Abstract
Information security must be maintained because the amount of data in the world today is growing exponentially. The issues related to security are growing as big data usage increases. Finding ways to identify intrusions into networks and information systems is one of the major issues in this subject. It is imperative and important to enhance intrusion detection skills in order to address malevolent behavior in large data. This paper presents a scalable approach to harmful data detection. Three variables have been considered in this strategy and model: scalability, user review, and temporal progress. High volumes of data can be processed using this technology. Time is split into time periods for data training in this system, and each time interval uses users’ review information to train the data. Large volumes of data require sophisticated strategies to handle, and scalability in storage allows for faster processing and fewer computations. This approach is a kind of hardware–software hybrid solution for malware detection. A fresh approach to feature extraction has also been applied. In the suggested method, the bacteria algorithm in conjunction with the immune system algorithm has been utilized for the prediction operation, and the modified support vector machine algorithm and optical density have been utilized for classification. Based on the findings, the suggested combination algorithm outperforms other comparable techniques with a 21% detection rate, a 62% false alarm rate, a 15% accuracy rate, and a 73% training duration.
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Miyato, T., Maeda, S.I., Ishii, S., Koyama, M.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1979–1993 (2018)
Dara, R.A., Khan, T., Azim, J., Cicchello, O., Cort, G.: A semi-supervised approach to customer relationship management. In Artificial Intelligence and Soft Computing, pp. 58–64, (2016)
Dutt, A., Aghabozrgi, S., Ismail, M.A.B., Mahroeian, H.: Clustering algorithms applied in educational data mining. Int. J. Inf. Electron. Eng. 5(2), 112 (2015)
Guo, C., Tang, H., Niu, B., Lee, C.B.P.: A survey of bacterial foraging optimization. Neurocomputing 452, 728–746 (2021)
Chen, H., Zhang, Q., Luo, J., Xu, Y., Zhang, X.: An enhanced bacterial foraging optimization and its application for training kernel extreme learning machine. Appl. Soft Comput. 86, 105884 (2020)
Pisner, D.A., Schnyer, D.M.: Support vector machine. In: Machine learning, pp. 101–121. Elsevier (2020). https://doi.org/10.1016/B978-0-12-815739-8.00006-7
Campbell, C., Ying, Y.: Learning with support vector machines. Springer Nature, UK (2022)
Afzali, N., Azmi, R., Pishgoo, B.: A new clonal selection algorithm based on radius regularization of anomaly detectors. Accepted in the 16th CSI international symposium on Artificial intelligence and signal processing. AISP; (2012)
Rahul, P.K., Sarangi, S., Monika: Analysis of machine learning models for malware detection. J. Discrete Math. Sci. Cryptography 23(2), 395–407 (2020). https://doi.org/10.1080/09720529.2020.1721870
Asrigo, K., Litty, L., Lie, D.: Using VMM-based sensors to monitor honeypots. In: 2nd International Conference on Virtual Execution Environments. VEE, pp. 13e23, (2006)
Bello, I., Chiroma, H., Abdullahi, U.A., Gital, A.Y.U., Jauro, F., Khan, A., Abdulhamid, S.I.M.: Detecting ransomware attacks using intelligent algorithms: recent development and next direction from deep learning and big data perspectives. J. Amb. Intell. Human. Comput. 12, 8699–8717 (2021)
Kumar, P., Gupta, G.P., Tripathi, R.: Toward the design of an intelligent cyber attack detection system using the hybrid feature-reduced approach for iot networks. Arab. J. Sci. Eng. 46, 3749–3778 (2021)
Wang, G., Wu, J., Trik, M.: A novel approach to reduce video traffic based on understanding user demand and D2D communication in 5G networks. IETE J. Res. 22, 1–17 (2023)
Wang, Z., Jin, Z., Yang, Z., Zhao, W., Trik, M.: Increasing efficiency for routing in the Internet of Things using binary grey wolf optimization and fuzzy logic. J. King Saud Univer.-Comput. Inf. Sci. 35(9), 101732 (2023)
Sun, J., Zhang, Y., Trik, M.: PBPHS: a profile-based predictive handover strategy for 5G networks. Cybern. Syst. 28, 1–22 (2022)
