Towards fuzzy anomaly detection-based security: a comprehensive review

In the data security context, anomaly detection is a branch of intrusion detection that can detect emerging intrusions and security attacks. A number of anomaly detection systems (ADSs) have been proposed in the literature that using various algorithms and techniques try to detect the intrusions and anomalies. This paper focuses on the ADS schemes which have applied fuzzy logic in combination with other machine learning and data mining techniques to deal with the inherent uncertainty in the intrusion detection process. For this purpose, it first presents the key knowledge about intrusion detection systems and then classifies the fuzzy ADS approaches regarding their utilized fuzzy algorithm. Afterward, it summarizes their major contributions and illuminates their advantages and limitations. Finally, concluding issues and directions for future researches in the fuzzy ADS context are highlighted.

[1]  Khalid Chougdali,et al.  Intrusion detection system using PCA and Fuzzy PCA techniques , 2016, 2016 International Conference on Advanced Communication Systems and Information Security (ACOSIS).

[2]  Shahaboddin Shamshirband,et al.  Anomaly Detection Using Cooperative Fuzzy Logic Controller , 2013, FIRA.

[3]  Saeed Khazaee,et al.  Using fuzzy C-means algorithm for improving intrusion detection performance , 2013, 2013 13th Iranian Conference on Fuzzy Systems (IFSC).

[4]  Julija Asmuss,et al.  Network traffic classification for anomaly detection fuzzy clustering based approach , 2015, 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[5]  Jonathon A. Chambers,et al.  Adding contextual information to Intrusion Detection Systems using Fuzzy Cognitive Maps , 2016, 2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA).

[6]  Gugulothu Narsimha,et al.  Feature Clustering for Anomaly Detection Using Improved Fuzzy Membership Function , 2018 .

[7]  Naruemon Wattanapongsakorn,et al.  Fuzzy-ART in network anomaly detection with feature-reduction dataset , 2011, 7th International Conference on Networked Computing.

[8]  Albert Y. Zomaya,et al.  NHAD: Neuro-Fuzzy Based Horizontal Anomaly Detection in Online Social Networks , 2018, IEEE Transactions on Knowledge and Data Engineering.

[9]  Hong Zhang,et al.  Intrusion Detection Based on Improvement of Genetic Fuzzy C-Means Algorithm , 2012 .

[10]  Li Lin,et al.  SFAD: Toward effective anomaly detection based on session feature similarity , 2019, Knowl. Based Syst..

[11]  Dervis Karaboga,et al.  An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training , 2016, Appl. Soft Comput..

[12]  Ning Wang,et al.  FCM technique for efficient intrusion detection system for wireless networks in cloud environment , 2017, Comput. Electr. Eng..

[13]  Antonio Pescapè,et al.  Integration of Cloud computing and Internet of Things: A survey , 2016, Future Gener. Comput. Syst..

[14]  Nitin Naik,et al.  Fuzzy Inference Based Intrusion Detection System: FI-Snort , 2015, 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing.

[15]  Han Wu,et al.  Anomaly intrusion detection based upon data mining techniques and fuzzy logic , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[16]  Manel Guerrero Zapata,et al.  A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks , 2015, Neurocomputing.

[17]  Juan Manuel Garcia Garcia,et al.  Discrete fuzzy transform applied to computer anomaly detection , 2011, 2011 Annual Meeting of the North American Fuzzy Information Processing Society.

[18]  Arputharaj Kannan,et al.  A Novel Weighted Fuzzy C –Means Clustering Based on Immune Genetic Algorithm for Intrusion Detection , 2012 .

[19]  Hamid Mohamadi,et al.  Design and analysis of genetic fuzzy systems for intrusion detection in computer networks , 2011, Expert Syst. Appl..

[20]  K. Kulothungan,et al.  Secure-MQTT: an efficient fuzzy logic-based approach to detect DoS attack in MQTT protocol for internet of things , 2019, EURASIP J. Wirel. Commun. Netw..

