Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review

Internet of Things (IoT) technology is prospering and entering every part of our lives, be it education, home, vehicles, or healthcare. With the increase in the number of connected devices, several challenges are also coming up with IoT technology: heterogeneity, scalability, quality of service, security requirements, and many more. Security management takes a back seat in IoT because of cost, size, and power. It poses a significant risk as lack of security makes users skeptical towards using IoT devices. This, in turn, makes IoT vulnerable to security attacks, ultimately causing enormous financial and reputational losses. It makes up for an urgent need to assess present security risks and discuss the upcoming challenges to be ready to face the same. The undertaken study is a multi-fold survey of different security issues present in IoT layers: perception layer, network layer, support layer, application layer, with further focus on Distributed Denial of Service (DDoS) attacks. DDoS attacks are significant threats for the cyber world because of their potential to bring down the victims. Different types of DDoS attacks, DDoS attacks in IoT devices, impacts of DDoS attacks, and solutions for mitigation are discussed in detail. The presented review work compares Intrusion Detection and Prevention models for mitigating DDoS attacks and focuses on Intrusion Detection models. Furthermore, the classification of Intrusion Detection Systems, different anomaly detection techniques, different Intrusion Detection System models based on datasets, various machine learning and deep learning techniques for data pre-processing and malware detection has been discussed. In the end, a broader perspective has been envisioned while discussing research challenges, its proposed solutions, and future visions.

[1]  Nicole C. Krämer,et al.  EMPIRICAL RESULTS ON DETERMINANTS OF ACCEPTANCE AND EMOTION ATTRIBUTION IN CONFRONTATION WITH A ROBOT RABBIT , 2011, Appl. Artif. Intell..

[2]  Yashwant Singh,et al.  IoT Vulnerability Assessment for Sustainable Computing: Threats, Current Solutions, and Open Challenges , 2020, IEEE Access.

[3]  Gregory J. Conti,et al.  Toward Instrumenting Network Warfare Competitions to Generate Labeled Datasets , 2009, CSET.

[4]  Xiaochun Cheng,et al.  Vulnerabilities and Limitations of MQTT Protocol Used between IoT Devices , 2019, Applied Sciences.

[5]  Michael I. Jordan,et al.  A Robust Minimax Approach to Classification , 2003, J. Mach. Learn. Res..

[6]  Ghizlane Orhanou,et al.  Neural Network-Based Voting System with High Capacity and Low Computation for Intrusion Detection in SIEM/IDS Systems , 2020, Secur. Commun. Networks.

[7]  Muhammad Imran,et al.  Perception layer security in Internet of Things , 2019, Future Gener. Comput. Syst..

[8]  Samarjeet Borah,et al.  Intrusion Detection Systems (IDS)—An Overview with a Generalized Framework , 2020 .

[9]  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.

[10]  MyungKeun Yoon,et al.  Using whitelisting to mitigate DDoS attacks on critical Internet sites , 2010, IEEE Communications Magazine.

[11]  Nareli Cruz Cortés,et al.  Feature selection to detect botnets using machine learning algorithms , 2017, 2017 International Conference on Electronics, Communications and Computers (CONIELECOMP).

[12]  B. Janet,et al.  Analyzing and eliminating phishing threats in IoT, network and other Web applications using iterative intersection , 2020, Peer-to-Peer Netw. Appl..

[13]  Andrea Zanella,et al.  IoT: Internet of Threats? A Survey of Practical Security Vulnerabilities in Real IoT Devices , 2019, IEEE Internet of Things Journal.

[14]  Mardhani Riasetiawan,et al.  Comparison of Hybrid Intrusion Detection System , 2020, 2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE).

[15]  Manuel López Martín,et al.  Application of deep reinforcement learning to intrusion detection for supervised problems , 2020, Expert Syst. Appl..

[16]  Md. Zakirul Alam Bhuiyan,et al.  Evolution of IoT-enabled connectivity and applications in automotive industry: A review , 2020, Veh. Commun..

[17]  Neelu Khare,et al.  An efficient XGBoost–DNN-based classification model for network intrusion detection system , 2020, Neural Computing and Applications.

