Automatic Building of a Powerful IDS for The Cloud Based on Deep Neural Network by Using a Novel Combination of Simulated Annealing Algorithm and Improved Self- Adaptive Genetic Algorithm

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Zouhair Chiba
Moulay Seddiq El Kasmi Alaoui
Noreddine Abghour
Khalid Moussaid


Cloud computing (CC) is the fastest-growing data hosting and computational technology that stands today as a satisfactory answer to the problem of data storage and computing. Thereby, most organizations are now migratingtheir services into the cloud due to its appealing features and its tangible advantages. Nevertheless, providing privacy and security to protect cloud assets and resources still a very challenging issue. To address the aboveissues, we propose a smart approach to construct automatically an efficient and effective anomaly network IDS based on Deep Neural Network, by using a novel hybrid optimization framework “ISAGASAA”. ISAGASAA framework combines our new self-adaptive heuristic search algorithm called “Improved Self-Adaptive Genetic Algorithm” (ISAGA) and Simulated Annealing Algorithm (SAA). Our approach consists of using ISAGASAA with the aim of seeking the optimal or near optimal combination of most pertinent values of the parametersincluded in building of DNN based IDS or impacting its performance, which guarantee high detection rate, high accuracy and low false alarm rate. The experimental results turn out the capability of our IDS to uncover intrusionswith high detection accuracy and low false alarm rate, and demonstrate its superiority in comparison with stateof-the-art methods.

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Chiba, Z., El Kasmi Alaoui, M. S., Abghour, N., & Moussaid, K. (2022). Automatic Building of a Powerful IDS for The Cloud Based on Deep Neural Network by Using a Novel Combination of Simulated Annealing Algorithm and Improved Self- Adaptive Genetic Algorithm. International Journal of Communication Networks and Information Security (IJCNIS), 14(1). (Original work published April 12, 2022)
Research Articles
Author Biographies

Zouhair Chiba, Faculty of Sciences, Hassan II University of Casablanca, Casablanca, Morocco

Department of Mathematics and Computers, LIS Labs

Moulay Seddiq El Kasmi Alaoui, Faculty of Sciences, Hassan II University of Casablanca, Casablanca, Morocco

Department of Mathematics and Computers, LIS Labs

Noreddine Abghour, Faculty of Sciences, Hassan II University of Casablanca, Casablanca, Morocco

Department of Mathematics and Computers, LIS Labs

Khalid Moussaid, Faculty of Sciences, Hassan II University of Casablanca, Casablanca, Morocco

Department of Mathematics and Computers, LIS Labs


Ravji, S., & Ali, M. (2018, August). Integrated Intrusion Detection and Prevention System with Honeypot in Cloud Computing. In 2018 International Conference on Computing, Electronics & Communications Engineering (ICCECE) (pp. 95-100). IEEE. doi: 10.1109/iCCECOME.2018.8658593.

Mell, P., & Grance, T. (2011). The NIST definition of cloud computing.

Brunette, G., Mogull, R., et al. Security guidance for critical areas of focus in cloud computing v2. 1. Cloud Secure Alliance. 2009:1–76.

Sakr, M. M., Tawfeeq, M. A., & El-Sisi, A. B. (2019). Network Intrusion Detection System based PSO-SVM for Cloud Computing. International Journal of Computer Network and Information Security, 11(3), 22-29. doi: 10.5815/ijcnis.2019.03.04.

Nathiya, T., & Suseendran, G. (2019). An Effective Hybrid Intrusion Detection System for Use in Security Monitoring in the Virtual Network Layer of Cloud Computing Technology. In Data Management, Analytics and Innovation (pp. 483-497). Springer, Singapore. doi: 10.1007/978-981-13-1274-8_36.

ORACLE and KPMG Enterprises. ORACLE and KPMG Cloud Threat Report 2020; 2020. Available from: [Accessed 08 August 2021].

Khatibzadeh, L., Bornaee, Z., & Ghaemi Bafghi, A. (2019). Applying Catastrophe Theory for Network Anomaly Detection in Cloud Computing Traffic. Security and Communication Networks, 2019. doi: 10.1155/2019/5306395.

Symantec Enterprise. 2019 Internet Security Threat Report; 2019. Available from: [Accessed 08 August 2021].

Netskope. 2020 Netskope Cloud and Threat Report- July 2021; 2021. Available from: [Accessed 08 August 2021].