[21]  Sanjeev Jain,et al.  Implementation of Intrusion Detection System using Adaptive Neuro-Fuzzy Inference System for 5G wireless communication network , 2017 .

[22]  Qiang Yang,et al.  State of the Journal , 2019, IEEE Trans. Big Data.

[23]  Marimuthu Palaniswami,et al.  Evolving Fuzzy Rules for Anomaly Detection in Data Streams , 2015, IEEE Transactions on Fuzzy Systems.

[24]  Muttukrishnan Rajarajan,et al.  A survey of intrusion detection techniques in Cloud , 2013, J. Netw. Comput. Appl..

[25]  Mohamed Rida,et al.  A novel architecture combined with optimal parameters for back propagation neural networks applied to anomaly network intrusion detection , 2018, Comput. Secur..

[26]  Malathi Arunachalam,et al.  Hybrid Fuzzy Adaptive Wiener Filtering with Optimization for Intrusion Detection , 2015 .

[27]  Cihan Kaleli,et al.  A review on deep learning for recommender systems: challenges and remedies , 2018, Artificial Intelligence Review.

[28]  Abdolreza Mirzaei,et al.  Intrusion detection using fuzzy association rules , 2009, Appl. Soft Comput..

[29]  Shahram Babaie,et al.  A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection , 2018, Comput. Networks.

[30]  Shoushan Luo,et al.  A two-level hybrid approach for intrusion detection , 2016, Neurocomputing.

[31]  Lilia Georgieva,et al.  Anomaly Detection Using Agglomerative Hierarchical Clustering Algorithm , 2018, ICISA.

[32]  Mohiuddin Ahmed,et al.  A survey of network anomaly detection techniques , 2016, J. Netw. Comput. Appl..

[33]  Ganesh Kumar,et al.  Anomaly Detection System in Cloud Environment Using Fuzzy Clustering Based ANN , 2015, Mobile Networks and Applications.

[34]  Ahmad Akbari,et al.  Improving Detection Rate in Intrusion Detection Systems Using FCM Clustering to Select Meaningful Landmarks in Incremental Landmark Isomap Algorithm , 2011 .

[35]  Shadi A. Aljawarneh,et al.  A fuzzy measure for intrusion and anomaly detection , 2017, 2017 International Conference on Engineering & MIS (ICEMIS).

[36]  Zhongxing Zhang,et al.  Intrusion Detection Network Based on Fuzzy C-Means and Particle Swarm Optimization , 2016 .

[37]  Ruby Sharma,et al.  An Enhanced Approach to Fuzzy C-means Clustering for Anomaly Detection , 2018 .

[38]  Mohammad Masdari,et al.  Towards workflow scheduling in cloud computing: A comprehensive analysis , 2016, J. Netw. Comput. Appl..

[39]  Mohammad Masdari,et al.  Key management in wireless Body Area Network: Challenges and issues , 2017, J. Netw. Comput. Appl..

[40]  Kwangjo Kim,et al.  Another Fuzzy Anomaly Detection System Based on Ant Clustering Algorithm , 2017, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[41]  Mohammad Masdari,et al.  A survey and taxonomy of the fuzzy signature-based Intrusion Detection Systems , 2020, Appl. Soft Comput..

[42]  Timo Hämäläinen,et al.  Weighted Fuzzy Clustering for Online Detection of Application DDoS Attacks in Encrypted Network Traffic , 2016, NEW2AN.

[43]  Koen Vanhoof,et al.  A Granular Intrusion Detection System Using Rough Cognitive Networks , 2016, Recent Advances in Computational Intelligence in Defense and Security.

[44]  BottaAlessio,et al.  Integration of Cloud computing and Internet of Things , 2016 .

[45]  Hongjuan Wu,et al.  Intrusion detection using evolving fuzzy classifiers , 2011, 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference.