[18]  Peter Filzmoser,et al.  Dynamic log file analysis: An unsupervised cluster evolution approach for anomaly detection , 2018, Comput. Secur..

[19]  E. Guillén,et al.  Detection of Non-Content Based Attacks Using GA with Extended KDD Features , 2022 .

[20]  Alexander D. Kent,et al.  Unified Host and Network Data Set , 2017, Security Science and Technology.

[21]  Yuan-Cheng Lai,et al.  A Spatiotemporal-Oriented Deep Ensemble Learning Model to Defend Link Flooding Attacks in IoT Network , 2021, Sensors.

[22]  Hicham Lakhlef,et al.  Internet of things security: A top-down survey , 2018, Comput. Networks.

[23]  Liu Jun-yi,et al.  Network Traffic Anomaly Detection Based on Incremental Possibilistic Clustering Algorithm , 2019 .

[24]  Emmanuel S. Pilli,et al.  Botnet analysis using ensemble classifier , 2016 .

[25]  S. Velliangiri,et al.  Detection of distributed denial of service attack in cloud computing using the optimization-based deep networks , 2020, J. Exp. Theor. Artif. Intell..

[26]  Hongle Du,et al.  Intrusion Detection System Based on Improved SVM Incremental Learning , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[27]  Ponsy R. K. Sathia Bhama,et al.  Detecting and confronting flash attacks from IoT botnets , 2019, The Journal of Supercomputing.

[28]  R. Gowri,et al.  A survey on semantic web service discovery , 2013, 2013 International Conference on Information Communication and Embedded Systems (ICICES).

[29]  Byung-Seo Kim,et al.  IoT Elements, Layered Architectures and Security Issues: A Comprehensive Survey , 2018, Sensors.

[30]  Hadis Karimipour,et al.  An improved two-hidden-layer extreme learning machine for malware hunting , 2020, Comput. Secur..

[31]  William J. Buchanan,et al.  Evaluation of TFTP DDoS amplification attack , 2016, Comput. Secur..

[32]  Ali A. Ghorbani,et al.  Toward developing a systematic approach to generate benchmark datasets for intrusion detection , 2012, Comput. Secur..

[33]  Stefano Secci,et al.  Detection of zero-day attacks: An unsupervised port-based approach , 2020, Comput. Networks.

[34]  Aiko Pras,et al.  Bad neighborhoods on the internet , 2014, IEEE Communications Magazine.

[35]  Nour Moustafa,et al.  UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) , 2015, 2015 Military Communications and Information Systems Conference (MilCIS).

[36]  Aida Mustapha,et al.  DETECTING BOTNET ATTACK IN INTERNET OF THINGS (IoTs) ENVIRONMENT BY USING MACHINE LEARNING TECHNIQUE: A REVIEW , 2020 .

[37]  Arputharaj Kannan,et al.  Decision tree based light weight intrusion detection using a wrapper approach , 2012, Expert Syst. Appl..

[38]  Kishore Angrishi,et al.  Turning Internet of Things(IoT) into Internet of Vulnerabilities (IoV) : IoT Botnets , 2017, ArXiv.

[39]  Christopher Leckie,et al.  High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning , 2016, Pattern Recognit..

[40]  Wan Haslina Hassan,et al.  Current research on Internet of Things (IoT) security: A survey , 2019, Comput. Networks.

[41]  Sultan Zavrak,et al.  Anomaly-Based Intrusion Detection From Network Flow Features Using Variational Autoencoder , 2020, IEEE Access.

[42]  Pichao Wang,et al.  Online human action recognition based on incremental learning of weighted covariance descriptors , 2018, Inf. Sci..

[43]  Iraj Mahdavi,et al.  Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms , 2019, J. King Saud Univ. Comput. Inf. Sci..

[44]  Yiqiang Sheng,et al.  A Packet-Length-Adjustable Attention Model Based on Bytes Embedding Using Flow-WGAN for Smart Cybersecurity , 2019, IEEE Access.

[45]  JooHwa Lee,et al.  GAN-based imbalanced data intrusion detection system , 2019, Personal and Ubiquitous Computing.

