A highly secured intrusion detection system for IoT using EXPSO-STFA feature selection for LAANN to detect attacks

Jeyaselvi, M.; Dhanaraj, Rajesh Kumar; Sathya, M.; Memon, Fida Hussain; Krishnasamy, Lalitha; Dev, Kapal; Wang, Ziyue; Qureshi, Nawab Muhammad Faseeh

doi:10.1007/s10586-022-03607-1

Cited by 30 publications

(8 citation statements)

References 31 publications

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“…Method Accuracy(%) Zegarra et al [41] TabNet 97.03 Yao R et al [42] XGBoost 91.06 Kareem et al [43] GTO-BSA 98.79 Jeyaselvi, M. et al [44] EXPSO-STFA 95.65 Proposed BBO 99.1 Reference Method Accuracy(%) Yaseen et al [45] differential evalution 80.15 Yao R et al [42] XGBoost 91.06 Kareem et al [43] GTO-BSA 95.59 Ingre et al [46] CFS+ANN 79.9 Proposed BBO 97.5…”

Section: Referencementioning

confidence: 99%

BBO-CFAT: Network Intrusion Detection Model Based on BBO Algorithm and Hierarchical Transformer

Jiang,

Fu,

Wang

2024

IEEE Access

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In today's network environments vulnerable to cyber threats like hackers and viruses, intrusion detection technology is considered the most effective means of detection and defense. Deep neural networks are commonly used in intrusion detection technology. However, improving the model's ability to extract feature information and reducing computational space while retaining local feature information are critical challenges that need to be addressed.To tackle these issues, this paper proposes a model named BBO-CFAT, which combines the Biogeography-Based Optimization algorithm (BBO) for feature selection and an improved Transformer model for preserving context information and reducing computational space. Specifically, the BBO-CFAT model employs a roulette selection method to control the operations of migration and mutation operators. It utilizes feature information entropy to weight updates of adaptive variables in these operators, thereby enhancing the credibility of feature selection. Furthermore, the Transformer framework is hierarchically designed to facilitate the acquisition of context information. Additionally, depthwise separable convolutions are employed to reduce computational space, thereby improving computational efficiency and training speed.Experimental evaluations using the CIC-IDS2017 and NSL-KDD datasets demonstrate promising accuracies for BBO-CFAT on both datasets, achieving 99.1% and 97.5% accuracy, respectively, surpassing the performance of comparative experiments.Overall, the BBO-CFAT model provides a comprehensive solution to the challenges of intrusion detection, effectively balancing feature preservation, computational efficiency, and training accuracy.

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Section: Referencementioning

confidence: 99%

BBO-CFAT: Network Intrusion Detection Model Based on BBO Algorithm and Hierarchical Transformer

Jiang,

Fu,

Wang

2024

IEEE Access

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“…In fact, FS is a preprocessing step in which redundant, irrelevant, and ineffective features are omitted because they do not contribute to the accuracy of the data mining model [27,28]. The technique has gained in popularity in recent years for numerous data mining applications in science [29][30][31], engineering [32][33][34], and medical diagnostics [35][36][37]. FS is a binary optimization problem in which the search agents are confined to binary (0, 1) values only, with the length of the vector depending on the number of features in the dataset, and these values correspond to selected and unselected features [7].…”

Section: Introductionmentioning

confidence: 99%

Binary modified Cat and Mouse based Optimizer for medical feature selection: A COVID-19 case study

Parizi

2024

Preprint

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Recent technological advances in medical diagnosis have led to the generation of high-dimensional datasets. The presence of redundant and irrelevant features in these datasets can have adverse effects on the performance of machine learning (ML) methods and reduce the accuracy of their results. Therefore, feature selection (FS), i.e., a popular preprocessing method in ML, is used to select the optimal subsets of features to improve the accuracy of ML methods. This performance enhancement is more crucial while addressing high-dimensional medical issues. Since FS is a multiobjective binary optimization problem, it is necessary to develop efficient FS algorithms. Although metaheuristic algorithms (MAs) have been widely used for FS in medicine, they face different challenges in most applications, e.g., a lack of sufficient effectiveness and scalability to select the most effective features in small and large medical datasets. The cat and mouse-based optimizer (CMBO) is a novel MA based on the natural competitive behavior of cats and mice. Despite its acceptable performance in a variety of problems, the CMBO faces various challenges such as limited exploitation abilities, an unbalanced search mechanism, and high fluctuation in solutions to complex problems, e.g., FS. This paper proposes a modified and binary version of the CMBO called the BMCMBO to enhance the performance in selecting effective features from medical datasets. The BMCMBO involves significant modifications to the method of updating the positions of search agents, the method of selecting mice, the effect of the positional information of the most optimal member of the population, and the addition of the adaptive step size. These modifications are meant to improve the exploitation abilities, boost the accuracy of the solutions, and balance the search process when dealing with the FS problem in medical datasets. The performance of the proposed algorithm on 12 real medical datasets was compared with the performance of the most effective MA and CMBO variants. The statistical results demonstrated that BMCMBO was more effective than other evaluated methods. In addition, the BMCMBO algorithm was employed to select features and diagnose COVID-19 in a real case study. The proposed algorithm identified healthy and infected COVID-19 correctly samples with an accuracy of 98.4\%, demonstrating its superiority.

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“…The entities or objects that are involved within an IoT network system are wirelessly linked using smart tiny sensors. Therefore, IoT devices have the capacity to interact with others without any human intervention [1]. IoT devices perform across numerous environments to achieve various objectives, leading to the development of several computing and communication technologies used in healthcare, military, agriculture, business and education [2].…”

Section: Introductionmentioning

confidence: 99%

Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things

Chalichalamala,

Govindan,

Kasarapu

2023

Sensors

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The Internet of Things (IoT) is a powerful technology that connect its users worldwide with everyday objects without any human interference. On the contrary, the utilization of IoT infrastructure in different fields such as smart homes, healthcare and transportation also raises potential risks of attacks and anomalies caused through node security breaches. Therefore, an Intrusion Detection System (IDS) must be developed to largely scale up the security of IoT technologies. This paper proposes a Logistic Regression based Ensemble Classifier (LREC) for effective IDS implementation. The LREC combines AdaBoost and Random Forest (RF) to develop an effective classifier using the iterative ensemble approach. The issue of data imbalance is avoided by using the adaptive synthetic sampling (ADASYN) approach. Further, inappropriate features are eliminated using recursive feature elimination (RFE). There are two different datasets, namely BoT-IoT and TON-IoT, for analyzing the proposed RFE-LREC method. The RFE-LREC is analyzed on the basis of accuracy, recall, precision, F1-score, false alarm rate (FAR), receiver operating characteristic (ROC) curve, true negative rate (TNR) and Matthews correlation coefficient (MCC). The existing researches, namely NetFlow-based feature set, TL-IDS and LSTM, are used to compare with the RFE-LREC. The classification accuracy of RFE-LREC for the BoT-IoT dataset is 99.99%, which is higher when compared to those of TL-IDS and LSTM.

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A highly secured intrusion detection system for IoT using EXPSO-STFA feature selection for LAANN to detect attacks

Cited by 30 publications

References 31 publications

BBO-CFAT: Network Intrusion Detection Model Based on BBO Algorithm and Hierarchical Transformer

BBO-CFAT: Network Intrusion Detection Model Based on BBO Algorithm and Hierarchical Transformer

Binary modified Cat and Mouse based Optimizer for medical feature selection: A COVID-19 case study

Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things

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