Multistage Ensembled Classifier for Wireless Intrusion Detection System

Digitization has given as a goliath whole of data that joins fragile information. Endeavours are attempting so hard to secure the data as far as mystery, insightfulness, and realness. One medium used by various associations for securing unapproved get to is through the intrusion detection system. This zone remains dynamic in researching as the peculiarity regarding intruders is growing exponentially on a regular reason. These solicitations successful figuring’s and systems that can recognize and take way better decisions practically increasingly current ambushes. A couple of AI based methodologies are existing recorded as a hard copy which can be upgraded for reduced wrong cautions. We have done a wide ask about experimentation on the AWID dataset for way better comes to fruition on DoS ambushes. We have used an embedded ridge-based decrease approach and ensemble classifier that gave us 99.94% exactness.

[1]  Vijay Varadharajan,et al.  A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection , 2019, IEEE Communications Surveys & Tutorials.

[2]  Francisco Herrera,et al.  On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on Intrusion Detection Systems , 2015, Expert Syst. Appl..

[3]  Georgios Kambourakis,et al.  TermID: a distributed swarm intelligence-based approach for wireless intrusion detection , 2017, International Journal of Information Security.

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

[5]  Paul D. Yoo,et al.  DEMISe: Interpretable Deep Extraction and Mutual Information Selection Techniques for IoT Intrusion Detection , 2019, ARES.

[6]  Bayu Adhi Tama,et al.  TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System , 2019, IEEE Access.

[7]  Norliza Katuk,et al.  Oving K-Means Clustering using discretization technique in Network Intrusion Detection System , 2016, 2016 3rd International Conference on Computer and Information Sciences (ICCOINS).

[8]  Chihli Hung,et al.  A selective ensemble based on expected probabilities for bankruptcy prediction , 2009, Expert Syst. Appl..

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

[10]  Kwangjo Kim,et al.  Detecting Impersonation Attack in WiFi Networks Using Deep Learning Approach , 2016, WISA.

[11]  Sannasi Ganapathy,et al.  Machine Learning Approach to Combat False Alarms in Wireless Intrusion Detection System , 2018, Comput. Inf. Sci..

[12]  Andrea Baiocchi,et al.  Statistical classification of services tunneled into SSH connections by a K-means based learning algorithm , 2010, IWCMC.

[13]  Ali Bou Nassif,et al.  Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection , 2019, Comput. Networks.

[14]  Xianbin Wang,et al.  Machine learning techniques for intrusion detection on public dataset , 2016, 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).

[15]  Jian Shen,et al.  Game-Theory-Based Active Defense for Intrusion Detection in Cyber-Physical Embedded Systems , 2016, ACM Trans. Embed. Comput. Syst..

[16]  Ravindra C. Thool,et al.  Intrusion Detection System Using Bagging Ensemble Method of Machine Learning , 2015, 2015 International Conference on Computing Communication Control and Automation.

[17]  Quamar Niyaz,et al.  An Ensemble Learning Based Wi-Fi Network Intrusion Detection System (WNIDS) , 2018, 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA).

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

[19]  Bo Li,et al.  Attack Detection for Wireless Enterprise Network: a Machine Learning Approach , 2018, 2018 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC).

[20]  Farrukh Aslam Khan,et al.  Network intrusion detection using hybrid binary PSO and random forests algorithm , 2015, Secur. Commun. Networks.

[21]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

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

[23]  Sangeetha Dhamodaran,et al.  Intrusion detection system for detecting wireless attacks in IEEE 802.11 networks , 2019, IET Networks.

[24]  Bihua Tang,et al.  A Semi-Supervised Learning Approach to IEEE 802.11 Network Anomaly Detection , 2019, 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring).

[25]  Khaled Elleithy,et al.  A majority voting technique for Wireless Intrusion Detection Systems , 2016, 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT).

[26]  Andrea Baiocchi,et al.  Real Time Identification of SSH Encrypted Application Flows by Using Cluster Analysis Techniques , 2009, Networking.

[27]  Miad Faezipour,et al.  Effective Features Selection and Machine Learning Classifiers for Improved Wireless Intrusion Detection , 2018, 2018 International Symposium on Networks, Computers and Communications (ISNCC).

[28]  Aiko Pras,et al.  An Overview of IP Flow-Based Intrusion Detection , 2010, IEEE Communications Surveys & Tutorials.

[29]  Vrizlynn L. L. Thing,et al.  IEEE 802.11 Network Anomaly Detection and Attack Classification: A Deep Learning Approach , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).