[46]  Usha Devi Gandhi,et al.  An empirical model in intrusion detection systems using principal component analysis and deep learning models , 2020 .

[47]  Sugata Sanyal,et al.  Survey of Security and Privacy Issues of Internet of Things , 2015, ArXiv.

[48]  Ioanna Zafeiriou.  IoT and Mobility in Smart Cities , 2020, 2020 3rd World Symposium on Communication Engineering (WSCE).

[49]  Wei Chen,et al.  Building Auto-Encoder Intrusion Detection System based on random forest feature selection , 2020, Comput. Secur..

[50]  Wenjuan Li,et al.  EFM: Enhancing the performance of signature-based network intrusion detection systems using enhanced filter mechanism , 2014, Comput. Secur..

[51]  Hemant Kumar Rath,et al.  IoT Standardization: The Road Ahead , 2018, Internet of Things - Technology, Applications and Standardization.

[52]  Shideh Saraeian,et al.  Application of Deep Learning Technique in an Intrusion Detection System , 2020, Int. J. Comput. Intell. Appl..

[53]  Ying Zhong,et al.  HELAD: A novel network anomaly detection model based on heterogeneous ensemble learning , 2020, Comput. Networks.

[54]  Masaru Fukushi,et al.  An NTP-based detection module for DDoS attacks on IoT , 2017, 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW).

[55]  Noorbakhsh Amiri Golilarz,et al.  An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers , 2019, Applied Sciences.

[56]  Bill Cheswick,et al.  Tracing Anonymous Packets to Their Approximate Source , 2000, LISA.

[57]  S. Manimurugan,et al.  Effective Attack Detection in Internet of Medical Things Smart Environment Using a Deep Belief Neural Network , 2020, IEEE Access.

[58]  Ali A. Ghorbani,et al.  Towards effective feature selection in machine learning-based botnet detection approaches , 2014, 2014 IEEE Conference on Communications and Network Security.

[59]  Ali Kashif Bashir,et al.  DIDDOS: An approach for detection and identification of Distributed Denial of Service (DDoS) cyberattacks using Gated Recurrent Units (GRU) , 2021, Future Gener. Comput. Syst..

[60]  Shashank Gupta,et al.  Future IoT‐enabled threats and vulnerabilities: State of the art, challenges, and future prospects , 2020, Int. J. Commun. Syst..

[61]  B. Prabadevi,et al.  A Review on Various Sniffing Attacks and its Mitigation Techniques , 2018 .

[62]  Zengguang Liu,et al.  LSTM-CGAN: Towards Generating Low-Rate DDoS Adversarial Samples for Blockchain-Based Wireless Network Detection Models , 2021, IEEE Access.

[63]  Azana Hafizah Mohd Aman,et al.  Internet of Things and Its Applications: A Comprehensive Survey , 2020, Symmetry.

[64]  Nagender Kumar Suryadevara,et al.  Smart Plugs: Paradigms and Applications in the Smart City-and-Smart Grid , 2019, Energies.

[65]  Mohammad S. Obaidat,et al.  Statistical vertical reduction‐based data abridging technique for big network traffic dataset , 2019, Int. J. Commun. Syst..

[66]  Lei Xiao,et al.  Support Vector Machine Based on Incremental Learning for Malware Detection , 2015 .

[67]  Kaushik Roy,et al.  Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks , 2020, Inf..

[68]  Ruchi Vishwakarma,et al.  A survey of DDoS attacking techniques and defence mechanisms in the IoT network , 2019, Telecommunication Systems.

[69]  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.

[70]  Saurabh Singh,et al.  SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection , 2020, Electronics.

[71]  Georgios Kambourakis,et al.  Intrusion Detection in 802.11 Networks: Empirical Evaluation of Threats and a Public Dataset , 2016, IEEE Communications Surveys & Tutorials.

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

[73]  Brij B. Gupta,et al.  Security, privacy and trust of different layers in Internet-of-Things (IoTs) framework , 2020, Future Gener. Comput. Syst..

[74]  Jin Kwak,et al.  A study on reduction of DDoS amplification attacks in the UDP-based CLDAP protocol , 2017, 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT).

