Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System

被引:9
|
作者
Al Duhayyim, Mesfer [1 ]
Alissa, Khalid A. [2 ]
Alrayes, Fatma S. [3 ]
Alotaibi, Saud S. [4 ]
El Din, ElSayed M. Tag [5 ]
Abdelmageed, Amgad Atta [6 ]
Yaseen, Ishfaq [6 ]
Motwakel, Abdelwahed [6 ]
机构
[1] Prince Sattam Bin Abdulaziz Univ, Coll Sci & Humanities Aflaj, Dept Comp Sci, Al Kharj 16278, Saudi Arabia
[2] Imam Abdulrahman Bin Faisal Univ, Coll Comp Sci & Informat Technol, Networks & Commun Dept, SAUDI ARAMCO Cybersecur Chair, POB 1982, Dammam 31441, Saudi Arabia
[3] Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Syst, POB 84428, Riyadh 11671, Saudi Arabia
[4] Umm Al Qura Univ, Coll Comp & Informat Syst, Dept Informat Syst, Mecca 24382, Saudi Arabia
[5] Future Univ Egypt, Fac Engn & Technol, Dept Elect Engn, New Cairo 11845, Egypt
[6] Prince Sattam Bin Abdulaziz Univ, Dept Comp & Self Dev, Preparatory Year Deanship, Al Kharj 16278, Saudi Arabia
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 14期
关键词
Internet of Things; deep learning; cyber physical systems; cloud computing; intrusion detection; security;
D O I
10.3390/app12146875
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
As cyberattacks develop in volume and complexity, machine learning (ML) was extremely implemented for managing several cybersecurity attacks and malicious performance. The cyber-physical systems (CPSs) combined the calculation with physical procedures. An embedded computer and network monitor and control the physical procedure, commonly with feedback loops whereas physical procedures affect calculations and conversely, at the same time, ML approaches were vulnerable to data pollution attacks. Improving network security and attaining robustness of ML determined network schemes were the critical problems of the growth of CPS. This study develops a new Stochastic Fractal Search Algorithm with Deep Learning Driven Intrusion Detection system (SFSA-DLIDS) for a cloud-based CPS environment. The presented SFSA-DLIDS technique majorly focuses on the recognition and classification of intrusions for accomplishing security from the CPS environment. The presented SFSA-DLIDS approach primarily performs a min-max data normalization approach to convert the input data to a compatible format. In order to reduce a curse of dimensionality, the SFSA technique is applied to select a subset of features. Furthermore, chicken swarm optimization (CSO) with deep stacked auto encoder (DSAE) technique was utilized for the identification and classification of intrusions. The design of a CSO algorithm majorly focuses on the parameter optimization of the DSAE model and thereby enhances the classifier results. The experimental validation of the SFSA-DLIDS model is tested using a series of experiments. The experimental results depict the promising performance of the SFSA-DLIDS model over the recent models.
引用
收藏
页数:17
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