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Chintapalli SSN, Singh SP, Frnda J, Bidare Divakarachari P, Sarraju VL, Falkowski-Gilski P. OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems. Heliyon 2024; 10:e29410. [PMID: 38644823 PMCID: PMC11031752 DOI: 10.1016/j.heliyon.2024.e29410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 03/16/2024] [Accepted: 04/08/2024] [Indexed: 04/23/2024] Open
Abstract
Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered a challenging task because of inappropriate features existing in the input data and the slow training process. In order to address these issues, an effective meta heuristic based feature selection and deep learning techniques are developed for enhancing the IDS. The Osprey Optimization Algorithm (OOA) based feature selection is proposed for selecting the highly informative features from the input which leads to an effective differentiation among the normal and attack traffic of network. Moreover, the traditional sigmoid and tangent activation functions are replaced with the Exponential Linear Unit (ELU) activation function to propose the modified Bi-directional Long Short Term Memory (Bi-LSTM). The modified Bi-LSTM is used for classifying the types of intrusion attacks. The ELU activation function makes gradients extremely large during back-propagation and leads to faster learning. This research is analysed in three different datasets such as N-BaIoT, Canadian Institute for Cybersecurity Intrusion Detection Dataset 2017 (CICIDS-2017), and ToN-IoT datasets. The empirical investigation states that the proposed framework obtains impressive detection accuracy of 99.98 %, 99.97 % and 99.88 % on the N-BaIoT, CICIDS-2017, and ToN-IoT datasets, respectively. Compared to peer frameworks, this framework obtains high detection accuracy with better interpretability and reduced processing time.
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Affiliation(s)
| | - Satya Prakash Singh
- Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India
| | - Jaroslav Frnda
- Department of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, 01026, Zilina, Slovakia
- Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, 70800, Ostrava, Czech Republic
| | - Parameshachari Bidare Divakarachari
- Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bengaluru 560064, Visvesvaraya Technological University, Belagavi, India
| | - Vijaya Lakshmi Sarraju
- Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India
| | - Przemysław Falkowski-Gilski
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233, Gdansk, Poland
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Vaduganathan L, Neware S, Falkowski-Gilski P, Divakarachari PB. Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks. Entropy (Basel) 2023; 25:1285. [PMID: 37761584 PMCID: PMC10528549 DOI: 10.3390/e25091285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 08/21/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023]
Abstract
The rapid advancement of wireless communication combined with insufficient spectrum exploitation opens the door for the expansion of novel wireless services. Cognitive radio network (CRN) technology makes it possible to periodically access the open spectrum bands, which in turn improves the effectiveness of CRNs. Spectrum sensing (SS), which allows unauthorized users to locate open spectrum bands, plays a fundamental part in CRNs. A precise approximation of the power spectrum is essential to accomplish this. On the assumption that each SU's parameter vector contains some globally and partially shared parameters, spectrum sensing is viewed as a parameter estimation issue. Distributed and cooperative spectrum sensing (CSS) is a key component of this concept. This work introduces a new component-specific cooperative spectrum sensing model (CSCSSM) in CRNs considering the amplitude and phase components of the input signal including Component Specific Adaptive Estimation (CSAE) for mean squared deviation (MSD) formulation. The proposed concept ensures minimum information loss compared to the traditional methods that consider error calculation among the direct signal vectors. The experimental results and performance analysis prove the robustness and efficiency of the proposed work over the traditional methods.