[75]  Gürkan Gür,et al.  JESS: Joint Entropy-Based DDoS Defense Scheme in SDN , 2018, IEEE Journal on Selected Areas in Communications.

[76]  Lifeng Cao,et al.  Analysis of Multi-Types of Flow Features Based on Hybrid Neural Network for Improving Network Anomaly Detection , 2019, IEEE Access.

[77]  Mahmood Yousefi-Azar,et al.  Malytics: A Malware Detection Scheme , 2018, IEEE Access.

[78]  Alejandro Zunino,et al.  An empirical comparison of botnet detection methods , 2014, Comput. Secur..

[79]  Walid Saad,et al.  Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[80]  Jill Slay,et al.  The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set , 2016, Inf. Secur. J. A Glob. Perspect..

[81]  Cristian Hesselman,et al.  The DNS in IoT: Opportunities, Risks, and Challenges , 2020, IEEE Internet Computing.

[82]  Kaushik Roy,et al.  Machine Learning Algorithms for Preventing IoT Cybersecurity Attacks , 2020, IntelliSys.

[83]  Ana Lucila Sandoval Orozco,et al.  Mirror saturation in amplified reflection Distributed Denial of Service: A case of study using SNMP, SSDP, NTP and DNS protocols , 2020, Future Gener. Comput. Syst..

[84]  Shengli Sun,et al.  Research on Artificial Intelligence Enhancing Internet of Things Security: A Survey , 2020, IEEE Access.

[85]  Andrei Petrovski,et al.  Botnet Detection in the Internet of Things using Deep Learning Approaches , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[86]  Mahdi H. Miraz,et al.  Internet of Nano-Things, Things and Everything: Future Growth Trends , 2018, Future Internet.

[87]  Wei Xu,et al.  Incremental SVM based on reserved set for network intrusion detection , 2011, Expert Syst. Appl..

[88]  Ali A. Ghorbani,et al.  Botnet detection based on traffic behavior analysis and flow intervals , 2013, Comput. Secur..

[89]  Jamal Ghasemi,et al.  Intrusion detection system using an optimized kernel extreme learning machine and efficient features , 2019, Sādhanā.

[90]  Abhishek Majumder,et al.  Side-Channel Attack in Internet of Things: A Survey , 2020 .

[91]  Antonio Colella,et al.  Amplification DDoS Attacks: Emerging Threats and Defense Strategies , 2014, CD-ARES.

[92]  Alan Tamer Vasques,et al.  Amplified Reflection DDoS Attacks over IoT Mirrors: A Saturation Analysis , 2019, 2019 Workshop on Communication Networks and Power Systems (WCNPS).

[93]  Birgit Vogel-Heuser,et al.  Towards Industrial Intrusion Prevention Systems: A Concept and Implementation for Reactive Protection , 2018 .

[94]  Suhaimi Abd Ishak,et al.  Characterising and detection of botnet in P2P network for UDP protocol , 2020 .

[95]  Muhammad Imran,et al.  Deep learning and big data technologies for IoT security , 2020, Comput. Commun..

[96]  Zhaoxin Zhang,et al.  A Novel Approach to Detect IRC-Based Botnets , 2009, 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing.

[97]  P Chellammal,et al.  Real-time anomaly detection using parallelized intrusion detection architecture for streaming data , 2020, Concurr. Comput. Pract. Exp..

[98]  Rachida Ait Abdelouahid,et al.  Perception layer security in the internet of things , 2020, FNC/MobiSPC.

[99]  Shie Mannor,et al.  Robust Regression and Lasso , 2008, IEEE Transactions on Information Theory.

[100]  Fan Zhang,et al.  An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units , 2018, IEEE Access.

[101]  Quoc-Dung Ngo,et al.  A survey of IoT malware and detection methods based on static features , 2020, ICT Express.

[102]  Tankut Acarman,et al.  Deep learning to detect botnet via network flow summaries , 2018, Neural Computing and Applications.

[103]  R.K. Cunningham,et al.  Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation , 2000, Proceedings DARPA Information Survivability Conference and Exposition. DISCEX'00.

