inherent security vulnerabilities of cloud computing pose significant risks to its overall safety. Consequently, intrusion detection systems (IDS) play a pivotal role in identifying malicious activities within a cloud system. The considerable volume of network traffic data may contain redundant and irrelevant features that can impact the classification performance of the classifier. In addition, the complexity and time consumption increase while processing such a substantial volume of data in the cloud intrusion detection process. To enhance the performance of the IDS, this study proposes a hybrid feature selection approach, combining two bio-inspired algorithms, namely the grasshopper optimization algorithm (GOA) and the genetic algorithm (GA). The combination of these two algorithms ensures a more efficient search for optimal solutions. A random forest (RF) classifier is trained using those optimal features. Moreover, the proposal addresses the challenge of imbalanced data by employing a hybrid approach: over-sampling the minority classes using an adaptive synthetic (ADASYN) algorithm, while implementing random under-sampling (RUS) for the majority class as needed. This integrated strategy significantly influences each category, enhancing the true positive rate (TPR) while minimizing the false positive rate (FPR), thus improving the overall system performance. The proposed approach was evaluated using three datasets: UNSW-NB15, CIC-DDoS2019, and CIC Bell DNS EXF 2021. The recorded accuracies for these datasets were 98%, 99%, and 92%, respectively. The hybrid feature selection-based IDS demonstrated superior performance in multi-class classification, along with exemplary results for individual classes within the datasets. The proposed strategy exhibited a marked superiority with the random forest classifier, especially when compared to other classifiers including SVM, LR, FLN, LSTM, AlexNet, DNN, DBN, DT, and XGBoost. Moreover, this performance remained consistent and commendable even when benchmarked against contemporary state-of-the-art methodologies across multiple evaluation metrics.

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Network optimization using defender system in cloud computing security based intrusion detection system withgame theory deep neural network (IDSGT-DNN)

Cited by 41 publications

References 16 publications

A Comprehensive Analysis of Network Security Attack Classification using Machine Learning Algorithms

A Comprehensive Analysis of Network Security Attack Classification using Machine Learning Algorithms

Detecting Unbalanced Network Traffic Intrusions With Deep Learning

Building a Cloud-IDS by Hybrid Bio-Inspired Feature Selection Algorithms Along With Random Forest Model

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