Toward Efficient Intrusion Detection System Using Hybrid Deep Learning Approach

The increased adoption of cloud computing resources produces major loopholes in cloud computing for cybersecurity attacks. An intrusion detection system (IDS) is one of the vital defenses against threats and attacks to cloud computing. Current IDSs encounter two challenges, namely, low accuracy and a high false alarm rate. Due to these challenges, additional efforts are required by network experts to respond to abnormal traffic alerts. To improve IDS efficiency in detecting abnormal network traffic, this work develops an IDS using a recurrent neural network based on gated recurrent units (GRUs) and improved long short-term memory (LSTM) through a computing unit to form Cu-LSTMGRU. The proposed system efficiently classifies the network flow instances as benign or malevolent. This system is examined using the most up-to-date dataset CICIDS2018. To further optimize computational complexity, the dataset is optimized through the Pearson correlation feature selection algorithm. The proposed model is evaluated using several metrics. The results show that the proposed model remarkably outperforms benchmarks by up to 12.045%. Therefore, the Cu-LSTMGRU model provides a high level of symmetry between cloud computing security and the detection of intrusions and malicious attacks.

[1]  F. Alarfaj,et al.  CCrFS: Combine Correlation Features Selection for Detecting Phishing Websites Using Machine Learning , 2022, Future Internet.

[2]  Mai A. Alduailij,et al.  Machine-Learning-Based DDoS Attack Detection Using Mutual Information and Random Forest Feature Importance Method , 2022, Symmetry.

[3]  Zeyuan Fu.  Computer Network Intrusion Anomaly Detection with Recurrent Neural Network , 2022, Mobile Information Systems.

[4]  Lal Khan,et al.  Semi-Natural and Spontaneous Speech Recognition Using Deep Neural Networks with Hybrid Features Unification , 2021, Processes.

[5]  I. Barranco-Chamorro,et al.  Techniques to Deal with Off-Diagonal Elements in Confusion Matrices , 2021, Mathematics.

[6]  Ammar Aldallal,et al.  Effective Intrusion Detection System to Secure Data in Cloud Using Machine Learning , 2021, Symmetry.

[7]  Yung-Cheol Byun,et al.  Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction , 2021, Symmetry.

[8]  Xueming Wang,et al.  Feature Selection and Ensemble-Based Intrusion Detection System: An Efficient and Comprehensive Approach , 2021, Symmetry.

[9]  Maciej Lawrynczuk,et al.  LSTM and GRU Neural Networks as Models of Dynamical Processes Used in Predictive Control: A Comparison of Models Developed for Two Chemical Reactors , 2021, Sensors.

[10]  Shuhana Azmin,et al.  Network Intrusion Detection System based on Conditional Variational Laplace AutoEncoder , 2020, NSysS.

[11]  Michal Choras,et al.  Intrusion detection approach based on optimised artificial neural network , 2020, Neurocomputing.

[12]  Aidong Xu,et al.  A Novel Network Intrusion Detection System Based on CNN , 2020, 2020 Eighth International Conference on Advanced Cloud and Big Data (CBD).

[13]  Paul Patras,et al.  Tiki-Taka: Attacking and Defending Deep Learning-based Intrusion Detection Systems , 2020, CCSW@CCS.

[14]  A. Anbarasa Kumar,et al.  Analysis of intrusion detection in cyber attacks using DEEP learning neural networks , 2020, Peer-to-Peer Netw. Appl..

[15]  S. Tamane,et al.  NIDS-Network Intrusion Detection System Based on Deep and Machine Learning Frameworks with CICIDS2018 using Cloud Computing , 2020, 2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC).

[16]  Mounir Ghogho,et al.  DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking , 2020 .

[17]  Robertas Damasevicius,et al.  A Novel Method for Detection of Tuberculosis in Chest Radiographs Using Artificial Ecosystem-Based Optimisation of Deep Neural Network Features , 2020, Symmetry.

[18]  Maninder Singh,et al.  Recurrent Neural Network Based Intrusion Detection System , 2020, 2020 International Conference on Communication and Signal Processing (ICCSP).

[19]  Soosan Naderi Mighan,et al.  A novel scalable intrusion detection system based on deep learning , 2020, International Journal of Information Security.