[104]  Aili Wang,et al.  Network Intrusion Detection Combined Hybrid Sampling With Deep Hierarchical Network , 2020, IEEE Access.

[105]  Ali A. Ghorbani,et al.  Detecting HTTP-based application layer DoS attacks on web servers in the presence of sampling , 2017, Comput. Networks.

[106]  Nishtha Kesswani,et al.  A Survey: Intrusion Detection Techniques for Internet of Things , 2019, Int. J. Inf. Secur. Priv..

[107]  Matthieu Guillaumin,et al.  Incremental Learning of Random Forests for Large-Scale Image Classification , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[108]  Bin Wu,et al.  Network Intrusion Detection Based on Supervised Adversarial Variational Auto-Encoder With Regularization , 2020, IEEE Access.

[109]  Massimo Vecchio,et al.  The Day After Mirai: A Survey on MQTT Security Solutions After the Largest Cyber-attack Carried Out through an Army of IoT Devices , 2017, IoTBDS.

[110]  Shuokang Huang,et al.  IGAN-IDS: An imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks , 2020, Ad Hoc Networks.

[111]  Niccolo Cascarano,et al.  GT: picking up the truth from the ground for internet traffic , 2009, CCRV.

[112]  Jenq-Shiou Leu,et al.  Robust Network Intrusion Detection Scheme Using Long-Short Term Memory Based Convolutional Neural Networks , 2020, Mobile Networks and Applications.

[113]  Jingmei Li,et al.  Incremental Learning for Malware Classification in Small Datasets , 2020, Secur. Commun. Networks.

[114]  Carlos García Garino,et al.  An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection , 2012, Expert Syst. Appl..

[115]  Hossam Faris,et al.  Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection , 2019, Journal of Ambient Intelligence and Humanized Computing.

[116]  Kesheng Wu,et al.  Botnet Detection Using Recurrent Variational Autoencoder , 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference.

[117]  Ditipriya Sinha,et al.  An integrated rule based intrusion detection system: analysis on UNSW-NB15 data set and the real time online dataset , 2019, Cluster Computing.

[118]  Eui-Nam Huh,et al.  Network Anomaly Detection based on Late Fusion of Several Machine Learning Algorithms , 2020 .

[119]  Chung-Horng Lung,et al.  Enhancing Routing Security in IoT: Performance Evaluation of RPL’s Secure Mode Under Attacks , 2020, IEEE Internet of Things Journal.

[120]  Degang Sun,et al.  A New Mimicking Attack by LSGAN , 2017, 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI).

[121]  Ali Selamat,et al.  Multilayer Framework for Botnet Detection Using Machine Learning Algorithms , 2021, IEEE Access.

[122]  OwezarskiPhilippe,et al.  Online and Scalable Unsupervised Network Anomaly Detection Method , 2017 .

[123]  Yuval Elovici,et al.  N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders , 2018, IEEE Pervasive Computing.

[124]  Wadii Boulila,et al.  Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions , 2020, Comput. Sci. Rev..

[125]  V. Sindhu,et al.  IOT System Model,Challenges and Threats , 2020 .

[126]  Tsung-Han Lee,et al.  Deep Learning Enabled Intrusion Detection and Prevention System over SDN Networks , 2020, 2020 IEEE International Conference on Communications Workshops (ICC Workshops).

[127]  Yasin Yilmaz,et al.  Timely Detection and Mitigation of Stealthy DDoS Attacks Via IoT Networks , 2020, IEEE Transactions on Dependable and Secure Computing.

[128]  Giancarlo Fortino,et al.  Evaluating Critical Security Issues of the IoT World: Present and Future Challenges , 2018, IEEE Internet of Things Journal.

[129]  Taran Singh Bharati.  Internet Of Things (Iot): A Critical Review , 2019 .

[130]  Marek Simon,et al.  DDoS Reflection Attack Based on IoT: A Case Study , 2018, CSOC.

[131]  Asmaa Munshi,et al.  DDOS Attack on IOT Devices , 2020, 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS).

[132]  Dong Hoon Lee,et al.  AI-IDS: Application of Deep Learning to Real-Time Web Intrusion Detection , 2020, IEEE Access.

