An Improved Design for a Cloud Intrusion Detection System Using Hybrid Features Selection Approach With ML Classifier

Bakro, Mhamad; Kumar, Rakesh Ranjan; Alabrah, Amerah; Ashraf, Zubair; Ahmed, Md. Kawser; Shameem, Mohammad; Abdelsalam, Ahmed

doi:10.1109/access.2023.3289405

Cited by 30 publications

(6 citation statements)

References 56 publications

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“…Although the three references [68], [40], and [34] outperformed our approach in specific classes, overall, our approach performed better on other metrics. According to Figure 9, we have constructed Table 16 which presents the assessment norms for each type in the CIC-DDoS 2019 dataset.…”

Section: Experiments and Results Discussionmentioning

confidence: 84%

“…[76], the "normal" class in works No. [86] and [34], and the "reconnaissance" and "analysis" classes in reference No. [34].…”

Section: Experiments and Results Discussionmentioning

confidence: 99%

“…Finally, the misuse (signature) database was updated using attack data gathered from several servers. Authors [34] offered an effective IDS that incorporates a feature selection concept comprised of IG, CS, and PSO to select meaningful features. Additionally, the system addresses data imbalance issues using SMOTE and employs a random forest for classification.…”

Section: Related Workmentioning

confidence: 99%

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

Bakro,

Kumar,

Husain

et al. 2024

IEEE Access

Self Cite

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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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Section: Experiments and Results Discussionmentioning

confidence: 84%

“…[76], the "normal" class in works No. [86] and [34], and the "reconnaissance" and "analysis" classes in reference No. [34].…”

Section: Experiments and Results Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Bakro,

Kumar,

Husain

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, to represent a more realistic network environment, a diverse dataset needed to be examined. Bakro et al [21] introduced a hybrid feature selection approach that combined filter techniques such as Particle Swarm Optimization (PSO), Chi-Square (CS), and Information Gain (IG). Combining each of these three techniques was a novel method that generated a more reliable process of feature selection by using every technique's strength to increase the possibilities of selecting the most associated features.…”

Section: Literature Surveymentioning

confidence: 99%

Healthcare Intrusion Detection using Hybrid Correlation-based Feature Selection-Bat Optimization Algorithm with Convolutional Neural Network

Bella,

Vasundra

2024

IJACSA

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Cloud computing is popular among users in various areas such as healthcare, banking, and education due to its low-cost services alongside increased reliability and efficiency. But, security is a significant problem in cloud-based systems due to the cloud services being accessed via the Internet by a variety of users. Therefore, the patient's health information needs to be kept confidential, secure, and accurate. Moreover, any change in actual patient data potentially results in errors during the diagnosis and treatment. In this research, the hybrid Correlation-based Feature Selection-Bat Optimization Algorithm (HCFS-BOA) based on the Convolutional Neural Network (CNN) model is proposed for intrusion detection to secure the entire network in the healthcare system. Initially, the data is obtained from the CIC-IDS2017, NSL-KDD datasets, after which min-max normalization is performed to normalize the acquired data. HCFS-BOA is employed in feature selection to examine the appropriate features that not only have significant correlations with the target variable, but also contribute to the optimal performance of intrusion detection in the healthcare system. Finally, CNN classification is performed to identify and classify intrusion detection accurately and effectively in the healthcare system. The existing methods namely, SafetyMed, Hybrid Intrusion Detection System (HIDS), and Blockchain-orchestrated Deep learning method for Secure Data Transmission in IoTenabled healthcare systems (BDSDT) are employed to evaluate the efficacy of HCFS-BOA-based CNN. The proposed HCFS-BOA-based CNN achieves a better accuracy of 99.45% when compared with the existing methods: SafetyMed, HIDS, and BDSDT.

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“…This reflects the traffic compositions and online intrusions of a particular time, which can be reproducible, modified, and extensible. To be able to do this, training a deep learning model to identify anomalies from a given dataset is one of the initial stages of data preprocessing which we will be looking at in the preceding sections (Alabrah, 2023;Bakro et al, 2023). Anomaly-based systems are the most effective IDSs, due to the application of machine learning algorithms embedded within the system.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Metrics of an Intrusion Detection System Through Window-Based Deep Learning Models

Isiaka

2023

JDSIS

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Intrusion and Prevention technologies perform reliably in harsh conditions by fortifying many of the world’s highest security sites with few defects in high performance. This paper aims to contribute by designing an Intrusion/Preventive System using a window-based convolutional neural network (CNN), an integrated recurrent neural network (RNN) and autoencoders (AutoE) to detect and test the performance of the intrusion detection system. The data packets were converted to images where the pixels were used as input. The CNN architecture shows a three (3) layer model with high predictive performance. The result shows high performance on CNN as compared to both RNN and AutoE, CNN seems to resist overfitting more than the rest of the models. The future perspective would be to test the model on other standard methods such as support vector machine (SVM) and Dynamic Control Systems.

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An Improved Design for a Cloud Intrusion Detection System Using Hybrid Features Selection Approach With ML Classifier

Cited by 30 publications

References 56 publications

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

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

Healthcare Intrusion Detection using Hybrid Correlation-based Feature Selection-Bat Optimization Algorithm with Convolutional Neural Network

Performance Metrics of an Intrusion Detection System Through Window-Based Deep Learning Models

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