[20]  S. Ganapathy,et al.  A deep learning approach for effective intrusion detection in wireless networks using CNN , 2020, Soft Comput..

[21]  Abdul Razaque,et al.  Deep recurrent neural network for IoT intrusion detection system , 2020, Simul. Model. Pract. Theory.

[22]  Kohei Shiomoto,et al.  Intrusion Detection System using Semi-Supervised Learning with Adversarial Auto-encoder , 2020, NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium.

[23]  Farah Barika Ktata,et al.  A deep learning-based multi-agent system for intrusion detection , 2020, SN Applied Sciences.

[24]  Yang Xin,et al.  Robust detection for network intrusion of industrial IoT based on multi-CNN fusion , 2020 .

[25]  Akhan Akbulut,et al.  Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic , 2020, Comput. Networks.

[26]  Mohammad Masdari,et al.  Efficient VM migrations using forecasting techniques in cloud computing: a comprehensive review , 2020, Cluster Computing.

[27]  M. Mayuranathan,et al.  RETRACTED ARTICLE: Best features based intrusion detection system by RBM model for detecting DDoS in cloud environment , 2019, Journal of Ambient Intelligence and Humanized Computing.

[28]  Haoxi Zhang,et al.  Intrusion detection system using deep learning for in-vehicle security , 2019, Ad Hoc Networks.

[29]  Shouhuai Xu,et al.  A Case Study on using Deep Learning for Network Intrusion Detection , 2019, MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM).

[30]  Michel Dagenais,et al.  A deep learning approach for proactive multi-cloud cooperative intrusion detection system , 2019, Future Gener. Comput. Syst..

[31]  Zhongjie Wang,et al.  Network Intrusion Detection Based on Deep Learning , 2019, 2019 International Conference on Communications, Information System and Computer Engineering (CISCE).

[32]  Geethapriya Thamilarasu,et al.  Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things , 2019, Sensors.

[33]  Howon Kim,et al.  Network Intrusion Detection Based on Novel Feature Selection Model and Various Recurrent Neural Networks , 2019, Applied Sciences.

[34]  Kazuhiko Ohkubo,et al.  A Botnet Detection Method on SDN using Deep Learning , 2019, 2019 IEEE International Conference on Consumer Electronics (ICCE).

[35]  Shulin Wang,et al.  Feature selection in machine learning: A new perspective , 2018, Neurocomputing.

[36]  Ahmed Elsherif,et al.  Automatic Intrusion Detection System Using Deep Recurrent Neural Network Paradigm , 2018 .

[37]  Xiaodong Liu,et al.  A Pearson's correlation coefficient based decision tree and its parallel implementation , 2018, Inf. Sci..

[38]  Hamid Mirvaziri,et al.  Attacks and Intrusion Detection in Cloud Computing Using Neural Networks and Particle Swarm Optimization Algorithms , 2018 .

[39]  Naseer Amara,et al.  Cloud Computing Security Threats and Attacks with Their Mitigation Techniques , 2017, 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC).

[40]  Rajni Ranjan Singh Makwana,et al.  Feature selection based intrusion detection system using the combination of DBSCAN, K-Mean++ and SMO algorithms , 2017, 2017 International Conference on Trends in Electronics and Informatics (ICEI).

[41]  Marcus Pendleton,et al.  A Survey on Systems Security Metrics , 2016, ACM Comput. Surv..

[42]  Lorentz Jäntschi,et al.  Sensitivity, specificity, and accuracy of predictive models on phenols toxicity , 2014, J. Comput. Sci..

[43]  Charles Elkan,et al.  Quadratic Programming Feature Selection , 2010, J. Mach. Learn. Res..

[44]  Muhammad Adnan Khan,et al.  Data Fusion-Based Machine Learning Architecture for Intrusion Detection , 2022, Computers, Materials & Continua.

[45]  Cihan Dagli,et al.  Network Intrusion Detection System using Deep Learning , 2021, Procedia Computer Science.

[46]  Nan Zhang,et al.  A network intrusion detection method based on deep learning with higher accuracy , 2020 .

[47]  Neha Gupta,et al.  Reducing False Positive in Intrusion Detection System : A Survey , 2016 .