IMPROVED INTRUSION DETECTION SYSTEM FOR CLOUD COMPUTING: A SURVEY
DOI:
https://doi.org/10.65009/w58a8742Keywords:
Cloud computing, Intrusion Detection System (IDS), WSN, Dataset, Attacks, Algorithm, etc.,,Abstract
Cloud computing has revolutionized the technology landscape with its scalability and cost
efficiency. However, it has also introduced unique security challenges. System Aided Design (SAD) has
emerged as a vital tool in addressing these issues by enhancing the classification of security threats
specific to the cloud environment. While cloud computing does offer flexibility and economic advantages,
the extensive sensitive data involved raise concerns about data security and privacy. Intrusion Detection
Systems (IDSs) are pivotal for cloud security but face challenges due to the dynamic nature of the cloud.
This research work focuses on developing a cloud-based IDS using neuro-swarm intelligence techniques
to efficiently analyze and classify network traffic, adapting seamlessly to the dynamic cloud landscape.
This approach promises to be a robust solution for safeguarding data and ensuring secure cloud
operations. A comprehensive evaluation of an Intrusion Detection System (IDS) that utilizes G-ABC and
DNN techniques has been performed under this research work. Moreover, this research work goes beyond
the well-detected DoS attacks to assess the IDS's performance in identifying U2R, R2L, and Probes
attacks using both the NSL KDD and UNSW NB15 datasets. The analysis includes precision, recall, F
measure, and accuracy metrics, highlighting the IDS's potential to enhance intrusion detection across
various attack categories.
References
Naveen Sai Bommina, Uppu Lokesh, Nandipati Sai Akash, Dr. Hussain Syed, Dr. Syed Umar,
"Optimizing AI-Driven Security Protocols in IoT Networks Using Metaheuristic Algorithms",
International Journal of Intelligent Systems and Applications in Engineering, IJISAE, 2024,
(23s), 3339–3347.
Naveen Sai Bommina, Nandipati Sai Akash, Uppu Lokesh, Dr. Hussain Syed, Dr. Syed Umar, "A
Hybrid Optimization Framework for Enhancing IoT Security via AI-based Anomaly Detection",
International Journal on Recent and Innovation Trends in Computing and Communication, ISSN:
-8169 Volume: 11 Issue: 3.
Nandipati Sai Akash, Uppu Lokesh, Naveen Sai Bommina, Hussain Syed, Syed Umar, "Swarm
Intelligence-Based Hyperparameter Optimization for AI-Powered IoT Threat Detection",
International Journal of Intelligent Systems and Applications in Engineering, (2024), 12(17s),
Uppu Lokesh, Naveen Sai Bommina, Nandipati Sai Akash, Dr. Hussain Syed, Dr. Syed Umar,
"Designing Energy-Efficient and Secure IoT Architectures Using Evolutionary Optimization
Algorithms", International Journal of Applied Engineering & Technology, Vol. 4 No.2,
September, 2022.
Divya Rohatgi, Dr. Tulika Pandey, "Regression Test Selection Framework for Web Services",
INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9,
ISSUE 03, MARCH 2020.
K Sankar, Divya Rohatgi, S Balakrishna Reddy, "COX Regressive Winsorized Correlated
Convolutional Deep Belief Boltzmann Network for Covid-19 Prediction with Big Data", Grenze
International Journal of Engineering & Technology (GIJET), Grenze ID: 01.GIJET.9.1.547, ©
Grenze Scientific Society, 2023.
Thakre N, Nimma D, Turukmane AV, Singh AK, Rohatgi D, Bangaru B (2024) Dynamic path
planning for autonomous robots in forest fire scenarios using hybrid deep reinforcement learning
and particle swarm optimization. Int J Adv Comput Sci Appl 15(9).
Ahmad, Z., Khan, A. S., Aqeel, S., Julaihi, A. A., Tarmizi, S., Annuar, N., & Habeeb, M. S. (2022,
May). S-ADS: spectrogram image-based anomaly detection system for IoT networks. In 2022
Applied Informatics International Conference (AiIC) (pp. 105-110). IEEE.
