Toward Design of an Intelligent Cyber Attack Detection System using Hybrid Feature Reduced Approach for IoT Networks

With simple connectivity and fast-growing demand of smart devices and networks, IoT has become more prone to cyber attacks. In order to detect and prevent cyber attacks in IoT networks, intrusion detection system (IDS) plays a crucial role. However, most of the existing IDS have dimensionality curse that reduces overall IoT systems efficiency. Hence, it is important to remove repetitive and irrelevant features while designing effective IDS. Motivated from aforementioned challenges, this paper presents an intelligent cyber attack detection system for IoT network using a novel hybrid feature reduced approach. This technique first performs feature ranking using correlation coefficient, random forest mean decrease accuracy and gain ratio to obtain three different feature sets. Then, features are combined using a suitably designed mechanism (AND operation), to obtain single optimized feature set. Finally, the obtained reduced feature set is fed to three well-known machine learning algorithms such as random forest, K-nearest neighbor and XGBoost for detection of cyber attacks. The efficiency of the proposed cyber attack detection framework is evaluated using NSL-KDD and two latest IoT-based datasets namely, BoT-IoT and DS2OS. Performance of the proposed framework is evaluated and compared with some recent state-of-the-art techniques found in literature, in terms of accuracy, detection rate (DR), precision and F1 score. Performance analysis using these three datasets shows that the proposed model has achieved DR up to 90%–100%, for most of the attack vectors that has close similarity to normal behaviors and accuracy above 99%.

[1]  Sanyam Shukla,et al.  An analysis of "A feature reduced intrusion detection system using ANN classifier" by Akashdeep et al. expert systems with applications (2017) , 2019, Expert Syst. Appl..

[2]  Marc-Oliver Pahl,et al.  All Eyes on You: Distributed Multi-Dimensional IoT Microservice Anomaly Detection , 2018, 2018 14th International Conference on Network and Service Management (CNSM).

[3]  Wentao Ma,et al.  Estimator with forgetting factor of correntropy and recursive algorithm for traffic network prediction , 2013, 2013 25th Chinese Control and Decision Conference (CCDC).

[4]  H. Dag,et al.  Comparison of feature selection algorithms for medical data , 2012, 2012 International Symposium on Innovations in Intelligent Systems and Applications.

[5]  Bayu Adhi Tama,et al.  HFSTE: Hybrid Feature Selections and Tree-Based Classifiers Ensemble for Intrusion Detection System , 2017, IEICE Trans. Inf. Syst..

[6]  Aakanksha Tewari,et al.  A novel ECC-based lightweight authentication protocol for internet of things devices , 2019, Int. J. High Perform. Comput. Netw..

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

[8]  Achim Zeileis,et al.  Bias in random forest variable importance measures: Illustrations, sources and a solution , 2007, BMC Bioinformatics.

[9]  Neeraj Kumar,et al.  Machine Learning Models for Secure Data Analytics: A taxonomy and threat model , 2020, Comput. Commun..

[10]  Neeraj Kumar,et al.  A feature reduced intrusion detection system using ANN classifier , 2017, Expert Syst. Appl..

[11]  Kehe Wu,et al.  A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks , 2018, IEEE Access.

[12]  Ritu Bala,et al.  A REVIEW ON KDD CUP99 AND NSL-KDD DATASET , 2019, International Journal of Advanced Research in Computer Science.

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

[14]  Pooja Chaudhary,et al.  DDoS Detection Framework in Resource Constrained Internet of Things Domain , 2019, 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE).

[15]  Majd Latah,et al.  Towards an Efficient Anomaly-Based Intrusion Detection for Software-Defined Networks , 2018, IET Networks.

[16]  Geir M. Køien,et al.  Cyber Security and the Internet of Things: Vulnerabilities, Threats, Intruders and Attacks , 2015, J. Cyber Secur. Mobil..

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

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

[19]  Brij B. Gupta,et al.  A novel ECC-based lightweight authentication protocol for internet of things devices , 2019 .

[20]  Sajjan G. Shiva,et al.  Comparative Analysis of ML Classifiers for Network Intrusion Detection , 2019, ICICT.

[21]  Yu Lasheng,et al.  Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection , 2018, IEEE Access.

[22]  Jiankun Hu,et al.  A holistic review of Network Anomaly Detection Systems: A comprehensive survey , 2019, J. Netw. Comput. Appl..

[23]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[24]  Plamen P. Angelov,et al.  Correntropy-Based Evolving Fuzzy Neural System , 2018, IEEE Transactions on Fuzzy Systems.

[25]  A Jesudoss,et al.  A SURVEY ON AUTHENTICATION ATTACKS AND COUNTERMEASURES IN A DISTRIBUTED ENVIRONMENT , 2014 .

[26]  Jiadong Ren,et al.  Building an Effective Intrusion Detection System by Using Hybrid Data Optimization Based on Machine Learning Algorithms , 2019, Secur. Commun. Networks.

[27]  Majd Latah,et al.  An efficient flow-based multi-level hybrid intrusion detection system for software-defined networks , 2018, CCF Transactions on Networking.

