Deep learning in cyber security for internet of things

Cyber threats are a showstopper for Internet of Things (IoT) which has recently gained popularity. Network layer attacks on IoT can cause significant disruptions and loss of information. Among such attacks, routing attacks are especially hard to defend against because of the ad-hoc nature of IoT systems and resource constraints of IoT devices. Hence a an efficient approach for detecting and predicting IoT attacks is needed. For the security of IoT, detecting malicious attacks is vital to avoid of unintended consequences such as lack of availability, integrity and confidentiality. For secure IoT needs a system that is able to robust detection against routing attacks. We propose a deep-learning based for continuous security monitoring analysis for IoT. Application of deep learning for cyber-security in IoT requires the availability of substantial IoT attack data, however the lack of IoT attack data is an important issue. In our study, the Cooja IoT simulator has been utilized for generation of high-fidelity attack data, within IoT networks ranging from up to 1000 nodes. We propose a highly scalable, deep-learning based attack detection methodology for detection of IoT routing attacks with high accuracy and precision.

[1]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[2]  Fengjun Li,et al.  Cyber-Physical Systems Security—A Survey , 2017, IEEE Internet of Things Journal.

[3]  Andrew W. Moore,et al.  Rule-based anomaly pattern detection for detecting disease outbreaks , 2002, AAAI/IAAI.

[4]  Ali A. Ghorbani,et al.  Network Intrusion Detection and Prevention - Concepts and Techniques , 2010, Advances in Information Security.

[5]  Thiemo Voigt,et al.  Routing Attacks and Countermeasures in the RPL-Based Internet of Things , 2013, Int. J. Distributed Sens. Networks.

[6]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[7]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[8]  Adam Dunkels,et al.  Cross-Level Sensor Network Simulation with COOJA , 2006, Proceedings. 2006 31st IEEE Conference on Local Computer Networks.

[9]  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).

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

[11]  Kay Römer,et al.  The design space of wireless sensor networks , 2004, IEEE Wireless Communications.

[12]  Naveen K. Chilamkurti,et al.  Distributed attack detection scheme using deep learning approach for Internet of Things , 2017, Future Gener. Comput. Syst..

[13]  John A. Stankovic,et al.  Security in wireless sensor networks , 2004, SASN '04.

[14]  Teerawat Issariyakul,et al.  Introduction to Network Simulator NS2 , 2008 .

[15]  Valerio Formicola,et al.  Enhancing Intrusion Detection in Wireless Sensor Networks through Decision Trees , 2013, EWDC.

[16]  Rajamani Vayanaperumal,et al.  Detecting and Preventing Sybil Attacks in Wireless Sensor Networks Using Message Authentication and Passing Method , 2015, TheScientificWorldJournal.

[17]  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.

[18]  Yuancheng Li,et al.  A Hybrid Malicious Code Detection Method based on Deep Learning , 2015 .

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

[20]  Marcus A. Maloof.  MACHINE LEARNING AND DATA MINING FOR COMPUTER SECURITY: METHODS AND APPLICATIONS , 2011 .

[21]  Maurizio A. Spirito,et al.  DEMO: An IDS framework for internet of things empowered by 6LoWPAN , 2013, CCS.

[22]  Arwa Alrawais,et al.  Fog Computing for the Internet of Things: Security and Privacy Issues , 2017, IEEE Internet Computing.

[23]  Siarhei Kuryla,et al.  RPL: IPv6 Routing Protocol for Low power and Lossy Networks , 2010 .

[24]  Tarik Taleb,et al.  Detecting and avoiding wormhole attacks in wireless ad hoc networks , 2008, IEEE Communications Magazine.

[25]  Marcos Augusto M. Vieira,et al.  Survey on wireless sensor network devices , 2003, EFTA 2003. 2003 IEEE Conference on Emerging Technologies and Factory Automation. Proceedings (Cat. No.03TH8696).

[26]  Mansoor Alam,et al.  A Deep Learning Approach for Network Intrusion Detection System , 2016, EAI Endorsed Trans. Security Safety.

[27]  Maninder Singh,et al.  Efficient hybrid technique for detecting zero-day polymorphic worms , 2014, 2014 IEEE International Advance Computing Conference (IACC).

[28]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[29]  Nirvana Meratnia,et al.  Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine , 2013, Ad Hoc Networks.

[30]  Balachander Krishnamurthy,et al.  Rule-Based Anomaly Detection on IP Flows , 2009, IEEE INFOCOM 2009.

[31]  C. Karlof,et al.  Secure routing in wireless sensor networks: attacks and countermeasures , 2003, Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003..

[32]  Remi Badonnel,et al.  A Taxonomy of Attacks in RPL-based Internet of Things , 2016, Int. J. Netw. Secur..

[33]  Tassos Dimitriou,et al.  Intrusion Detection of Sinkhole Attacks in Wireless Sensor Networks , 2007, ALGOSENSORS.

[34]  Sargolzaei Arman,et al.  Epilepsy, a Cyberattack on Brains' Networked Control System , 2016 .