[46]  Ming-Yang Su,et al.  Genetic-fuzzy association rules for network intrusion detection systems , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[47]  Mohammad Masdari,et al.  A survey and taxonomy of the authentication schemes in Telecare Medicine Information Systems , 2017, J. Netw. Comput. Appl..

[48]  P. Ganeshkumar,et al.  Adaptive Neuro-Fuzzy-Based Anomaly Detection System in Cloud , 2016, Int. J. Fuzzy Syst..

[49]  Chung-Horng Lung,et al.  Network Traffic Anomaly Detection Using Adaptive Density-Based Fuzzy Clustering , 2014, 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications.

[50]  Ainuddin Wahid Abdul Wahab,et al.  Feature Selection of Denial-of-Service Attacks Using Entropy and Granular Computing , 2018 .

[51]  Jiejun Hu,et al.  False positive elimination in intrusion detection based on clustering , 2015, 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[52]  N. Jeyanthi,et al.  Intelligent intrusion detection system using temporal analysis and type-2 fuzzy neural classification , 2018 .

[53]  Milos Manic,et al.  Fuzzy logic based anomaly detection for embedded network security cyber sensor , 2011, 2011 IEEE Symposium on Computational Intelligence in Cyber Security (CICS).

[54]  B. Sujata,et al.  Combining Fuzzy C-Means and KNN Algorithms in Performance Improvement of Intrusion Detection System , 2018 .

[55]  Chuan Heng Foh,et al.  Defending against Packet-In messages flooding attack under SDN context , 2018, Soft Comput..

[56]  Mohammad Masdari,et al.  An overview of virtual machine placement schemes in cloud computing , 2016, J. Netw. Comput. Appl..

[57]  Sarab M. Hameed,et al.  INTRUSION DETECTION USING A MIXED FEATURES FUZZY CLUSTERING ALGORITHM , 2012 .

[58]  Saeed Sharifian,et al.  A novel framework, based on fuzzy ensemble of classifiers for intrusion detection systems , 2014, 2014 4th International Conference on Computer and Knowledge Engineering (ICCKE).

[59]  Wolfgang Banzhaf,et al.  The use of computational intelligence in intrusion detection systems: A review , 2010, Appl. Soft Comput..

[60]  Steven Furnell,et al.  D-FICCA: A density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks , 2014 .

[61]  MasdariMohammad,et al.  Towards workflow scheduling in cloud computing , 2016 .

[62]  Qiang Shen,et al.  Dynamic Fuzzy Rule Interpolation and Its Application to Intrusion Detection , 2018, IEEE Transactions on Fuzzy Systems.

[63]  K. Raghuveer,et al.  An Effective Technique for Intrusion Detection Using Neuro-Fuzzy and Radial SVM Classifier , 2013 .

[64]  Lei Li,et al.  A New Intrusion Detection System Based on Rough Set Theory and Fuzzy Support Vector Machine , 2011, 2011 3rd International Workshop on Intelligent Systems and Applications.

[65]  Jia Liu,et al.  Intrusion Detection Techniques Based on Improved Intuitionistic Fuzzy Neural Networks , 2016, 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS).

[66]  Pascal Poncelet,et al.  Fuzzy anomaly detection in monitoring sensor data , 2010, International Conference on Fuzzy Systems.

[67]  Joel J. P. C. Rodrigues,et al.  A comprehensive survey on network anomaly detection , 2018, Telecommunication Systems.

[68]  Zhiliang Zhu,et al.  Selecting Features for Anomaly Intrusion Detection: A Novel Method using Fuzzy C Means and Decision Tree Classification , 2013, CSS.

[69]  Raja Touahni,et al.  Identifying Intrusions in Computer Networks Using Robust Fuzzy PCA , 2017, 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA).

[70]  Huwaida Tagelsir Elshoush,et al.  Alert correlation in collaborative intelligent intrusion detection systems - A survey , 2011, Appl. Soft Comput..

[71]  Mohammad Masdari,et al.  A survey and taxonomy of DoS attacks in cloud computing , 2016, Secur. Commun. Networks.