[133]  Adnan M. Abu-Mahfouz,et al.  A Survey on the Security of Low Power Wide Area Networks: Threats, Challenges, and Potential Solutions , 2020, Sensors.

[134]  Abderrazak Jemai,et al.  A Survey of IoT Security Based on a Layered Architecture of Sensing and Data Analysis , 2020, Sensors.

[135]  Tarrah R. Glass-Vanderlan,et al.  A Survey of Intrusion Detection Systems Leveraging Host Data , 2018, ACM Comput. Surv..

[136]  Victor Guilherme Turrisi da Costa,et al.  IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices , 2019, Sensors.

[137]  Wisam Mahmood Lafta,et al.  Best strategy to control data on internet-of-robotic-things in heterogeneous networks , 2021 .

[138]  Ali A. Ghorbani,et al.  Detecting P2P botnets through network behavior analysis and machine learning , 2011, 2011 Ninth Annual International Conference on Privacy, Security and Trust.

[139]  Konstantinos Demestichas,et al.  Survey on Security Threats in Agricultural IoT and Smart Farming , 2020, Sensors.

[140]  Burak Kantarci,et al.  On the Feasibility of Deep Learning in Sensor Network Intrusion Detection , 2019, IEEE Networking Letters.

[141]  Mohammad Marufuzzaman,et al.  Botnet detection using graph-based feature clustering , 2017, Journal of Big Data.

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

[143]  Ali A. Ghorbani,et al.  Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy , 2019, 2019 International Carnahan Conference on Security Technology (ICCST).

[144]  Biplab Sikdar,et al.  A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures , 2019, IEEE Access.

[145]  Farookh Khadeer Hussain,et al.  DDoS attacks in IoT networks: a comprehensive systematic literature review , 2021, World Wide Web.

[146]  Anton O. Prokofiev,et al.  A method to detect Internet of Things botnets , 2018, 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus).

[147]  Lukasz Saganowski,et al.  Statistical and signal‐based network traffic recognition for anomaly detection , 2012, Expert Syst. J. Knowl. Eng..

[148]  Nassir Navab,et al.  Generalizability vs. Robustness: Adversarial Examples for Medical Imaging , 2018, ArXiv.

[149]  Kim-Kwang Raymond Choo,et al.  Detecting Internet of Things attacks using distributed deep learning , 2020, J. Netw. Comput. Appl..

[150]  Tim Merino,et al.  Expansion of Cyber Attack Data from Unbalanced Datasets Using Generative Adversarial Networks , 2019, Software Engineering Research, Management and Applications.

[151]  Chih-Fong Tsai,et al.  CANN: An intrusion detection system based on combining cluster centers and nearest neighbors , 2015, Knowl. Based Syst..

[152]  Ji Won Kim,et al.  CNN-Based Network Intrusion Detection against Denial-of-Service Attacks , 2020, Electronics.

[153]  Ali A. Ghorbani,et al.  Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization , 2018, ICISSP.

[154]  Muhammad Hanif Durad,et al.  Intrusion detection using deep sparse auto-encoder and self-taught learning , 2019, Neural Computing and Applications.

[155]  O. S. Jannath Nisha,et al.  A Survey on Code Injection Attacks in Mobile Cloud Computing Environment , 2018 .

[156]  Gisung Kim,et al.  A novel hybrid intrusion detection method integrating anomaly detection with misuse detection , 2014, Expert Syst. Appl..

[157]  Yidong Li,et al.  BotMark: Automated botnet detection with hybrid analysis of flow-based and graph-based traffic behaviors , 2020, Inf. Sci..

[158]  LippmannRichard,et al.  The 1999 DARPA off-line intrusion detection evaluation , 2000 .

[159]  Zhen Zhang,et al.  An Optimization Method for Intrusion Detection Classification Model Based on Deep Belief Network , 2019, IEEE Access.

[160]  Xinyu Yang,et al.  A Survey on the Edge Computing for the Internet of Things , 2018, IEEE Access.

[161]  Frank Beer,et al.  A new Attack Composition for Network Security , 2017, DFN-Forum Kommunikationstechnologien.