Habeeb, M. S., & Babu, T. R. (2024, October). Enhancing IoT Security Through Advanced Feature
Selection and Deep Learning. In International Conference on Computing and Communication
Networks (pp. 37-49). Singapore: Springer Nature Singapore.
Uppu Lokesh , Naveen Sai Bommina , Nandipati Sai Akash , Dr. Hussain Syed , Dr. Syed Umar.
(2021). Deep Reinforcement Learning with Genetic Algorithm Tuning for Intrusion Detection in IoT Systems. International Journal of Communication Networks and Information Security
(IJCNIS), 13(3), 582–595.
Naveen Sai Bommina , Nandipati Sai Akash, Uppu Lokesh , Dr. Hussain Syed , Dr. Syed Umar,
"Multi-Objective Genetic Algorithms for Secure Routing and Data Privacy in IoT Networks",
International Journal of Communication Networks and Information Security (IJCNIS), (2020),
(3), 632–643.
S. Shamshirband, M. Fathi, A. T. Chronopoulos, A. Montieri, F. Palumbo, and A. Pescapè, ―Computational intelligence intrusion detection techniques in mobile cloud computing
environments: Review, taxonomy, and open research issues,‖ Journal of Information Security and
Applications, vol. 55, p. 102582, Dec. 2020, doi: 10.1016/J.JISA.2020.102582.
A. Bahaa, A. Abdelaziz, A. Sayed, L. Elfangary, and H. Fahmy, ―Monitoring Real Time Security
Attacks for IoT Systems Using DevSecOps: A Systematic Literature Review,‖ Information 2021,
Vol. 12, Page 154, vol. 12, no. 4, p. 154, Apr. 2021, doi: 10.3390/INFO12040154.
P. Chouhan and R. Singh, ―Security Attacks on Cloud Computing With Possible Solution,‖
International Journal of Advanced Research in Computer Science and Software Engineering, vol.
, no. 1, pp. 92–96, 2016, Accessed: Feb. 08, 2023. [Online]. Available: www.ijarcsse.com.
S. Abidin, ―Wireless Sensor Network and Security Mechanism by Encryption‖.
A. N. Jaber and S. U. Rehman, ―FCM--SVM based intrusion detection system for cloud
computing environment,‖ Cluster Computing, vol. 23, no. 4, pp. 3221–3231, 2020.
M. Islabudeen and M. K. Kavitha Devi, ―A Smart Approach for Intrusion Detection and
Prevention System in Mobile Ad Hoc Networks Against Security Attacks,‖ Wireless Personal
Communications, vol. 112, no. 1, pp. 193–224, May 2020, doi: 10.1007/S11277-019-07022
/METRICS.
S. Velliangiri, P. Karthikeyan, and V. Vinoth Kumar, ―Detection of distributed denial of service
attack in cloud computing using the optimization-based deep networks,‖ Journal of Experimental
& Theoretical Artificial Intelligence, vol. 33, no. 3, pp. 405–424, May 2021, doi:
1080/0952813X.2020.1744196.
B. Eren, Ü. Fen, B. Dergisi, and F. Türk, ―Analysis of Intrusion Detection Systems in UNSW
NB15 and NSL-KDD Datasets with Machine Learning Algorithms,‖ Bitlis Eren Üniversitesi Fen
Bilimleri Dergisi, vol. 12, no. 2, pp. 465–477, Jun. 2023, doi:10.17798/BITLISFEN.1240469.
S. Maya, K. Ueno, and T. Nishikawa, ―dLSTM: a new approach for anomaly detection using
deep learning with delayed prediction,‖ International Journal of Data Science and Analytics, vol.
, pp. 137–164, 2019.
A. Abbas, M. A. Khan, S. Latif, M. Ajaz, A. A. Shah, and J. Ahmad, ―A new ensemblebased
intrusion detection system for internet of things,‖ Arabian Journal for Science and Engineering, pp.
–15, 2021.
A. Kurani, P. Doshi, A. Vakharia, and M. Shah, ―A Comprehensive Comparative Study of
Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting,‖
Annals of Data Science, vol. 10, no. 1, pp. 183–208, Feb. 2023, doi:10.1007/S40745-021-00344
X/METRICS.