[35]  Salim Hariri,et al.  Analyzing Attacks in Wireless Ad Hoc Network with Self-Organizing Maps , 2007, Fifth Annual Conference on Communication Networks and Services Research (CNSR '07).

[36]  Mohsen Guizani,et al.  Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[37]  Omer F. Rana,et al.  Predicting client-side attacks via behaviour analysis using honeypot data , 2011, 2011 7th International Conference on Next Generation Web Services Practices.

[38]  Gaël Varoquaux,et al.  The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.

[39]  Wei-Yang Lin,et al.  Intrusion detection by machine learning: A review , 2009, Expert Syst. Appl..

[40]  Richard A. Kemmerer,et al.  State Transition Analysis: A Rule-Based Intrusion Detection Approach , 1995, IEEE Trans. Software Eng..

[41]  Adam Dunkels,et al.  Contiki - a lightweight and flexible operating system for tiny networked sensors , 2004, 29th Annual IEEE International Conference on Local Computer Networks.

[42]  Anand Nayyar,et al.  Mint-Route to Avoid Congestion in Wireless Sensor Network , 2014 .

[43]  Insup Lee,et al.  Cyber-physical systems: The next computing revolution , 2010, Design Automation Conference.

[44]  Y.A. Sekercioglu,et al.  Detecting Selective Forwarding Attacks in Wireless Sensor Networks using Support Vector Machines , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[45]  Zheng Zhang,et al.  HIDE : a Hierarchical Network Intrusion Detection System Using Statistical Preprocessing and Neural Network Classification , 2001 .

[46]  Thiemo Voigt,et al.  SVELTE: Real-time intrusion detection in the Internet of Things , 2013, Ad Hoc Networks.

[47]  Rodrigo Roman,et al.  Securing the Internet of Things , 2017, Smart Cards, Tokens, Security and Applications, 2nd Ed..

[48]  Risto Miikkulainen,et al.  Intrusion Detection with Neural Networks , 1997, NIPS.

[49]  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.

[50]  Stefano Chessa,et al.  Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards , 2007, Comput. Commun..

[51]  Hon Sun Chiu,et al.  Real Time Intrusion and Wormhole Attack Detection in Internet of Things , 2015 .

[52]  Ivan Martinovic,et al.  Crowdsourcing security for wireless air traffic communications , 2017, 2017 9th International Conference on Cyber Conflict (CyCon).

[53]  Je-Won Kang,et al.  Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security , 2016, PloS one.

[54]  Ran Wolff,et al.  In-Network Outlier Detection in Wireless Sensor Networks , 2006, ICDCS.

[55]  Kevin B. Korb,et al.  Anomaly detection in vessel tracks using Bayesian networks , 2014, Int. J. Approx. Reason..

[56]  Marimuthu Palaniswami,et al.  Quarter Sphere Based Distributed Anomaly Detection in Wireless Sensor Networks , 2007, 2007 IEEE International Conference on Communications.

[57]  Sema F. Oktug,et al.  RPL version number attacks: In-depth study , 2016, NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium.

[58]  Wes McKinney,et al.  Data Structures for Statistical Computing in Python , 2010, SciPy.

[59]  M. Hemanth Kumar,et al.  Cyber security and the Internet of Things: Vulnerabilities, threats, intruders and attacks , 2019 .

[60]  D. Janakiram,et al.  Outlier Detection in Wireless Sensor Networks using Bayesian Belief Networks , 2006, 2006 1st International Conference on Communication Systems Software & Middleware.

[61]  Camilius A. Sanga,et al.  A Survey on Detection of Sinkhole Attack in Wireless Sensor Network , 2015, ArXiv.

[62]  Alexander Perez-Pons,et al.  HMM-Based Intrusion Detection System for Software Defined Networking , 2016, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA).

[63]  Zhang Yang,et al.  An online outlier detection technique for wireless sensor networks using unsupervised quarter-sphere support vector machine , 2008, 2008 International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[64]  Philippe Jacquet,et al.  Optimized Link State Routing Protocol (OLSR) , 2003, RFC.

[65]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

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

[67]  José Manuel Moya,et al.  Detecting Unknown Attacks in Wireless Sensor Networks That Contain Mobile Nodes , 2012, Sensors.

[68]  Tanupriya Choudhury,et al.  Securing the Internet of Things: A proposed framework , 2017, 2017 International Conference on Computing, Communication and Automation (ICCCA).

[69]  Luigi Coppolino,et al.  An Intrusion Detection System for Critical Information Infrastructures using Wireless Sensor Network technologies , 2010, 2010 5th International Conference on Critical Infrastructure (CRIS).

[70]  Ken Ferens,et al.  Network Intrusion Detection Using Machine Learning , 2016 .

[71]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[72]  Li Deng,et al.  A tutorial survey of architectures, algorithms, and applications for deep learning , 2014, APSIPA Transactions on Signal and Information Processing.

[73]  Edgar H. Callaway,et al.  Wireless Sensor Networks: Architectures and Protocols , 2003 .

[74]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[75]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[76]  S. Shankar Sastry,et al.  A Taxonomy of Cyber Attacks on SCADA Systems , 2011, 2011 International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing.