A Collaborative Security Framework for Software-Defined Wireless Sensor Networks

With the advent of 5G, technologies such as Software-Defined Networks (SDNs) and Network Function Virtualization (NFV) have been developed to facilitate simple programmable control of Wireless Sensor Networks (WSNs). However, WSNs are typically deployed in potentially untrusted environments. Therefore, it is imperative to address the security challenges before they can be implemented. In this paper, we propose a software-defined security framework that combines intrusion prevention in conjunction with a collaborative anomaly detection systems. Initially, an IPS-based authentication process is designed to provide a lightweight intrusion prevention scheme in the data plane. Subsequently, a collaborative anomaly detection system is leveraged with the aim of supplying a cost-effective intrusion detection solution near the data plane. Moreover, to correlate the true positive alerts raised by the sensor nodes in the network edge, a Smart Monitoring System (SMS) is exploited in the control plane. The performance of the proposed model is evaluated under different security scenarios as well as compared with other methods, where the model’s high security and reduction of false alarms are demonstrated.

[1]  Liang Gong,et al.  An intelligent SDN framework for 5G heterogeneous networks , 2015, IEEE Communications Magazine.

[2]  Rajamanickam Murugesan,et al.  A node authentication clustering based security for ADHOC network , 2014, 2014 International Conference on Communication and Signal Processing.

[3]  P. Karthigaikumar,et al.  ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications , 2018, IEEE Access.

[4]  Patrick Solé,et al.  The Most Significant Bit of Maximum-Length Sequences Over BBZ2l: Autocorrelation and Imbalance , 2004, IEEE Trans. Inf. Theory.

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

[6]  Eduardo Alchieri,et al.  Evaluation of Distributed Denial of Service threat in the Internet of Things , 2016, 2016 IEEE 15th International Symposium on Network Computing and Applications (NCA).

[7]  Taeshik Shon,et al.  A hybrid machine learning approach to network anomaly detection , 2007, Inf. Sci..

[8]  Hai-Ning Liang,et al.  A Review of Multimodal Facial Biometric Authentication Methods in Mobile Devices and Their Application in Head Mounted Displays , 2018, 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).

[9]  Xiangjian He,et al.  Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm , 2016, IEEE Transactions on Computers.

[10]  Shau-Yin Tseng,et al.  Integrated design of AES (Advanced Encryption Standard) encrypter and decrypter , 2002, Proceedings IEEE International Conference on Application- Specific Systems, Architectures, and Processors.

[11]  Salvatore J. Stolfo,et al.  Cross-Domain Collaborative Anomaly Detection: So Far Yet So Close , 2011, RAID.

[12]  Xiangjian He,et al.  A Sybil Attack Detection Scheme for a Centralized Clustering-Based Hierarchical Network , 2015, 2015 IEEE Trustcom/BigDataSE/ISPA.

[13]  Mianxiong Dong,et al.  ActiveTrust: Secure and Trustable Routing in Wireless Sensor Networks , 2016, IEEE Transactions on Information Forensics and Security.

[14]  Muxiang Zhang,et al.  Security analysis and enhancements of 3GPP authentication and key agreement protocol , 2005, IEEE Transactions on Wireless Communications.

[15]  Chiara Petrioli,et al.  Online Energy Harvesting Prediction in Environmentally Powered Wireless Sensor Networks , 2016, IEEE Sensors Journal.

[16]  Vishal M. Patel,et al.  Efficient and Low Latency Detection of Intruders in Mobile Active Authentication , 2018, IEEE Transactions on Information Forensics and Security.

[17]  Azeddine Bilami,et al.  A Cross-Layer Watermarking-Based Mechanism for Data Aggregation Integrity in Heterogeneous WSNs , 2015, IEEE Communications Letters.

[18]  Georges Kaddoum,et al.  Cross-Layer Authentication Protocol Design for Ultra-Dense 5G HetNets , 2018, 2018 IEEE International Conference on Communications (ICC).

[19]  Sandra Scott-Hayward,et al.  Tennison: A Distributed SDN Framework for Scalable Network Security , 2018, IEEE Journal on Selected Areas in Communications.

[20]  Guangjie Han,et al.  IDSEP: a novel intrusion detection scheme based on energy prediction in cluster-based wireless sensor networks , 2013, IET Inf. Secur..

[21]  Mansoor Ahmed,et al.  Towards a formally verified zero watermarking scheme for data integrity in the Internet of Things based-wireless sensor networks , 2017, Future Gener. Comput. Syst..

[22]  Abdallah Makhoul,et al.  Self-Adaptive Data Collection and Fusion for Health Monitoring Based on Body Sensor Networks , 2016, IEEE Transactions on Industrial Informatics.

[23]  Samuel Kounev,et al.  Quantifying the Attack Detection Accuracy of Intrusion Detection Systems in Virtualized Environments , 2016, 2016 IEEE 27th International Symposium on Software Reliability Engineering (ISSRE).

[24]  Gaurav Jaswal,et al.  Multimodal Biometric Authentication System Using Hand Shape, Palm Print, and Hand Geometry , 2019 .

[25]  Mohamed F. Younis,et al.  Energy-aware routing in cluster-based sensor networks , 2002, Proceedings. 10th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunications Systems.

[26]  Gerhard P. Hancke,et al.  A Survey on Software-Defined Wireless Sensor Networks: Challenges and Design Requirements , 2017, IEEE Access.

[27]  Mubashir Husain Rehmani,et al.  Applications of wireless sensor networks for urban areas: A survey , 2016, J. Netw. Comput. Appl..

[28]  Andrei V. Gurtov,et al.  Securing the control channel of software-defined mobile networks , 2014, Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014.

[29]  Victor C. M. Leung,et al.  Collaborative Location-Based Sleep Scheduling for Wireless Sensor Networks Integratedwith Mobile Cloud Computing , 2015, IEEE Transactions on Computers.

[30]  Tao Yang,et al.  Intrusion Detection System for Hybrid DoS Attacks using Energy Trust in Wireless Sensor Networks , 2018 .

[31]  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).

[32]  Shashi Gurung,et al.  A review of black-hole attack mitigation techniques and its drawbacks in Mobile Ad-hoc Network , 2017, 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

[33]  Gerhard P. Hancke,et al.  Security in software-defined wireless sensor networks: Threats, challenges and potential solutions , 2017, 2017 IEEE 15th International Conference on Industrial Informatics (INDIN).

[34]  D. Saraswady,et al.  Biometric Authentication Using Fused Multimodal Biometric , 2016 .

[35]  Ping Wang,et al.  Anonymous Two-Factor Authentication in Distributed Systems: Certain Goals Are Beyond Attainment , 2015, IEEE Transactions on Dependable and Secure Computing.

[36]  Yang Yu,et al.  A Hybrid Methodologies for Intrusion Detection Based Deep Neural Network with Support Vector Machine and Clustering Technique , 2016 .

[37]  E. G. Rajan,et al.  Comprehensive analysis of security attacks and intrusion detection system in wireless sensor networks , 2016, 2016 2nd International Conference on Next Generation Computing Technologies (NGCT).

[38]  H. Haken,et al.  Chapman-Kolmogorov equation and path integrals for discrete chaos in presence of noise , 1981 .

[39]  Ashraf Matrawy,et al.  Smart wireless sensor network management based on software-defined networking , 2014, 2014 27th Biennial Symposium on Communications (QBSC).

[40]  Chin-Chen Chang,et al.  Finding optimal least-significant-bit substitution in image hiding by dynamic programming strategy , 2003, Pattern Recognit..

[41]  Shahid Mumtaz,et al.  Energy Prediction Based MAC Layer Optimization for Harvesting Enabled WSNs in Smart Cities , 2018, 2018 IEEE 87th Vehicular Technology Conference (VTC Spring).

[42]  Gamze Uslu,et al.  DS+: Reliable Distributed Snapshot Algorithm for Wireless Sensor Networks , 2013, J. Comput. Networks Commun..

[43]  Dong-Seong Kim,et al.  Overhead reduction scheme for SDN-based Data Center Networks , 2019, Comput. Stand. Interfaces.

[44]  Milos S. Stankovic,et al.  A Distributed Support Vector Machine Learning Over Wireless Sensor Networks , 2015, IEEE Transactions on Cybernetics.