Modi, C., Patel, D., Borisaniya, B., Patel, H., Patel, A., & Rajarajan, M. (2013). A survey of intrusion detection techniques in cloud. Journal of network and computer applications, 36(1), 42-57. doi: 10.1016/j.jnca.2012.05.003.

Gao, Y., Liu, Y., Jin, Y., Chen, J., & Wu, H. (2018). A Novel Semi-Supervised Learning Approach for Network Intrusion Detection on Cloud-Based Robotic System. IEEE Access, 6, 50927-50938. doi : 10.1109/ACCESS.2018.2868171.

Ghosh, P., Karmakar, A., Sharma, J., & Phadikar, S. (2019). CS-PSO based Intrusion Detection System in Cloud Environment. In Emerging Technologies in Data Mining and Information Security (pp. 261-269). Springer, Singapore. doi : 10.1007/978-981-13-1951-8_24.

Idhammad, M., Afdel, K., & Belouch, M. (2018). Distributed Intrusion Detection System for Cloud Environments based on Data Mining techniques. Procedia Computer Science, 127, 35-41. doi : 10.1016/j.procs.2018.01.095.

Mehibs, S. M., Hashim, S. H. (2018). Proposed Network Intrusion Detection System In Cloud Environment Based on Back Propagation Neural Network. Journal of University of Babylon for Pure and Applied Sciences, Vol. 26, No. 1, pp. 29-40.

Yassin, W., Udzir, N. I., Muda, Z., Abdullah, A., & Abdullah, M. T. (2012, June). A cloud-based intrusion detection service framework. In Proceedings Title: 2012 International Conference on Cyber Security, Cyber Warfare and Digital Forensic (CyberSec) (pp. 213-218). IEEE. doi: 10.1109/CyberSec.2012.6246098.

Wu, S. X., & Banzhaf, W. (2010). The use of computational intelligence in intrusion detection systems: A review. Applied soft computing, 10(1), 1-35. doi: 10.1016/j.asoc.2009.06.019.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).

Chiba, Z., Abghour, N., Moussaid, K., El Omri, A., & Rida, M. (2018). A novel architecture combined with optimal parameters for back propagation neural networks applied to anomaly network intrusion detection. Computers & Security, 75, 36-58. doi : 10.1016/j.cose.2018.01.023.

Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A. R., Jaitly, N., ... & Sainath, T. (2012). Deep neural networks for acoustic modeling in speech recognition. IEEE Signal processing magazine, 29. doi: 10.1109/MSP.2012.2205597.

Jacobson, L., Kanbe, B. (2015). Genetic algorithms in Java basics. Apress, New York, USA.

Kim, D. E., & Gofman, M. (2018, January). Comparison of shallow and deep neural networks for network intrusion

detection. In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 204-208). IEEE. doi: 10.1109/CCWC.2018.8301755.

Woo, J. H., Song, J. Y., & Choi, Y. J. (2019, February). Performance Enhancement of Deep Neural Network Using

Feature Selection and Preprocessing for Intrusion Detection. In 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (pp. 415-417). IEEE. doi: 10.1109/ICAIIC.2019.8668995.

Sheela, K. G., & Deepa, S. N. (2013). Review on methods to fix number of hidden neurons in neural networks. Mathematical Problems in Engineering, 2013. doi: 10.1155/2013/425740.

Zhang, Z., Zhang, G., Shen, Y., & Zhu, Y. (2019, July). Intrusion Detection Model Based on GA Dimension Reduction and MEA-Elman Neural Network. In International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (pp. 354-365). Springer, Cham. doi: 10.1007/978-3-319-93554-6_33.

Ghanshala, K. K., Mishra, P., Joshi, R. C., & Sharma, S. (2018, December). BNID: A Behavior-based Network Intrusion Detection at Network-Layer in Cloud Environment. In 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) (pp. 100-105). IEEE. doi: 10.1109/ICSCCC.2018.8703265.

Shyla, S. I., & Sujatha, S. S. (2019). Cloud Security: LKM and Optimal Fuzzy System for Intrusion Detection in Cloud Environment. Journal of Intelligent Systems, 29(1), 1626-1642. doi: 10.1515/jisys-2018-0479.

Ibrahim, N. M., & Zainal, A. (2020). A Distributed Intrusion Detection Scheme for Cloud Computing. International

Journal of Distributed Systems and Technologies (IJDST), 11(1), 68-82. doi: 10.4018/IJDST.2020010106.

