Chaotic Metaheuristics with Multi-Spiking Neural Network Based Cloud Intrusion Detection

被引:1
作者
Yamin, Mohammad [1 ]
Bajaba, Saleh [2 ]
AlKubaisy, Zenah Mahmoud [1 ]
机构
[1] King Abdulaziz Univ, Fac Econ & Adm, Dept Management Informat Syst, Jeddah 21589, Saudi Arabia
[2] King Abdulaziz Univ, Fac Econ & Adm, Dept Business Adm, Jeddah 21589, Saudi Arabia
来源
CMC-COMPUTERS MATERIALS & CONTINUA | 2023年 / 74卷 / 03期
关键词
Cloud computing; security; intrusion detection; feature selection; multi -spiking neural network; SYSTEM;
D O I
10.32604/cmc.2023.033677
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cloud Computing (CC) provides data storage options as well as computing services to its users through the Internet. On the other hand, cloud users are concerned about security and privacy issues due to the increased number of cyberattacks. Data protection has become an important issue since the users' information gets exposed to third parties. Computer networks are exposed to different types of attacks which have extensively grown in addition to the novel intrusion methods and hacking tools. Intrusion Detection Systems (IDSs) can be used in a network to manage suspicious activities. These IDSs monitor the activities of the CC environment and decide whether an activity is legitimate (normal) or malicious (intrusive) based on the established system's confidentiality, availability and integrity of the data sources. In the current study, a Chaotic Metaheuristics with Optimal Multi -Spiking Neural Network-based Intrusion Detection (CMOMSNN-ID) model is proposed to secure the cloud environment. The presented CMOMSNN-ID model involves the Chaotic Artificial Bee Colony Optimization-based Feature Selection (CABC-FS) technique to reduce the curse of dimensionality. In addition, the Multi-Spiking Neural Network (MSNN) classifier is also used based on the simulation of brain functioning. It is applied to resolve pattern classification problems. In order to fine-tune the parameters relevant to the MSNN model, the Whale Optimization Algorithm (WOA) is employed to boost the classification results. To demonstrate the superiority of the proposed CMOMSNN-ID model, a useful set of simulations was performed. The simulation outcomes inferred that the proposed CMOMSNN-ID model accomplished a superior performance over other models with a maximum accuracy of 99.20%.
引用
收藏
页码:6101 / 6118
页数:18
相关论文
共 18 条
  • [1] Achbarou Omar, 2018, International Journal of Communication Networks and Information Security, V10, P526
  • [2] Optimizing connection weights in neural networks using the whale optimization algorithm
    Aljarah, Ibrahim
    Faris, Hossam
    Mirjalili, Seyedali
    [J]. SOFT COMPUTING, 2018, 22 (01) : 1 - 15
  • [3] Enhanced intrusion detection and prevention system on cloud environment using hybrid classification and OTS generation
    Balamurugan, V.
    Saravanan, R.
    [J]. CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2019, 22 (Suppl 6): : 13027 - 13039
  • [4] LR-HIDS: logistic regression host-based intrusion detection system for cloud environments
    Besharati, Elham
    Naderan, Marjan
    Namjoo, Ehsan
    [J]. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2019, 10 (09) : 3669 - 3692
  • [5] A Survey on Intrusion Detection Systems for Fog and Cloud Computing
    Chang, Victor
    Golightly, Lewis
    Modesti, Paolo
    Xu, Qianwen Ariel
    Doan, Le Minh Thao
    Hall, Karl
    Boddu, Sreeja
    [J]. FUTURE INTERNET, 2022, 14 (03):
  • [6] Chiba Zouhair, 2019, International Journal of Communication Networks and Information Security, V11, P61
  • [7] HIDS: A host based intrusion detection system for cloud computing environment
    Deshpande P.
    Sharma S.C.
    Peddoju S.K.
    Junaid S.
    [J]. Deshpande, Prachi (deprachi3@gmail.com), 2018, Springer (09) : 567 - 576
  • [8] A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection
    Ghosh-Dastidar, Samanwoy
    Adeli, Hojjat
    [J]. NEURAL NETWORKS, 2009, 22 (10) : 1419 - 1431
  • [9] FCM-SVM based intrusion detection system for cloud computing environment
    Jaber, Aws Naser
    Ul Rehman, Shafiq
    [J]. CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2020, 23 (04): : 3221 - 3231
  • [10] Jacob I.J., 2021, J. Artif. Intell, V3, P62, DOI 10.36548/jaicn.2021.1.006