1
Technical Computer Engineering Department, Al-Kunooze University College,Basrah, Iraq
(Tamarah.Alaa@kunoozu.edu.iq)
2
Department of Computer Engineering techniques,National University of science and technology, Thi Qar, Iraq
(kadim.jabber@nust.edu.iq)
3
Medical instruments engineering techniques, Al-farahidi University, Baghdad, Iraq
(Ahmedtaha@uoalfarahidi.edu.iq)
4
Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq
(naseerali@alayen.edu.iq)
5
Computer Technologies Engineering, Al-Turath University College, Baghdad, Iraq
(sura.raheem@turath.edu.iq)
6
Keele University (KU), Keele, United Kingdom, Staffordshire, ST5 5AA
(Almohaiqny@gmail.com)
7
Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India
(v.rajinikanth@ieee.org)
Abstract :
The development of power industries creates impacts on the intelligent power grids. The power grids are more valuable for transmitting information over the network. Several intermediate activities influence the networks, which are interrupted by traffic, creating network security issues. Therefore, the threats highly influence power grids, and the number of attacks also increased gradually. Several conceptual approaches are introduced to overcome the security issues; however, computation complexity is still a significant problem while detecting network anomalies. This research problem is overcome by applying the Organic Combination of Various Deep Learning (OC-VDL) approach. The introduced method observes the industry standards with the help of the Innovative Blockchain Network (IBN). During this process, IBN observes the infrastructure using the communication protocol and Manufacturing Internet of Things (IoT). The collected information is processed with the help of the Intense Autoencoder Classifier Model (IACM), which manages bilateral traffic control and helps predict abnormal activities. The effective prediction of network traffic minimizes the intermediate activities and improves the overall security up to 98.8% accuracy.
Keywords :
Power grids; Network Anomaly Detection; Deep Model; Intense Autoencoder Classifier Model .
References :
[1] Gao, J., Wang, H., & Shen, H. (2020, August). Machine learning-based workload prediction in cloud computing. In 2020 29th international conference on computer communications and Networks (ICCCN) (pp. 1-9). IEEE.
[2] Liu, B. H., Nguyen, N. T., Pham, V. T., & Lin, Y. X. (2017). Novel methods for energy charging and data collection in wireless rechargeable sensor networks. International Journal of Communication Systems, 30(5), e3050.
[3] Jaber, M.M., Ali, M.H., CB, S., Asaad, R.R., Agrawal, R., Bizu, B., and Sanz-Prieto, I., 2023. Future smart grids creation and dimensionality reduction with signal handling on smart grid using targeted projection. Sustainable Computing: Informatics and Systems, 39.
[4] Javed, A. R., Rehman, S. U., Khan, M. U., Alazab, M., & Khan, H. U. (2021). Betalogger: Smartphone sensor-based side-channel attack detection and text inference using language modeling and dense MultiLayer neural network. Transactions on Asian and Low-Resource Language Information Processing, 20(5), 1-17.
[5] A. Lakhan, M.A. Mohammed, K.H.Abdul kareemetal.,Secure blockchain assisted internet of medical things architecture for data fusion enabled cancer workflow, Internet of Things(2023),doi:https://doi.org/10.1016/j.iot.2023.100928.
[6] Hussein, A.F., ALZubaidi, A.K., Habash, Q.A., and Jaber, M.M., 2019. An adaptive biomedical data managing scheme based on the blockchain technique. Applied Sciences (Switzerland), 9(12).
[7] Pallavi Goel,Sarika Chaudhary, Maximizing Anomaly Detection Performance in Next-Generation Networks, Journal of Cybersecurity and Information Management, Vol. 12 , No. 2 , (2023) : 36-51` (Doi : https://doi.org/10.54216/JCIM.120203)
[8] Abidi, M. H., Alkhalefah, H., Moiduddin, K., Alazab, M., Mohammed, M. K., Ameen, W., &Gadekallu, T. R. (2021). Optimal 5G network slicing using machine learning and deep learning concepts. Computer Standards & Interfaces, 76, 103518.
[9] Hammad, M., Alkinani, M. H., Gupta, B. B., &Abd El-Latif, A. A. (2021). Myocardial infarction detection based on deep neural network on imbalanced data. Multimedia Systems, 1-13.
[10] Naeem, M. A., Nguyen, T. N., Ali, R., Cengiz, K., Meng, Y., &Khurshaid, T. (2021). Hybrid Cache Management in IoT-based Named Data Networking. IEEE Internet of Things Journal.