Rabbani, M., Wang, Y. L., Khoshkangini, R., Jelodar, H., Zhao, R., & Hu, P. (2020). A hybrid machine learning approach for malicious behaviour detection and recognition in cloud computing. Journal of Network and Computer

Applications, 151, 102507. doi: 10.1016/j.jnca.2019.102507.

Neha, N., Raman, M. G., Somu, N., Senthilnathan, R., & Sriram, V. S. (2020). An Improved Feedforward Neural Network Using Salp Swarm Optimization Technique for the Design of Intrusion Detection System for Computer Network. In Computational Intelligence in Pattern Recognition (pp. 867-875). Springer, Singapore. doi: 10.1007/978-981-13-9042-5_74.

Krishnaveni, S., Vigneshwar, P., Kishore, S., Jothi, B., & Sivamohan, S. (2020). Anomaly-Based Intrusion Detection

System Using Support Vector Machine. In Artificial Intelligence and Evolutionary Computations in Engineering Systems (pp. 723-731). Springer, Singapore. doi: 10.1007/978-981-15-0199-9_62.

Abirami, M. S., Yash, U., & Singh, S. (2020). Building an Ensemble Learning Based Algorithm for Improving Intrusion Detection System. In Artificial Intelligence and Evolutionary Computations in Engineering Systems (pp. 635-649). Springer, Singapore. doi: 10.1007/978-981-15-0199-9_55.

Thilagam, T., & Aruna, R. (2021). Intrusion detection for network based cloud computing by custom RC-NN and optimization. ICT Express. doi :10.1016/j.icte.2021.04.006

Sobin Soniya, S., & Maria Celestin Vigila, S. (2021). Feedback deer hunting optimization algorithm for intrusion detection in cloud based deep residual network. International Journal of Modeling, Simulation, and Scientific Computing, 2150047. doi :10.1142/S1793962321500471.

Sayed, S., Nassef, M., Badr, A., & Farag, I. (2019). A Nested Genetic Algorithm for feature selection in high-dimensional cancer Microarray datasets. Expert Systems with Applications, 121, 233-243. doi: 10.1016/j.eswa.2018.12.022.

Pereira, R., & Aelenei, L. (2019). Optimization assessment of the energy performance of a BIPV/T-PCM system using Genetic Algorithms. Renewable Energy, 137, 157-166. doi: 10.1016/j.renene.2018.06.118.

Kendall, G., AI methods simulated annealing, Nottingham University. Available from: [Accessed 08 August 2021].

Rere, L. R., Fanany, M. I., & Arymurthy, A. M. (2015). Simulated annealing algorithm for deep learning. Procedia Computer Science, 72, 137-144. doi: 10.1016/j.procs.2015.12.114.

Du, K. L., & Swamy, M. N. S. (2016). Search and optimization by metaheuristics. Techniques and Algorithms Inspired by Nature; Birkhauser: Basel, Switzerland.

Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The journal of chemical physics, 21(6), 1087-1092. doi: 10.1063/1.1699114.

Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680.

Suman, B., & Kumar, P. (2006). A survey of simulated annealing as a tool for single and multiobjective optimization. Journal of the operational research society, 57(10), 1143-1160. doi: 10.1057/palgrave.jors.2602068.

Nourani, Y., & Andresen, B. (1998). A comparison of simulated annealing cooling strategies. Journal of Physics A: Mathematical and General, 31(41), 8373-8385. doi: 10.1088/0305-4470/31/41/011.

Lokeswari, N., & Rao, B. C. (2016). Artificial Neural Network Classifier for Intrusion Detection System in Computer Network. In Proceedings of the Second International Conference on Computer and Communication Technologies (pp. 581-591). Springer, New Delhi. doi : 10.1007/978-81-322-2526-3_60.

Tama, B.A, Rhee, K. (2017) .Attack Classification Analysis of IoT Network via Deep Learning Approach. Research Briefs on Information & Communication Technology Evolution (ReBICTE), 3, 1-9.

Song, J., Takakura, H., Okabe, Y., Eto, M., Inoue, D., & Nakao, K. (2011, April). Statistical analysis of honeypot data and building of Kyoto 2006+ dataset for NIDS evaluation. In Proceedings of the First Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (pp. 29-36). ACM. doi: 10.1145/1978672.1978676.

Musbau, D.A, Alhassan, J.K, (2018) .Ensemble Learning Approach for the Enhancement of Performance of Intrusion Detection System. International Conference on Information and Communication Technology and its Applications (ICTA 2018) (pp. 1-8), Minna, Nigeria.

Singh, D., Patel, D., Borisaniya, B., & Modi, C. (2016). Collaborative ids framework for cloud. International Journal of Network Security, 18(4), 699-709.