[72]  Sivakami Raja,et al.  An Efficient Fuzzy-Based Hybrid System to Cloud Intrusion Detection , 2016, International Journal of Fuzzy Systems.

[73]  Ayush Sharma,et al.  Genetic Algorithm Based Feature Selection Algorithm for Effective Intrusion Detection in Cloud Networks , 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.

[74]  Hai Zhao,et al.  Using Intuitionistic Fuzzy Set for Anomaly Detection of Network Traffic From Flow Interaction , 2018, IEEE Access.

[75]  Taufik Abrao,et al.  A Game Theoretical Based System Using Holt-Winters and Genetic Algorithm With Fuzzy Logic for DoS/DDoS Mitigation on SDN Networks , 2017, IEEE Access.

[76]  Seyed-Amin Hosseini-Seno,et al.  An anomaly based VoIP DoS attack detection and prevention method using fuzzy logic , 2016, 2016 8th International Symposium on Telecommunications (IST).

[77]  Jill Slay,et al.  Novel Geometric Area Analysis Technique for Anomaly Detection Using Trapezoidal Area Estimation on Large-Scale Networks , 2019, IEEE Transactions on Big Data.

[78]  Shingo Mabu,et al.  An Intrusion-Detection Model Based on Fuzzy Class-Association-Rule Mining Using Genetic Network Programming , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[79]  Jugal K. Kalita,et al.  Network Anomaly Detection: Methods, Systems and Tools , 2014, IEEE Communications Surveys & Tutorials.

[80]  Dervis Karaboga,et al.  Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey , 2018, Artificial Intelligence Review.

[81]  Khelchandra Thongam,et al.  Detection and differentiation of application layer DDoS attack from flash events using fuzzy-GA computation , 2018, IET Inf. Secur..

[82]  Xing Li,et al.  A dynamic artificial immune-based intrusion detection method using rough and fuzzy set , 2013 .

[83]  V Vetriselvi,et al.  Intrusion Detection System for Software-Defined Networks Using Fuzzy System , 2018 .

[84]  Narendra Shekokar,et al.  Anomaly Detection in VoIP System Using Neural Network and Fuzzy Logic , 2011 .

[85]  Gugulothu Narsimha,et al.  An approach for intrusion detection using fuzzy feature clustering , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[86]  Maghsoud Abbaspour,et al.  Adaptive Anomaly-Based Intrusion Detection System Using Fuzzy Controller , 2012, Int. J. Netw. Secur..

[87]  Mohammed Anbar,et al.  Intrusion Detection Systems of ICMPv6-based DDoS attacks , 2016, Neural Computing and Applications.

[88]  S. Selvakumar,et al.  Detection of distributed denial of service attacks using an ensemble of adaptive and hybrid neuro-fuzzy systems , 2013, Comput. Commun..

[89]  K. S. Anil Kumar,et al.  Adaptive Fuzzy Neural Network Model for intrusion detection , 2014, 2014 International Conference on Contemporary Computing and Informatics (IC3I).

[90]  Shalini Batra,et al.  Fuzzified Cuckoo based Clustering Technique for Network Anomaly Detection , 2017, Comput. Electr. Eng..

[91]  Yu-Lin He,et al.  Toward an efficient fuzziness based instance selection methodology for intrusion detection system , 2017, Int. J. Mach. Learn. Cybern..

[92]  Mohammad Masdari,et al.  Green Cloud Computing Using Proactive Virtual Machine Placement: Challenges and Issues , 2019, Journal of Grid Computing.

[93]  Shadi Aljawarneh,et al.  PAREEKSHA: a machine learning approach for intrusion and anomaly detection , 2018, DATA.

[94]  Taufik Abrão,et al.  Network Anomaly Detection System using Genetic Algorithm and Fuzzy Logic , 2018, Expert Syst. Appl..

[95]  Tetiana Gladkykh,et al.  Fuzzy logic inference for unsupervised anomaly detection , 2016, 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP).