Network Attacks Detection Methods Based on Deep Learning Techniques: A Survey

With the development of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have emerged to wireless communication system, especially in cybersecurity. In this paper, we offer a review on attack detection methods involving strength of deep learning techniques. Specifically, we firstly summarize fundamental problems of network security and attack detection and introduce several successful related applications using deep learning structure. On the basis of categorization on deep learning methods, we pay special attention to attack detection methods built on different kinds of architectures, such as autoencoders, generative adversarial network, recurrent neural network, and convolutional neural network. Afterwards, we present some benchmark datasets with descriptions and compare the performance of representing approaches to show the current working state of attack detection methods with deep learning structures. Finally, we summarize this paper and discuss some ways to improve the performance of attack detection under thoughts of utilizing deep learning structures.

[1]  Chunhua Wang,et al.  Machine Learning and Deep Learning Methods for Cybersecurity , 2018, IEEE Access.

[2]  Steven Aftergood,et al.  Cybersecurity: The cold war online , 2017, Nature.

[3]  Ling Gao,et al.  An Intrusion Detection Model Based on Deep Belief Networks , 2014, 2014 Second International Conference on Advanced Cloud and Big Data.

[4]  Lijuan Zheng,et al.  Intrusion Detection Using Deep Belief Network and Probabilistic Neural Network , 2017, 22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC).

[5]  Qi Shi,et al.  A Deep Learning Approach to Network Intrusion Detection , 2018, IEEE Transactions on Emerging Topics in Computational Intelligence.

[6]  Huaming Wu,et al.  Spatio-Temporal Representation With Deep Neural Recurrent Network in MIMO CSI Feedback , 2019, IEEE Wireless Communications Letters.

[7]  Xiaojiang Du,et al.  A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security , 2018, IEEE Communications Surveys & Tutorials.

[8]  Ce Wang,et al.  Label-Removed Generative Adversarial Networks Incorporating with K-Means , 2019, Neurocomputing.

[9]  Howon Kim,et al.  Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection , 2016, 2016 International Conference on Platform Technology and Service (PlatCon).

[10]  Hongyu Yang,et al.  Wireless Network Intrusion Detection Based on Improved Convolutional Neural Network , 2019, IEEE Access.

[11]  Feng Qu,et al.  An Intrusion Detection Model Based on Deep Belief Network , 2017, ICNCC.

[12]  Michele Colajanni,et al.  On the effectiveness of machine and deep learning for cyber security , 2018, 2018 10th International Conference on Cyber Conflict (CyCon).

[13]  Hongyu Liu,et al.  CNN and RNN based payload classification methods for attack detection , 2019, Knowl. Based Syst..

[14]  Md Zakirul Alam Bhuiyan,et al.  Joint Optimization of Offloading Utility and Privacy for Edge Computing Enabled IoT , 2020, IEEE Internet of Things Journal.

[15]  K. P. Soman,et al.  Deep Learning Approach for Intelligent Intrusion Detection System , 2019, IEEE Access.

[16]  Daniel S. Berman,et al.  A Survey of Deep Learning Methods for Cyber Security , 2019, Inf..

[17]  Katinka Wolter,et al.  A secure and cost-efficient offloading policy for Mobile Cloud Computing against timing attacks , 2018, Pervasive Mob. Comput..

[18]  Ralf C. Staudemeyer,et al.  Applying long short-term memory recurrent neural networks to intrusion detection , 2015 .

[19]  Mohammed Gharib,et al.  AutoIDS: Auto-encoder Based Method for Intrusion Detection System , 2019, ArXiv.

[20]  K. P. Soman,et al.  Evaluating effectiveness of shallow and deep networks to intrusion detection system , 2017, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[21]  Md Zahangir Alom,et al.  Intrusion detection using deep belief networks , 2015, 2015 National Aerospace and Electronics Conference (NAECON).

[22]  Liu Tang,et al.  MF-CNN: a New Approach for LDoS Attack Detection Based on Multi-feature Fusion and CNN , 2020, Mobile Networks and Applications.

[23]  Nicola Jones,et al.  Computer science: The learning machines , 2014, Nature.

[24]  Shouhuai Xu,et al.  Enhancing Robustness of Deep Neural Networks Against Adversarial Malware Samples: Principles, Framework, and AICS'2019 Challenge , 2018, ArXiv.

[25]  Yalin E. Sagduyu,et al.  Deep Learning for Launching and Mitigating Wireless Jamming Attacks , 2018, IEEE Transactions on Cognitive Communications and Networking.

[26]  Jie Zhang,et al.  A Blockchain-Powered Crowdsourcing Method With Privacy Preservation in Mobile Environment , 2019, IEEE Transactions on Computational Social Systems.

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

[28]  Richard Lippmann,et al.  The 1999 DARPA off-line intrusion detection evaluation , 2000, Comput. Networks.

[29]  Li Deng,et al.  A tutorial survey of architectures, algorithms, and applications for deep learning , 2014, APSIPA Transactions on Signal and Information Processing.

[30]  Abien Fred Agarap.  A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data , 2017, ICMLC.