[11] Billah, M. F. R. M., Saoda, N., Gao, J., & Campbell, B. (2021, May). BLE Can See: A Reinforcement Learning Approach for RF-based Indoor Occupancy Detection. In Proceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021) (pp. 132-147).
[12] Hussein, A.F., ALZubaidi, A.K., Habash, Q.A., and Jaber, M.M., 2019. An adaptive biomedical data managing scheme based on the blockchain technique. Applied Sciences (Switzerland), 9(12).
[13] Guo, Z., Tang, L., Guo, T., Yu, K., Alazab, M., &Shalaginov, A. (2021). Deep graph neural network-based spammer detection under the perspective of heterogeneous cyberspace. Future Generation Computer Systems, 117, 205-218.
[14] Sedik, A., Hammad, M., Abd El-Latif, A. A., El-Banby, G. M., Khalaf, A. A., Abd El-Samie, F. E., &Iliyasu, A. M. (2021). Deep Learning Modalities for Biometric Alteration Detection in 5G Networks-Based Secure Smart Cities. IEEE Access, 9, 94780-94788.
[15] Nguyen, N. T., Liu, B. H., Chu, S. I., &Weng, H. Z. (2018). Challenges, designs, and performances of a distributed algorithm for minimum-latency of data-aggregation in multi-channel WSNs. IEEE Transactions on Network and Service Management, 16(1), 192-205.
[16] Waleed Abd Elkhalik,Ibrahim Elhenawy, Semi-supervised Transformer Network for Anomaly Detection in Cellular Internet of Things, International Journal of Wireless and Ad Hoc Communication, Vol. 4 , No. 1 , (2022) : 56-68 (Doi : https://doi.org/10.54216/IJWAC.040106).
[17] Asghar, M. Z., Subhan, F., Ahmad, H., Khan, W. Z., Hakak, S., Gadekallu, T. R., &Alazab, M. (2021). Senti‐eSystem: A sentiment‐based eSystem‐using hybridized fuzzy and deep neural network for measuring customer satisfaction. Software: Practice and Experience, 51(3), 571-594.
[18] Khan, W. U., Javed, M. A., Nguyen, T. N., Khan, S., &Elhalawany, B. M. (2021). Energy-efficient resource allocation for 6G backscatter-enabled NOMA IoV networks. IEEE Transactions on Intelligent Transportation Systems.
[19] Rauf, H. T., Gao, J., Almadhor, A., Arif, M., &Nafis, M. T. (2021). Enhanced bat algorithm for COVID-19 short-term forecasting using optimized LSTM. Soft Computing, 1-11.
[20] Rezaee, K., Savarkar, S., Yu, X., & Zhang, J. (2022). A hybrid deep transfer learning-based approach for Parkinson's disease classification in surface electromyography signals. Biomedical Signal Processing and Control, 71, 103161.
[21] Rehman, A., Rehman, S. U., Khan, M., Alazab, M., & Reddy, T. (2021). CANintelliIDS: detecting in-vehicle intrusion attacks on a controller area network using CNN and attention-based GRU. IEEE Transactions on Network Science and Engineering.
[22] Saeed, V. A. (2024). A Framework for Recognition of Facial Expression Using HOG Features. International Journal of Mathematics, Statistics, and Computer Science, 2, 1–8. https://doi.org/10.59543/ijmscs.v2i.7815
[23] Salman, O. H., Taha, Z., Alsabah, M. Q., Hussein, Y. S., Mohammed, A. S., & Aal-Nouman, M. (2021). A review on utilising machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work. Computer Methods and Programs in Biomedicine, 209, 106357.
[24] Xie, G., Yang, L. T., Yang, Y., Luo, H., Li, R., &Alazab, M. (2021). Threat Analysis for Automotive CAN Networks: A GAN Model-Based Intrusion Detection Technique. IEEE Transactions on Intelligent Transportation Systems.
[25] Zhang, X., & Wang, Y. (2021). Research on intelligent medical big data system based on Hadoop and blockchain. EURASIP Journal on Wireless Communications and Networking, 2021(1), 1-21.
[26] Ghanbari Ghoushchi, N. ., Ahmadzadeh, K., & Jafarzadeh Ghoushchi, S. (2023). A New Extended Approach to Reduce Admission Time in Hospital Operating Rooms Based on the FMEA Method in an Uncertain Environment. Journal of Soft Computing and Decision Analytics, 1(1), 80-101. https://doi.org/10.31181/jscda11202310
[27] Demertzis, K., Iliadis, L., Tziritas, N., &Kikiras, P. (2020). Anomaly detection via blockchained deep learning smart contracts in industry 4.0. Neural Computing and Applications, 32(23), 17361-17378.
[28] Ahmed Abdelmonem, Nehal N. Mostafa, Interpretable Machine Learning Fusion and Data Analytics Models for Anomaly Detection, Fusion: Practice and Applications, Vol. 3 , No. 1 , (2021) : 54-69 (Doi : https://doi.org/10.54216/FPA.030104)
[29] Hou, R., Pan, M., Zhao, Y., & Yang, Y. (2019). Image anomaly detection for IoT equipment based on deep learning. Journal of Visual Communication and Image Representation, 64, 102599.
[30] Garcia-Font, V., Garrigues, C., &Rifà-Pous, H. (2018). Difficulties and challenges of anomaly detection in smart cities: A laboratory analysis. Sensors, 18(10), 3198.
[31] Nayak, R., Pati, U. C., & Das, S. K. (2020). A comprehensive review on deep learning-based methods for video anomaly detection. Image and Vision Computing, 104078.
| Style | # |
|---|---|
| MLA | Tamarah Alaa Diame, Kadim A. Jabbar, Ahmed Taha, Naseer Ali Hussien, Sura Rahim Alatba, Mohammed Nasser Al-Mhiqani, Venkatesan Rajinikanth. "Anomaly Detection in Complex Power Grid using Organic Combination of Various Deep Learning (OC-VDL)." Journal of Intelligent Systems and Internet of Things, Vol. 9, No. 2, 2023 ,PP. 78-92 (Doi : https://doi.org/10.54216/JISIoT.090206) |
| APA | Tamarah Alaa Diame, Kadim A. Jabbar, Ahmed Taha, Naseer Ali Hussien, Sura Rahim Alatba, Mohammed Nasser Al-Mhiqani, Venkatesan Rajinikanth. (2023). Anomaly Detection in Complex Power Grid using Organic Combination of Various Deep Learning (OC-VDL). Journal of Journal of Intelligent Systems and Internet of Things, 9 ( 2 ), 78-92 (Doi : https://doi.org/10.54216/JISIoT.090206) |
| Chicago | Tamarah Alaa Diame, Kadim A. Jabbar, Ahmed Taha, Naseer Ali Hussien, Sura Rahim Alatba, Mohammed Nasser Al-Mhiqani, Venkatesan Rajinikanth. "Anomaly Detection in Complex Power Grid using Organic Combination of Various Deep Learning (OC-VDL)." Journal of Journal of Intelligent Systems and Internet of Things, 9 no. 2 (2023): 78-92 (Doi : https://doi.org/10.54216/JISIoT.090206) |
| Harvard | Tamarah Alaa Diame, Kadim A. Jabbar, Ahmed Taha, Naseer Ali Hussien, Sura Rahim Alatba, Mohammed Nasser Al-Mhiqani, Venkatesan Rajinikanth. (2023). Anomaly Detection in Complex Power Grid using Organic Combination of Various Deep Learning (OC-VDL). Journal of Journal of Intelligent Systems and Internet of Things, 9 ( 2 ), 78-92 (Doi : https://doi.org/10.54216/JISIoT.090206) |
| Vancouver | Tamarah Alaa Diame, Kadim A. Jabbar, Ahmed Taha, Naseer Ali Hussien, Sura Rahim Alatba, Mohammed Nasser Al-Mhiqani, Venkatesan Rajinikanth. Anomaly Detection in Complex Power Grid using Organic Combination of Various Deep Learning (OC-VDL). Journal of Journal of Intelligent Systems and Internet of Things, (2023); 9 ( 2 ): 78-92 (Doi : https://doi.org/10.54216/JISIoT.090206) |
| IEEE | Tamarah Alaa Diame, Kadim A. Jabbar, Ahmed Taha, Naseer Ali Hussien, Sura Rahim Alatba, Mohammed Nasser Al-Mhiqani, Venkatesan Rajinikanth, Anomaly Detection in Complex Power Grid using Organic Combination of Various Deep Learning (OC-VDL), Journal of Journal of Intelligent Systems and Internet of Things, Vol. 9 , No. 2 , (2023) : 78-92 (Doi : https://doi.org/10.54216/JISIoT.090206) |