Framework for Mobile Internet of Things Security Monitoring Based on Big Data Processing and Machine Learning

The paper discusses a new framework combining the possibilities of Big Data processing and machine leaning developed for security monitoring of mobile Internet of Things. The mathematical foundations and the problem statement are considered. The description of the used data set and the architecture of proposed security monitoring framework are provided. The framework specifies several machine learning mechanisms intended for solving classification tasks. The classifier operation results are exposed to plurality voting, weighted voting, and soft voting. The framework performance and accuracy is assessed experimentally.

[1]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[2]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[3]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[4]  George A. F. Seber,et al.  Linear regression analysis , 1977 .

[5]  Christian Bauckhage,et al.  Malware Detection on Mobile Devices Using Distributed Machine Learning , 2010, 2010 20th International Conference on Pattern Recognition.

[6]  Anuradha Pillai,et al.  Applications of Machine Learning in Cyber Security , 2020, Handbook of Research on Machine and Deep Learning Applications for Cyber Security.

[7]  Hwee Pink Tan,et al.  Mobile big data analytics using deep learning and apache spark , 2016, IEEE Network.

[8]  Ehud Gudes,et al.  Security and privacy aspects in MapReduce on clouds: A survey , 2016, Comput. Sci. Rev..

[9]  Fabio Roli,et al.  Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371) , 2012, Dagstuhl Reports.

[10]  Chaitanya P. Garware.  A Security Framework for Big Data Computing through Distributed Cloud Data Centres in G-Hadoop , 2016 .

[11]  Philip K. Chan,et al.  Machine Learning for Computer Security , 2006, J. Mach. Learn. Res..

[12]  Liyuan Liu,et al.  Deep learning approach for cyberattack detection , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[13]  Tariq Rahim Soomro,et al.  Big Data Analysis: Apache Spark Perspective , 2015 .

[14]  Igor V. Kotenko,et al.  Network Anomaly Detection Based on an Ensemble of Adaptive Binary Classifiers , 2017, MMM-ACNS.

[15]  Igor Kotenko,et al.  Hybridization of computational intelligence methods for attack detection in computer networks , 2017, J. Comput. Sci..

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

[17]  Mi-Jin Kim,et al.  Development of Real-time Big Data Analysis System and a Case Study on the Application of Information in a Medical Institution , 2015 .

[18]  Eryk Dutkiewicz,et al.  Cyberattack detection in mobile cloud computing: A deep learning approach , 2017, 2018 IEEE Wireless Communications and Networking Conference (WCNC).

[19]  Latifur Khan,et al.  A Machine Learning Approach to Android Malware Detection , 2012, 2012 European Intelligence and Security Informatics Conference.

[20]  Quan Pan,et al.  Combination of Classifiers With Optimal Weight Based on Evidential Reasoning , 2018, IEEE Transactions on Fuzzy Systems.

[21]  Igor V. Kotenko,et al.  Aggregation of elastic stack instruments for collecting, storing and processing of security information and events , 2017, 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).

[22]  Harry Zhang,et al.  The Optimality of Naive Bayes , 2004, FLAIRS.

[23]  Igor V. Kotenko,et al.  Parallel big data processing system for security monitoring in Internet of Things networks , 2017, J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl..

[24]  Z. Reitermanová.  Data Splitting , 2010 .

[25]  Jörg Sander.  Density-Based Clustering , 2017, Encyclopedia of Machine Learning and Data Mining.

[26]  Andrew Y. Ng,et al.  Learning Feature Representations with K-Means , 2012, Neural Networks: Tricks of the Trade.

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

[28]  Alex Holmes.  Hadoop in Practice , 2012 .

[29]  Quan Pan,et al.  Classifier Fusion With Contextual Reliability Evaluation , 2018, IEEE Transactions on Cybernetics.