[28]  Alexander G. Eustis The Mirai Botnet and the Importance of IoT Device Security , 2019 .

[29]  Kandasamy Muniasamy,et al.  Improving the Accuracy of Intrusion Detection Using GAR-Forest with Feature Selection , 2015, FICTA.

[30]  Ivan Letteri,et al.  Security in the internet of things: botnet detection in software-defined networks by deep learning techniques , 2019, Int. J. High Perform. Comput. Netw..

[31]  Karim Afdel,et al.  Semi-supervised machine learning approach for DDoS detection , 2018, Applied Intelligence.

[32]  Adel Sabry Eesa,et al.  A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems , 2015, Expert Syst. Appl..

[33]  M. M. A. Hashem,et al.  Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches , 2019, Internet Things.

[34]  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.

[35]  Balachandra Muniyal,et al.  Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection , 2016 .

[36]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[37]  Xiaojiang Du,et al.  Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city , 2020, Future Gener. Comput. Syst..

[38]  Oladayo Olufemi Olakanmi,et al.  An Efficient Privacy-preserving Approach for Secure Verifiable Outsourced Computing on Untrusted Platforms , 2019, Int. J. Cloud Appl. Comput..

[39]  Verónica Bolón-Canedo,et al.  A review of feature selection methods in medical applications , 2019, Comput. Biol. Medicine.

[40]  Yixian Yang,et al.  Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks , 2019, Applied Sciences.

[41]  Naveen K. Chilamkurti,et al.  Survey on SDN based network intrusion detection system using machine learning approaches , 2018, Peer-to-Peer Networking and Applications.

[42]  Zhen Liu,et al.  An Adaptive Ensemble Machine Learning Model for Intrusion Detection , 2019, IEEE Access.

[43]  Gholamhossein Dastghaibyfard,et al.  Two-tier network anomaly detection model: a machine learning approach , 2017, Journal of Intelligent Information Systems.

[44]  Petros Spachos,et al.  Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey , 2020, J. Netw. Comput. Appl..

[45]  E. Karthikeyan,et al.  Sigmis: A Feature Selection Algorithm Using Correlation Based Method , 2012 .

[46]  Xiaoming Xu,et al.  A hybrid genetic algorithm for feature selection wrapper based on mutual information , 2007, Pattern Recognit. Lett..

[47]  Ünal Çavusoglu,et al.  A new hybrid approach for intrusion detection using machine learning methods , 2019, Applied Intelligence.

[48]  Ali Dehghantanha,et al.  A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks , 2019, IEEE Transactions on Emerging Topics in Computing.

[49]  Wei Cong,et al.  Anomaly intrusion detection based on PLS feature extraction and core vector machine , 2013, Knowl. Based Syst..

[50]  Adnan Shaout,et al.  An intelligent intrusion detection system , 2019, Applied Intelligence.

[51]  Brij B. Gupta,et al.  Security, privacy & efficiency of sustainable Cloud Computing for Big Data & IoT , 2018, Sustain. Comput. Informatics Syst..

[52]  Young-Ho Park,et al.  A Variable Impacts Measurement in Random Forest for Mobile Cloud Computing , 2017, Wirel. Commun. Mob. Comput..

[53]  Brij B. Gupta,et al.  IoT-Based Big Data Secure Management in the Fog Over a 6G Wireless Network , 2021, IEEE Internet of Things Journal.

[54]  Feng Liu,et al.  A Deep Learning Approach for Network Intrusion Detection Based on NSL-KDD Dataset , 2019, 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID).

[55]  Nhien-An Le-Khac,et al.  Security Considerations for Internet of Things: A Survey , 2020, SN Computer Science.

[56]  Ferat Sahin,et al.  A survey on feature selection methods , 2014, Comput. Electr. Eng..

[57]  Seema Shah,et al.  A Comprehensive Survey of Machine Learning-Based Network Intrusion Detection , 2018, Smart Intelligent Computing and Applications.

[58]  Paulus Insap Santosa,et al.  Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features , 2020, Electronics.

[59]  S. El-Rabaie,et al.  Feature Selection Ranking and Subset-Based Techniques with Different Classifiers for Intrusion Detection , 2020, Wirel. Pers. Commun..

[60]  Kuan-Ching Li,et al.  An intrusion detection approach based on improved deep belief network , 2020, Applied Intelligence.

[61]  Sheng Wang,et al.  BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset , 2020, IEEE Access.

[62]  Erhan Guven,et al.  A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection , 2016, IEEE Communications Surveys & Tutorials.

[63]  Phurivit Sangkatsanee,et al.  Practical real-time intrusion detection using machine learning approaches , 2011, Comput. Commun..

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

[65]  Elena Sitnikova,et al.  Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset , 2018, Future Gener. Comput. Syst..

[66]  Tim Watson,et al.  Hybrid feature selection technique for intrusion detection system , 2019, Int. J. High Perform. Comput. Netw..

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

[68]  Ivan Letteri,et al.  Security in the internet of things: botnet detection in software-defined networks by deep learning techniques , 2019 .