Trik, M., Akhavan, H., Bidgoli, A.M., Molk, A.M.N.G., Vashani, H., Mozaffari, S.P.: A new adaptive selection strategy for reducing latency in networks on chip. Integration 89, 9–24 (2023)
Trik, M., Molk, A.M.N.G., Ghasemi, F., Pouryeganeh, P.: A hybrid selection strategy based on traffic analysis for improving performance in networks on chip. J. Sens. 2022, 1–19 (2022). https://doi.org/10.1155/2022/3112170
Yan, A., Li, Z., Gao, Z., Zhang, J., Huang, Z., NiWen, T.X.: MURLAV: a multiple-node-upset recovery latch and algorithm-based verification method. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. (2024). https://doi.org/10.1109/TCAD.2024.3357593
Aljojo, N.: Network transmission flags data affinity-based classification by K-nearest neighbor. Aro-The Sci. J. Koya Univ. 10(1), 35–43 (2022)
Mahmood, N.H., Kadir, D.H., Alzawbaee, O.M.M.: Building a statistical model to forecast traffic accidents for death and injuries by using Bivariate time series analysis. Zanco J. Human Sci. 28(1), 278–289 (2024)
Jameel, W.J., Kadhem, S.M., Abbas, A.R.: Detecting deepfakes with deep learning and gabor filters. ARO-Sci. J. Koya Univ. 10(1), 18–22 (2022)
Ali, P.J.M.: Investigating the Impact of min-max data normalization on the regression performance of K-nearest neighbor with different similarity measurements. ARO- Sci. J. Koya Univ. 10(1), 85–91 (2022)
Hongping, H., Luo, P., Kadir, D.H., Hassanvand, A.: Assessing the impact of aneurysm morphology on the risk of internal carotid artery aneurysm rupture: a statistical and computational analysis of endovascular coiling. Phys. Fluids (2023). https://doi.org/10.1063/5.0165575
Hai, T., Kadir, D.H., Ghanbari, A.: Modeling the emission characteristics of the hydrogen-enriched natural gas engines by multi-output least-squares support vector regression: comprehensive statistical and operating analyses. Energy 276, 127515 (2023)
Hussein, N.A.: Synchro software-based alternatives for improving traffic operations at signalized intersections. Aro-the Sci. J. Koya Univ. 10(1), 123–131 (2022)
Jalal, N., Ghafoor, K.Z.: Machine learning algorithms for detecting and analyzing social bots using a novel dataset. Aro-The Sci. J. Koya Univ. 10(2), 11–21 (2022)
Taher, A.H.: Train support vector machine using fuzzy c-means without a prior knowledge for hyperspectral image content classification. Aro-The Sci. J. Koya Univ. 10(2), 22–28 (2022)
Kadir, D.H., Rahi, A.R.K.: Applying the Bayesian technique in designing a single sampling plan. Cihan Univ-Erbil Sci. J. 7(2), 17–25 (2023)
Othman, T.S., Abdullah, S.M.: An intelligent intrusion detection system for internet of things attack detection and identification using machine learning. Aro-The Sci. J. Koya Univ. 11(1), 126–137 (2023)
Sun, G., Xu, Z., Yu, H., Chen, X., ChangVasilakos, V.A.V.: Low-latency and resource-efficient service function chaining orchestration in network function virtualization. IEEE Internet Things J. 7(7), 5760–5772 (2020). https://doi.org/10.1109/JIOT.2019.2937110
Sun, G., Liao, D., Zhao, D., Xu, Z., Yu, H.: Live migration for multiple correlated virtual machines in cloud-based data centers. IEEE Trans. Serv. Comput. 11(2), 279–291 (2018). https://doi.org/10.1109/TSC.2015.2477825
Saleh, D.M., Kadir, D.H., Jamil, D.I.: A comparison between some penalized methods for estimating parameters: simulation study. QALAAI Zanist J. 8(1), 1122–1134 (2023)
Omer, S.M., Ghafoor, K.Z., Askar, S.K.: Plant disease diagnosing based on deep learning techniques. Aro-The Sci. J. Koya Univ. 11(1), 38–47 (2023)
Dai, M., Luo, L., Ren, J., Yu, H., Sun, G.: PSACCF: prioritized online slice admission control considering fairness in 5G/B5G networks. IEEE Trans. Netw. Sci. Eng. 9(6), 4101–4114 (2022). https://doi.org/10.1109/TNSE.2022.3195862
Zou, X., Yuan, J., Shilane, P., Xia, W., ZhangWang, H.X.: From hyper-dimensional structures to linear structures: maintaining deduplicated data’s locality. ACM Trans. Storage 18(3), 1–28 (2022). https://doi.org/10.1145/3507921
Radha, H.M., Hassan, A.K.A., Al-Timemy, A.H.: Enhancing upper limb prosthetic control in amputees using non-invasive EEG and EMG signals with machine learning techniques. Aro- Sci J. Koya Univ. 11(2), 99–108 (2023)
Xia, W., Pu, L., Zou, X., Shilane, P., LiZhangWang, S.H.X.: The design of fast and lightweight resemblance detection for efficient post-deduplication delta compression. ACM Trans. Storage 19(3), 1–30 (2023). https://doi.org/10.1145/3584663
Liu, D., Cao, Z., Jiang, H., Zhou, S., Xiao, Z., Zeng, F.: Concurrent low-power listening: a new design paradigm for duty-cycling communication. ACM Trans. Sens. Netw. 19(1), 1–24 (2022). https://doi.org/10.1145/3517013
Khezri, E., Yahya, R.O., Hassanzadeh, H., Mohaidat, M., Ahmadi, S., Trik, M.: DLJSF: data-locality aware job scheduling IoT tasks in fog-cloud computing environments. Results Eng. 21, 101780 (2024)
Jiang, H., Wang, M., Zhao, P., Xiao, Z., Dustdar, S.: A utility-aware general framework with quantifiable privacy preservation for destination prediction in LBSs. IEEE/ACM Trans. Netw. 29(5), 2228–2241 (2021). https://doi.org/10.1109/TNET.2021.3084251
Omar, S.Y., Mamand, D.M., Omer, R.A., Rashid, R.F., Salih, M.I.: Investigating the role of metoclopramide and hyoscine-N-Butyl bromide in colon motility. Aro-The Sci. J. Koya Univ. 11(2), 109–115 (2023)
Sajadi, S.M., Kadir, D.H., Balaky, S.M., Perot, E.M.: An Eco-friendly nanocatalyst for removal of some poisonous environmental pollutions and statistically evaluation of its performance. Surfaces and Interfaces 23, 100908 (2021)
Kadir, D.H.: Statistical evaluation of main extraction parameters in twenty plant extracts for obtaining their optimum total phenolic content and its relation to antioxidant and antibacterial activities. Food Sci. Nutr. 9(7), 3491–3499 (2021)
Blbas, H., Kadir, D.H.: An application of factor analysis to identify the most effective reasons that university students hate to read books. Int. J. Innov. Creat. Change 6(2), 251–265 (2019)
Khezri, E., Zeinali, E., Sargolzaey, H.: SGHRP: secure greedy highway routing protocol with authentication and increased privacy in vehicular ad hoc networks. PLoS ONE 18(4), e0282031 (2023)
Omer, A.W., Blbas, H.T.A., Kadir, D.H.: A comparison between Brown’s and Holt’s double exponential smoothing for forecasting applied generation electrical energies in kurdistan region. Cihan University-Erbil Sci. J. 5(2), 56–63 (2021). https://doi.org/10.24086/cuesj.v5n2y2021.pp56-63
Ding, X., Yao, R., Khezri, E.: An efficient algorithm for optimal route node sensing in smart tourism Urban traffic based on priority constraints. Wireless Netw. (2023). https://doi.org/10.1007/s11276-023-03541-z
Yu, J., Lu, L., Chen, Y., Zhu, Y., Kong, L.: An indirect eavesdropping attack of keystrokes on touch screen through acoustic sensing. IEEE Trans. Mob. Comput. 20(2), 337–351 (2021). https://doi.org/10.1109/TMC.2019.2947468
Wu, Z., Liu, G., Wu, J., Tan, Y.: Are neighbors alike? A semisupervised probabilistic collaborative learning model for online review spammers detection. Inf. Syst. Res. (2023). https://doi.org/10.1287/isre.2022.0047
Xiao, L., Cao, Y., Gai, Y., Khezri, E., Liu, J., Yang, M.: Recognizing sports activities from video frames using deformable convolution and adaptive multiscale features. J. Cloud Comput. 12(1), 1–20 (2023)
Khosravi, M., Trik, M., Ansari, A.: Diagnosis and classification of disturbances in the power distribution network by phasor measurement unit based on fuzzy intelligent system. J. Eng. 2024(1), e12322 (2024)
Li, K., Ji, L., Yang, S., Li, H., Liao, X.: Couple-group consensus of cooperative-competitive heterogeneous multiagent systems: a fully distributed event-triggered and pinning control method. IEEE Trans. Cybern. 52(6), 4907–4915 (2022). https://doi.org/10.1109/TCYB.2020.3024551
Zheng, W., Deng, P., Gui, K., Wu, X.: An abstract syntax tree based static fuzzing mutation for vulnerability evolution analysis. Inf. Softw. Technol. 158, 107194 (2023). https://doi.org/10.1016/j.infsof.2023.107194
Ma, J., Hu, J.: Safe consensus control of cooperative-competitive multi-agent systems via differential privacy. Kybernetika 58(3), 426–439 (2022). https://doi.org/10.14736/kyb-2022-3-0426
Li, Y., Wang, H., Trik, M.: Design and simulation of a new current mirror circuit with low power consumption and high performance and output impedance. Analog Integrated Circuits Signal Process 119(1), 29–41 (2024). https://doi.org/10.1007/s10470-023-02243-y
Wang, Q., Hu, J., Wu, Y., Zhao, Y.: Output synchronization of wide-area heterogeneous multi-agent systems over intermittent clustered networks. Inf. Sci. 619, 263–275 (2023). https://doi.org/10.1016/j.ins.2022.11.035
Li, J., Huang, C., Yang, Y., Liu, J., LinPan, X.J.: How nursing students’ risk perception affected their professional commitment during the COVID-19 pandemic: the mediating effects of negative emotions and moderating effects of psychological capital. Human. Social Sci. Commun. 10(1), 195 (2023). https://doi.org/10.1057/s41599-023-01719-6
Cai, R., Tang, J., Deng, C., Lv, G., Xu, X., SylviaPan, S.J.: Violence against health care workers in China, 2013–2016: evidence from the national judgment documents. Hum. Resour. Health 17(1), 103 (2019). https://doi.org/10.1186/s12960-019-0440-y
Zhang, X., Deng, H., Xiong, Z., Liu, Y., Rao, Y., Lyu, Y., Li, Y.: Secure routing strategy based on attribute-based trust access control in social-aware networks. J. Signal Process. Syst. (2024). https://doi.org/10.1007/s11265-023-01908-1
Lyu, T., Xu, H., Zhang, L., Han, Z.: Source selection and resource allocation in wireless-powered relay networks: an adaptive dynamic programming-based approach. IEEE Internet Things J. 11(5), 8973–8988 (2024). https://doi.org/10.1109/JIOT.2023.3321673
Wang, D., Zhang, W., Wu, W., Guo, X.: Soft-label for multi-domain fake news detection. IEEE Access 11, 98596–98606 (2023). https://doi.org/10.1109/ACCESS.2023.3313602
Ding, Y., Zhang, W., Zhou, X., Liao, Q., LuoNi, Q.L.M.: FraudTrip: taxi fraudulent trip detection from corresponding trajectories. IEEE Internet Things J. 8(16), 12505–12517 (2021). https://doi.org/10.1109/JIOT.2020.3019398
Liao, Q., Chai, H., Han, H., Zhang, X., Wang, X., XiaDing, W.Y.: An integrated multi-task model for fake news detection. IEEE Trans. Knowl. Data Eng. 34(11), 5154–5165 (2022). https://doi.org/10.1109/TKDE.2021.3054993
Xu, Y., Wang, E., Yang, Y., Chang, Y.: A unified collaborative representation learning for neural-network based recommender systems. IEEE Trans. Knowl. Data Eng. 34(11), 5126–5139 (2022). https://doi.org/10.1109/TKDE.2021.3054782
Zhang, H., Mi, Y., Liu, X., Zhang, Y., Wang, J., Tan, J.: A differential game approach for real-time security defense decision in scale-free networks. Comput. Netw. 224(2023), 109635 (2023)
Wenjing, W., Zhang, L., Yuhang, W., Zhao, H.: Adaptive saturated two-bit-triggered bipartite consensus control for networked MASs with periodic disturbances: a low-computation method. IMA J. Math. Control. Inf. 41(1), 116–148 (2024). https://doi.org/10.1093/imamci/dnae002
Zhao, H., Wang, H., Ning, Xu., Zhao, X., Sharaf, S.: Fuzzy approximation-based optimal consensus control for nonlinear multiagent systems via adaptive dynamic programming. Neurocomputing 533, 126529 (2023)
Zhao, H., Zong, G., Wang, H., Zhao, X., Ning, X.: Zero-sum game-based hierarchical sliding-mode fault-tolerant tracking control for interconnected nonlinear systems via adaptive critic design. IEEE Trans. Autom. Sci. Eng. (2024). https://doi.org/10.1109/TASE.2023.3317902
Zhang, H., Zou, Q., Ying, Ju., Song, C., Chen, D.: Distance-based support vector machine to predict DNA N6-methyladine Modification. Curr. Bioinform. 17(5), 473–482 (2022)
Cao, C., Wang, J., Kwok, D., Cui, F., Zhang, Z., Zhao, D., Li, M.J., Zou, Q.: webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study. Nucleic Acids Res. 50(D1), D1123–D1130 (2022). https://doi.org/10.1093/nar/gkab957
Liu, C.H., Chen, W.H.: The study of using big data analysis to detecting APT attack. J. Comput. 30(1), 206–222 (2019)
Xu, G., Su, W., He, Z.: An efficient implementation of network malicious traffic screening based on big data analytics. In 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), pp. 1274–1277, IEEE, (2021)
Louati, F., Ktata, F.B., Amous, I.: Big-IDS: a decentralized multi agent reinforcement learning approach for distributed intrusion detection in big data networks. Cluster Comput. 2, 1–19 (2024)
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All authors contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “ Yue WANG and Yan SH “. The first draft of the manuscript was written by Yue WANG and Yan SH commented on previous versions of the manuscript.
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Wang, Y., Shi, Y. Detecting the penetration of malicious behavior in big data using hybrid algorithms. SIViP (2024). https://doi.org/10.1007/s11760-024-03203-3
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DOI: https://doi.org/10.1007/s11760-024-03203-3