[31]  Kwangjo Kim,et al.  Deep Learning in Intrusion Detection System: An Overview , 2016 .

[32]  Mounir Ghogho,et al.  Deep learning approach for Network Intrusion Detection in Software Defined Networking , 2016, 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM).

[33]  Hyrum S. Anderson,et al.  Predicting Domain Generation Algorithms with Long Short-Term Memory Networks , 2016, ArXiv.

[34]  Arun Kumar Sangaiah,et al.  A real-time and ubiquitous network attack detection based on deep belief network and support vector machine , 2020, IEEE/CAA Journal of Automatica Sinica.

[35]  Fei Dai,et al.  Dynamic Resource Provisioning With Fault Tolerance for Data-Intensive Meteorological Workflows in Cloud , 2020, IEEE Transactions on Industrial Informatics.

[36]  Mahmood Yousefi-Azar,et al.  Autoencoder-based feature learning for cyber security applications , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[37]  Sheng Chen,et al.  Application of Deep Belief Networks for opcode based malware detection , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[38]  Fei Dai,et al.  Trust-Oriented IoT Service Placement for Smart Cities in Edge Computing , 2020, IEEE Internet of Things Journal.

[39]  Shouhuai Xu,et al.  HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples , 2018, ArXiv.

[40]  Ming Zhang,et al.  A Deep Learning Method to Detect Web Attacks Using a Specially Designed CNN , 2017, ICONIP.

[41]  Xu Chen,et al.  Network Intrusion Detection: Based on Deep Hierarchical Network and Original Flow Data , 2019, IEEE Access.

[42]  Claudia Eckert,et al.  Deep Learning for Classification of Malware System Call Sequences , 2016, Australasian Conference on Artificial Intelligence.

[43]  Claudia Eckert,et al.  Empowering convolutional networks for malware classification and analysis , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[44]  Razvan Pascanu,et al.  Malware classification with recurrent networks , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[45]  Yuancheng Li,et al.  A Hybrid Malicious Code Detection Method based on Deep Learning , 2015 .

[46]  Howon Kim,et al.  An Effective Intrusion Detection Classifier Using Long Short-Term Memory with Gradient Descent Optimization , 2017, 2017 International Conference on Platform Technology and Service (PlatCon).

[47]  Carla Purdy,et al.  Toward an Online Anomaly Intrusion Detection System Based on Deep Learning , 2016, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA).

[48]  Georgios Kambourakis,et al.  Introducing Deep Learning Self-Adaptive Misuse Network Intrusion Detection Systems , 2019, IEEE Access.

[49]  Zeng Feng,et al.  Classification for DGA-Based Malicious Domain Names with Deep Learning Architectures , 2017 .

[50]  N. R. Raajan,et al.  AN INTELLECTUAL INTRUSION DETECTION SYSTEM MODEL FOR ATTACKS CLASSIFICATION USING RNN , 2017 .

[51]  Mohamed Amine Ferrag,et al.  Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study , 2020, J. Inf. Secur. Appl..

[52]  Ming Zhu,et al.  Malware traffic classification using convolutional neural network for representation learning , 2017, 2017 International Conference on Information Networking (ICOIN).

[53]  Xuyun Zhang,et al.  BeCome: Blockchain-Enabled Computation Offloading for IoT in Mobile Edge Computing , 2020, IEEE Transactions on Industrial Informatics.

[54]  Wei Huang,et al.  An intrusion detection method based on DBN in ad hoc networks , 2016 .

[55]  Ali Gökhan Yavuz,et al.  Network Anomaly Detection with Stochastically Improved Autoencoder Based Models , 2017, 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud).

[56]  Simone A. Ludwig.  Intrusion detection of multiple attack classes using a deep neural net ensemble , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[57]  Mansoor Alam,et al.  A Deep Learning Approach for Network Intrusion Detection System , 2016, EAI Endorsed Trans. Security Safety.

[58]  Konstantin Berlin,et al.  eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys , 2017, ArXiv.

[59]  Daniel L. Marino,et al.  Generalization of Deep Learning for Cyber-Physical System Security: A Survey , 2018, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society.

[60]  Muhammad Munwar Iqbal,et al.  Enhanced Network Anomaly Detection Based on Deep Neural Networks , 2018, IEEE Access.

[61]  Yang Yu,et al.  Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders , 2017, Secur. Commun. Networks.

[62]  Yunheung Paek,et al.  LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems , 2016, ArXiv.

[63]  A. A. Zaidan,et al.  Review of intrusion detection systems based on deep learning techniques: coherent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions , 2019, Neural Computing and Applications.

[64]  Yuefei Zhu,et al.  A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks , 2017, IEEE Access.

[65]  Fahimeh Farahnakian,et al.  A deep auto-encoder based approach for intrusion detection system , 2018, 2018 20th International Conference on Advanced Communication Technology (ICACT).

[66]  Ali A. Ghorbani,et al.  A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.

[67]  Xuyun Zhang,et al.  A blockchain‐based computation offloading method for edge computing in 5G networks , 2019, Softw. Pract. Exp..