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Affiliation(s)
- Lakshminarayanan Vaduganathan
- Department of Electrical and Electronics Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi 642003, India;
| | - Shubhangi Neware
- Department of Computer Science and Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur 440013, India;
| | - Przemysław Falkowski-Gilski
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland
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Chelladurai A, Narayan DL, Divakarachari PB, Loganathan U. fMRI-Based Alzheimer's Disease Detection Using the SAS Method with Multi-Layer Perceptron Network. Brain Sci 2023; 13:893. [PMID: 37371371 DOI: 10.3390/brainsci13060893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/24/2023] [Accepted: 05/30/2023] [Indexed: 06/29/2023] Open
Abstract
In the present scenario, Alzheimer's Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD. However, analyzing complex brain structures in fMRI is a time-consuming and complex task; so, a novel automated model was proposed in this manuscript for early diagnosis of AD using fMRI images. Initially, the fMRI images are acquired from an online dataset: Alzheimer's Disease Neuroimaging Initiative (ADNI). Further, the quality of the acquired fMRI images was improved by implementing a normalization technique. Then, the Segmentation by Aggregating Superpixels (SAS) method was implemented for segmenting the brain regions (AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC)) from the denoised fMRI images. From the segmented brain regions, feature vectors were extracted by employing Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques. The obtained feature vectors were dimensionally reduced by implementing Honey Badger Optimization Algorithm (HBOA) and fed to the Multi-Layer Perceptron (MLP) model for classifying the fMRI images as AD, NC, MCI, EMCI, LMCI, and SMC. The extensive investigation indicated that the presented model attained 99.44% of classification accuracy, 88.90% of Dice Similarity Coefficient (DSC), 90.82% of Jaccard Coefficient (JC), and 88.43% of Hausdorff Distance (HD). The attained results are better compared with the conventional segmentation and classification models.
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Affiliation(s)
- Aarthi Chelladurai
- Department of Electronics and Communication Engineering, Sengunthar Engineering College, Tiruchengode 637205, Tamil Nadu, India
| | - Dayanand Lal Narayan
- Department of Computer Science Engineering, GITAM School of Technology, GITAM University, Bengaluru 561203, Karnataka, India
| | | | - Umasankar Loganathan
- Department of Electrical and Electronics Engineering, S.A. Engineering College, Chennai 600077, Tamilnadu, India
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Natarajan R, Megharaj G, Marchewka A, Divakarachari PB, Hans MR. Energy and Distance Based Multi-Objective Red Fox Optimization Algorithm in Wireless Sensor Network. Sensors 2022; 22:s22103761. [PMID: 35632170 PMCID: PMC9144256 DOI: 10.3390/s22103761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/07/2022] [Accepted: 05/12/2022] [Indexed: 02/01/2023]
Abstract
In modern trends, wireless sensor networks (WSNs) are interesting, and distributed in the environment to evaluate received data. The sensor nodes have a higher capacity to sense and transmit the information. A WSN contains low-cost, low-power, multi-function sensor nodes, with limited computational capabilities, used for observing environmental constraints. In previous research, many energy-efficient routing methods were suggested to improve the time of the network by minimizing energy consumption; sometimes, the sensor nodes run out of power quickly. The majority of recent articles present various methods aimed at reducing energy usage in sensor networks. In this paper, an energy-efficient clustering/routing technique, called the energy and distance based multi-objective red fox optimization algorithm (ED-MORFO), was proposed to reduce energy consumption. In each communication round of transmission, this technique selects the cluster head (CH) with the most residual energy, and finds the optimal routing to the base station. The simulation clearly shows that the proposed ED-MORFO achieves better performance in terms of energy consumption (0.46 J), packet delivery ratio (99.4%), packet loss rate (0.6%), end-to-end delay (11 s), routing overhead (0.11), throughput (0.99 Mbps), and network lifetime (3719 s), when compared with existing MCH-EOR and RDSAOA-EECP methods.