Wang, W., Zhang, X., Gombault, S., & Knapskog, S. J. (2009, December). Attribute normalization in network intrusion detection. In Pervasive systems, algorithms, and networks (ISPAN), 2009 10th international symposium on (pp. 448-453). IEEE. doi: 10.1109/I-SPAN.2009.49.

Kumar, S., & Yadav, A. (2014, May). Increasing performance of intrusion detection system using neural network. In Advanced Communication Control and Computing Technologies (ICACCCT), 2014 International Conference on (pp. 546-550). IEEE. doi: 10.1109/ICACCCT.2014.7019145.

Sen, R., Chattopadhyay, M., & Sen, N. (2015, June). An efficient approach to develop an intrusion detection system based on multi layer backpropagation neural network algorithm: Ids using bpnn algorithm. In Proceedings of the 2015 ACM SIGMIS Conference on Computers and People Research (pp. 105-108). ACM. doi: 10.1145/2751957.2751979.

Gaidhane, R., Vaidya, C., & Raghuwanshi, M. (2014). Intrusion detection and attack classification using Back-propagation Neural Network. International Journal of Engineering Research and Technology, 3(3), 1112-1115.

Song, J., Takakura, H., & Okabe, Y. (2006). Description of kyoto university benchmark data. Available at link: http://www. takakura. com/Kyoto_data/BenchmarkData-Description-v5. pdf. [Accessed 08 August 2021]

Jabbar, M. A., & Aluvalu, R. (2017). RFAODE: A novel ensemble intrusion detection system. Procedia computer science, 115, 226-234. doi: 10.1016/j.procs.2017.09.129.

Proti?, D. D. (2018). Review of KDD Cup'99, NSL-KDD and Kyoto 2006+ datasets. Vojnotehni?ki glasnik, 66(3), 580-596. Doi: doi:10.5937/vojtehg66-16670.

Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information processing & management, 45(4), 427-437. doi: 10.1016/j.ipm.2009.03.002.

Chiba, Z., Abghour, N., Moussaid, K., & Rida, M. (2016). A cooperative and hybrid network intrusion detection framework in cloud computing based on snort and optimized back propagation neural network. Procedia Computer Science, 83, 1200-1206. doi: 10.1016/j.procs.2016.04.249.

Modi, C. N., Patel, D. R., Patel, A., & Muttukrishnan, R. (2012, July). Bayesian Classifier and Snort based network intrusion detection system in cloud computing. In Computing Communication & Networking Technologies (ICCCNT), 2012 Third International

Conference on (pp. 1-7). IEEE. doi: 10.1109/ICCCNT.2012.6396086.

Abouabdalla, O., El-Taj, H., Manasrah, A., & Ramadass, S. (2009, October). False positive reduction in intrusion detection system: A survey. In 2009 2nd IEEE International Conference on Broadband Network & Multimedia Technology (pp. 463-466). IEEE. doi: 10.1109/ICBNMT.2009.5348536.

Ali, U., Dewangan, K. K., & Dewangan, D. K. (2018). Distributed Denial of Service Attack Detection Using Ant Bee Colony and Artificial Neural Network in Cloud Computing. In Nature Inspired Computing (pp. 165-175). Springer, Singapore. doi: 10.1007/978-981-10-6747-1_19.

Kyoto 2006+ dataset. Available from: [Accessed 08 August 2021].

CIDDS-001 dataset. Available: [Accessed 08 August 2021].

Ring, M., Wunderlich, S., Grüdl, D., Landes, D., & Hotho, A. (2017, June). Flow-based benchmark data sets for intrusion detection. In Proceedings of the 16th European Conference on Cyber Warfare and Security. ACPI (pp. 361-369).

Zhang, Y., Chen, X., Jin, L., Wang, X., & Guo, D. (2019). Network Intrusion Detection: Based on Deep Hierarchical Network and Original Flow Data. IEEE Access, 7, 37004-37016. doi: 10.1109/ACCESS.2019.2905041.

Hatef, M. A., Shaker, V., Jabbarpour, M. R., Jung, J., & Zarrabi, H. (2018). HIDCC: A hybrid intrusion detection approach in cloud computing. Concurrency and Computation: Practice and Experience, 30(3), e4171-e7180. doi: 10.1002/cpe.4171

Chung, S., & Kim, K. (2015). A Heuristic Approach to Enhance the performance of Intrusion Detection System using Machine Learning Algorithms. In Proceedings of the Korea Institutes of Information Security and Cryptology Conference (CISC-W’15).

Javaid, A., Niyaz, Q., Sun, W., & Alam, M. (2016, May). A deep learning approach for network intrusion detection system. In Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS) (pp. 21-26). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).doi: 10.4108/eai.3-12-2015.2262516.

Gurung, S., Ghose, M. K., & Subedi, A. (2019). Deep Learning Approach on Network Intrusion Detection System using NSL-KDD Dataset. International Journal of Computer Network and Information Security (IJCNIS), 11(3), 8-14. doi: 10.5815/ijcnis.2019.03.02.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

Demuth, H. B., Beale, M. H., De Jess, O., & Hagan, M. T. (2014). Neural network design. Martin Hagan.

Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A deep learning approach for intrusion detection using recurrent neural networks. Ieee Access, 5, 21954-21961. doi: 10.1109/ACCESS.2017.2762418.

Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A deep learning approach to network intrusion detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 41-50. doi: 10.1109/TETCI.2017.2772792.

Al-Qatf, M., Lasheng, Y., Al-Habib, M., & Al-Sabahi, K. (2018). Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection. IEEE Access, 6, 52843-52856. doi: 10.1109/ACCESS.2018.2869577.

Ma, T., Yu, Y., Wang, F., Zhang, Q., & Chen, X. (2016, July). A hybrid methodologies for intrusion detection based deep neural network with support vector machine and clustering technique. In International Conference on Frontier Computing (pp. 123-134). Springer, Singapore. doi: 10.1007/978-981-10-3187-8_13.

Yang, Y., Zheng, K., Wu, C., & Yang, Y. (2019). Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network. Sensors, 19(11), 2528. Doi: 10.3390/s19112528.

Mehmood, Y., Shibli, M. A., Kanwal, A., & Masood, R. (2015, December). Distributed intrusion detection system using mobile agents in cloud computing environment. In 2015 Conference on Information Assurance and Cyber Security (CIACS) (pp. 1-8). IEEE. doi: 10.1109/CIACS.2015.7395559.

Singh, R., Kumar, H., & Singla, R. K. (2015). An intrusion detection system using network traffic profiling and online sequential extreme learning machine. Expert Systems with Applications, 42(22), 8609-8624. doi: 10.1016/j.eswa.2015.07.015.

Ghosh, P., Jha, S., Dutta, R., & Phadikar, S. (2016, July). Intrusion detection system based on BCS-GA in cloud environment. In International Conference on Emerging Research in Computing, Information, Communication and Applications (pp. 393-403). Springer, Singapore. doi: 10.1007/978-981-10-4741-1_35.

Aminanto, M. E., Kim, H., Kim, K. M., & Kim, K. (2017). Another fuzzy anomaly detection system based on ant clustering algorithm. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 100(1), 176-183.doi: 10.1587/transfun.E100.A.176.

Pajouh, H. H., Dastghaibyfard, G., & Hashemi, S. (2017). Two-tier network anomaly detection model: a machine learning approach. Journal of Intelligent Information Systems, 48(1), 61-74. doi: 10.1007/s10844-015-0388-x.

Mehibs, S. M., & Hashim, S. H. (2018). Proposed network intrusion detection system based on fuzzy c mean algorithm in cloud computing environment. Journal of University of Babylon, 26(2), 27-35.

Hamamoto, A. H., Carvalho, L. F., Sampaio, L. D. H., Abrão, T., & Proença Jr, M. L. (2018). Network anomaly detection system using genetic algorithm and fuzzy logic. Expert Systems with Applications, 92, 390-402. doi: 10.1016/j.eswa.2017.09.013.

Sharma, R., & Chaurasia, S. (2018). An enhanced approach to fuzzy C-means clustering for anomaly detection. In Proceedings of First International Conference on Smart System, Innovations and Computing (pp. 623-636). Springer, Singapore. doi: 10.1007/978-981-10-5828-8_60.

Borah, S., Panigrahi, R., & Chakraborty, A. (2018). An enhanced intrusion detection system based on clustering. In Progress in Advanced Computing and Intelligent Engineering (pp. 37-45). Springer, Singapore. doi: 10.1007/978-981-10-6875-1_5.

Achbarou, O., El Kiram, M. A., Bourkoukou, O., & Elbouanani, S. (2018). A New Distributed Intrusion Detection System Based on Multi-Agent System for Cloud Environment. International Journal of Communication Networks and Information Security, 10(3), 526-533.