CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks

Alabsi, Basim Ahmad; Anbar, Mohammed; Rihan, Shaza Dawood Ahmed

doi:10.3390/s23146507

Cited by 27 publications

(13 citation statements)

References 44 publications

(54 reference statements)

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“…The results of the proposed ASB-IB and LW-PWECC methods are implemented. Several existing classifiers like Dual CNN [44], LSTM, Spiking Neural Network (SNN), Binarized Spiking Neural Network (BSNN) [46][59] and encryption methods like Identity-Based Encryption (IBE) [60], Advanced Encryption Standard (AES) [63], Rivest-Shamir-Adleman (RSA) [64] and ECC [63] are taken to compare the performance of the introduced approach.…”

Section: Resultsmentioning

confidence: 99%

LW-PWECC: Cryptographic Framework of Attack Detection and Secure Data Transmission in IoT

Ranjith,

Mahantesh,

Abhilash

2024

Journal of Robotics and Control

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In the present era, the number of Internet of Health Things (IoHT) devices and applications has drastically expanded. Security and attack are major issues in the IoHT domain because of the nature of its architecture and sorts of devices. Over the recent few years, network attacks have dramatically increased. Many detection and encryption techniques are existing however they lack accuracy, training stability, insecurity, delay etc. By the above concerns, this manuscript introduces a novel deep learning technique called Agnostic Spiking Binarized neural network with Improved Billiards optimization for accurate detection of network attacks and Light Weight integrated Puzzle War Elliptic Curve Cryptographic framework for secure data transmission with high security and minimal delay. Optimal features from the datasets are selected by volcano eruption optimization algorithm with better convergence for reducing the overall processing time. Wilcoxon Rank Sum and Mc Neymar’s tests are performed for proving the statistical analyses. The outcomes show that the introduced approach performs with an overall accuracy of 99.93% which is better than the previous techniques demonstrating the effectiveness.

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Section: Resultsmentioning

confidence: 99%

LW-PWECC: Cryptographic Framework of Attack Detection and Secure Data Transmission in IoT

Ranjith,

Mahantesh,

Abhilash

2024

Journal of Robotics and Control

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“…By iteratively exploring diverse feature subsets and evaluating their performance, wrapper methods ascertain the most informative combination of features [26]. An IDS is designed with dual CNN, the former to select the attributes and the later for classification [27]. The FPR of this method is 1.9% for BoT IoT 2020 dataset.…”

Section: Related Workmentioning

confidence: 99%

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks

Sundaram,

Natarajan,

Perumalsamy

et al. 2024

Wireless Communications and Mobile Computing

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The rapid growth of the Internet of Things (IoT) has created a situation where a huge amount of sensitive data is constantly being created and sent through many devices, making data security a top priority. In the complex network of IoT, detecting intrusions becomes a key part of strengthening security. Since IoT environments can be easily affected by a wide range of cyber threats, intrusion detection systems (IDS) are crucial for quickly finding and dealing with potential intrusions as they happen. IDS datasets can have a wide range of features, from just a few to several hundreds or even thousands. Managing such large datasets is a big challenge, requiring a lot of computer power and leading to long processing times. To build an efficient IDS, this article introduces a combined feature selection strategy using recursive feature elimination and information gain. Then, a cascaded long–short-term memory is used to improve attack classifications. This method achieved an accuracy of 98.96% and 99.30% on the NSL-KDD and UNSW-NB15 datasets, respectively, for performing binary classification. This research provides a practical strategy for improving the effectiveness and accuracy of intrusion detection in IoT networks.

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“…Feature selection reduces the large feature sets to the most significant features by minimizing the data’s dimensions. This step is critical for optimizing diagnostic efficiency with respect to predictive accuracy, learning time and storage needs [ 49 ]. Therefore, feature-selection research is considered one of the most productive and active fields of machine learning applications [ 50 ] with many feature-selection methods proposed in the last few decades [ 41 , 42 ].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Diagnosis of Rotating Elements in Roll-to-Roll Manufacturing Systems through Feature Selection Approach Considering Overlapping Data Density and Distance Analysis

Lee,

et al. 2023

Sensors

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Roll-to-roll manufacturing systems have been widely adopted for their cost-effectiveness, eco-friendliness, and mass-production capabilities, utilizing thin and flexible substrates. However, in these systems, defects in the rotating components such as the rollers and bearings can result in severe defects in the functional layers. Therefore, the development of an intelligent diagnostic model is crucial for effectively identifying these rotating component defects. In this study, a quantitative feature-selection method, feature partial density, to develop high-efficiency diagnostic models was proposed. The feature combinations extracted from the measured signals were evaluated based on the partial density, which is the density of the remaining data excluding the highest class in overlapping regions and the Mahalanobis distance by class to assess the classification performance of the models. The validity of the proposed algorithm was verified through the construction of ranked model groups and comparison with existing feature-selection methods. The high-ranking group selected by the algorithm outperformed the other groups in terms of training time, accuracy, and positive predictive value. Moreover, the top feature combination demonstrated superior performance across all indicators compared to existing methods.

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CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks

Cited by 27 publications

References 44 publications

LW-PWECC: Cryptographic Framework of Attack Detection and Secure Data Transmission in IoT

LW-PWECC: Cryptographic Framework of Attack Detection and Secure Data Transmission in IoT

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks

Enhancing Diagnosis of Rotating Elements in Roll-to-Roll Manufacturing Systems through Feature Selection Approach Considering Overlapping Data Density and Distance Analysis

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