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Affiliation(s)
- Rajathi Natarajan
- Department of Information Technology, Kumaraguru College of Technology, Coimbatore 641049, India;
| | - Geetha Megharaj
- Department of Artificial Intelligence and Machine Learning, Sri Krishna Institute of Technology, Bangalore 560090, India;
| | - Adam Marchewka
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796 Bydgoszcz, Poland
- Correspondence:
| | | | - Manoj Raghubir Hans
- Department of ECE, School of Engineering, MITADT University, Pune 412201, India;
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Kumar TM, Balmuri KR, Marchewka A, Bidare Divakarachari P, Konda S. Implementation of Speed-Efficient Key-Scheduling Process of AES for Secure Storage and Transmission of Data. Sensors (Basel) 2021; 21:s21248347. [PMID: 34960447 PMCID: PMC8706429 DOI: 10.3390/s21248347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 12/08/2021] [Accepted: 12/10/2021] [Indexed: 11/16/2022]
Abstract
Nowadays, a large number of digital data are transmitted worldwide using wireless communications. Therefore, data security is a significant task in communication to prevent cybercrimes and avoid information loss. The Advanced Encryption Standard (AES) is a highly efficient secure mechanism that outperforms other symmetric key cryptographic algorithms using message secrecy. However, AES is efficient in terms of software and hardware implementation, and numerous modifications are done in the conventional AES architecture to improve the performance. This research article proposes a significant modification to the AES architecture’s key expansion section to increase the speed of producing subkeys. The fork–join model of key expansion (FJMKE) architecture is developed to improve the speed of the subkey generation process, whereas the hardware resources of AES are minimized by avoiding the frequent computation of secret keys. The AES-FJMKE architecture generates all of the required subkeys in less than half the time required by the conventional architecture. The proposed AES-FJMKE architecture is designed and simulated using the Xilinx ISE 5.1 software. The Field Programmable Gate Arrays (FPGAs) behaviour of the AES-FJMKE architecture is analysed by means of performance count for hardware resources, delay, and operating frequency. The existing AES architectures such as typical AES, AES-PNSG, AES-AT, AES-BE, ISAES, AES-RS, and AES-MPPRM are used to evaluate the efficiency of AES-FJMKE. The AES-FJMKE implemented using Spartan 6 FPGA used fewer slices (i.e., 76) than the AES-RS.
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Affiliation(s)
- Thanikodi Manoj Kumar
- Department of Electronics and Communication Engineering, Karpagam Institute of Technology, Coimbatore 641105, Tamil Nadu, India;
| | - Kavitha Rani Balmuri
- Department of Information Technology, CMR Technical Campus, Hyderabad 501401, Telangana, India;
| | - Adam Marchewka
- Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796 Bydgoszcz, Poland
- Correspondence:
| | | | - Srinivas Konda
- Department of Computer Science Engineering, CMR Technical Campus, Kandlakoya, Hyderabad 501401, India;
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Sumathi AC, Akila M, Pérez de Prado R, Wozniak M, Divakarachari PB. Dynamic Bargain Game Theory in the Internet of Things for Data Trustworthiness. Sensors (Basel) 2021; 21:s21227611. [PMID: 34833686 PMCID: PMC8621105 DOI: 10.3390/s21227611] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 11/16/2022]
Abstract
Smart home and smart building systems based on the Internet of Things (IoT) in smart cities currently suffer from security issues. In particular, data trustworthiness and efficiency are two major concerns in Internet of Things (IoT)-based Wireless Sensor Networks (WSN). Various approaches, such as routing methods, intrusion detection, and path selection, have been applied to improve the security and efficiency of real-time networks. Path selection and malicious node discovery provide better solutions in terms of security and efficiency. This study proposed the Dynamic Bargaining Game (DBG) method for node selection and data transfer, to increase the data trustworthiness and efficiency. The data trustworthiness and efficiency are considered in the Pareto optimal solution to select the node, and the bargaining method assigns the disagreement measure to the nodes to eliminate the malicious nodes from the node selection. The DBG method performs the search process in a distributed manner that helps to find an effective solution for the dynamic networks. In this study, the data trustworthiness was measured based on the node used for data transmission and throughput was measured to analyze the efficiency. An SF attack was simulated in the network and the packet delivery ratio was measured to test the resilience of the DBG and existing methods. The results of the packet delivery ratio showed that the DBG method has higher resilience than the existing methods in a dynamic network. Moreover, for 100 nodes, the DBG method has higher data trustworthiness of 98% and throughput of 398 Mbps, whereas the existing fuzzy cross entropy method has data trustworthiness of 94% and a throughput of 334 Mbps.
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Affiliation(s)
- Appasamy C. Sumathi
- Department of CSE, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu 641407, India; (A.C.S.); (M.A.)
| | - Muthuramalingam Akila
- Department of CSE, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu 641407, India; (A.C.S.); (M.A.)
| | - Rocío Pérez de Prado
- Telecommunication Engineering Department, University of Jaén, 23071 Linares, Spain
- Correspondence: (R.P.d.P.); (M.W.)
| | - Marcin Wozniak
- Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
- Correspondence: (R.P.d.P.); (M.W.)
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