Quantifying the need for supervised machine learning in conducting live forensic analysis of emergent configurations (ECO) in IoT environments

Alkhabbas, 2018, Eco-iot: An architectural approach for realizing emergent configurations in the internet of things, European Conference on Software Architecture, 86, 10.1007/978-3-030-00761-4_6 Alkhabbas, 2017, Architecting emergent configurations in the internet of things, 2017 IEEE International Conference on Software Architecture (ICSA), 221, 10.1109/ICSA.2017.37 Alkhabbas, 2017, Emergent configurations in the internet of things as system of systems, 2017 IEEE/ACM Joint 5th International Workshop on Software Engineering for Systems-of-Systems and 11th Workshop on Distributed Software Development, Software Ecosystems and Systems-of-Systems. (JSOS), 70, 10.1109/JSOS.2017.6 Alkhabbas, 2017, A commitment-based approach to realize emergent configurations in the internet of things, 2017 IEEE International Conference on Software Architecture Workshops (ICSAW), 88, 10.1109/ICSAW.2017.55 Alkhabbas, 2016, Iot-based systems of systems, Proceedings of the 2nd edition of Swedish Workshop on the Engineering of Systems of Systems (SWESOS 2016) Wu, 2018, Bot detection using unsupervised machine learning, Microsystem Technologies, 24, 209, 10.1007/s00542-016-3237-0 Beebe, 2007, Digital forensic text string searching: Improving information retrieval effectiveness by thematically clustering search results, Digital Invest., 4, 49, 10.1016/j.diin.2007.06.005 Pajouh, 2016, A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in iot backbone networks, IEEE Transactions on Emerging Topics in Computing Tall’on-Ballesteros, 2014, Data mining methods applied to a digital forensics task for supervised machine learning, Computational Intelligence in Digital Forensics: Forensic Investigation and Applications, 413 Mohlala, 2017, User attribution based on keystroke dynamics in digital forensic readiness process, 2017 IEEE Conference on Application Information Network Security (AINS), 124, 10.1109/AINS.2017.8270436 Adeyemi, 2016, Understanding online behavior: exploring the probability of online personality trait using supervised machine-learning approach, Front. ICT, 3, 8, 10.3389/fict.2016.00008 Huang, 2016, Forensic analysis of distributed computing network based on decision values, 2016 International Symposium on Computer Consumer Control (IS3C), 423, 10.1109/IS3C.2016.113 Buccoli, 2014, Unsupervised feature learning for bootleg detection using deep learning architectures, 2014 IEEE International Workshop on Information Forensics Security (WIFS), 131, 10.1109/WIFS.2014.7084316 Bhide, 2015, i-learning iot: An intelligent self learning system for home automation using iot, 2015 International Conference on Communications and Signal Processing (ICCSP), 1763, 10.1109/ICCSP.2015.7322825 Ham, 2014, Linear svm-based android malware detection for reliable iot services, J. Appl. Math., 2014, 10.1155/2014/594501 Karie, 2019, Diverging deep learning cognitive computing techniques into cyber forensics, Forensic Sci. Int.: Synergy, 1, 61 Khan, 2008 Costantini, 2019, Digital forensics and investigations meet artificial intelligence, Ann. Math. Artif. Intel., 10.1007/s10472-019-09632-y Mitchell, 2010, The use of artificial intelligence in digital forensics: An introduction, Digital Evid. Elec. Signature L. Rev. Ray, 2018, A survey on internet of things architectures, J. King Saud Univ.-Comput. Inform. Sci. Khorashadizadeh, 2019, Generic 5g infrastructure for iot ecosystem, International Conference of Reliable Information and Communication Technology, 451 R.-C. Mihailescu, R. Spalazzese, C. Heyer, and P. Davidsson, A role-based approach for orchestrating emergent configurations in the internet of things, arXiv preprint arXiv:1809.09870 (2018). Kebande, 2016, A generic digital forensic investigation framework for internet of things (iot), 2016 IEEE 4th International Conference on Future Internet of Things Cloud (FiCloud), 356, 10.1109/FiCloud.2016.57 Li, 2019, Iot forensics: Amazon echo as a use case, IEEE Internet Things J., 6, 6487, 10.1109/JIOT.2019.2906946 Zhang, 2020, Iot botnet forensics: A comprehensive digital forensic case study on mirai botnet servers, Forensic Sci. Int.: Digital Invest., 32, 300926 Zhang, 2019 Zhang, 2019, How do i share my iot forensic experience with the broader community?. an automated knowledge sharing iot forensic platform, IEEE Internet of Things J., 6, 6850, 10.1109/JIOT.2019.2912118 Alkadi, 2020, A deep blockchain framework-enabled collaborative intrusion detection for protecting iot and cloud networks, IEEE Internet Things J. Saharkhizan, 2020, An ensemble of deep recurrent neural networks for detecting iot cyber attacks using network traffic, IEEE Internet Things J., 10.1109/JIOT.2020.2996425 Alkhabbas, 2018, Enacting emergent configurations in the iot through domain objects, International Conference on Service-Oriented Computing, 279 Mihailescu, 2016, Towards collaborative sensing using dynamic intelligent virtual sensors, 217 Tegen, 2019, Collaborative sensing with interactive learning using dynamic intelligent virtual sensors, Sensors, 19, 477, 10.3390/s19030477 Keramati, 2014, Improved churn prediction in telecommunication industry using data mining techniques, Appl. Soft Comput., 24, 994, 10.1016/j.asoc.2014.08.041 Cortes, 1995, Support-vector networks, Machine Learn., 20, 273, 10.1007/BF00994018 He, 2005, The inverse problem of support vector machines and its solution, 4322 Liu, 2008, Evaluating the supplier cooperative design ability using a novel support vector machine algorithm, 986 He, 2006, 3503 Dewi, 2017, Improving na”ive bayes performance in single image pap smear using weighted principal component analysis (wpca), 2017 International Conference on Computing, Engineering, and Design (ICCED), 1, 10.1109/CED.2017.8308130 Alfisahrin, 2013, Data mining techniques for optimization of liver disease classification, 379 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Garla, 2012, Ontology-guided feature engineering for clinical text classification, J. Biomed. Inform., 45, 992, 10.1016/j.jbi.2012.04.010 Dash, 1997, Feature selection for classification, Intelligent Data Anal., 1, 131, 10.3233/IDA-1997-1302 Narendra, 1977, A branch and bound algorithm for feature subset selection, IEEE Trans. Comput., 917 Hira, 2015, A review of feature selection and feature extraction methods applied on microarray data, Adv. Bioinform., 10.1155/2015/198363 Kira, 1992, The feature selection problem: Traditional methods and a new algorithm, Aaai, Vol. 2, 129 Aksoy, 2001, Feature normalization and likelihood-based similarity measures for image retrieval, Pattern Recognit. Lett., 22, 563, 10.1016/S0167-8655(00)00112-4 Ikuesan, 2020, A heuristics for http traffic identification in measuring user dissimilarity, Human-Intelligent Syst. Integration, 1 Al-Dhaqm, 2020, Categorization and organization of database forensic investigation processes, IEEE Access, 1 Ikuesan, 2019, Polychronicity tendency-based online behavioral signature, Int. J. Machine Learn. Cybernet., 10, 2103, 10.1007/s13042-017-0748-7 Adeyemi, 2016, Observing consistency in online communication patterns for user re-identification, PLOS ONE, 11, e0166930, 10.1371/journal.pone.0166930 Ikuesan, 2019, Digital behavioral-fingerprint for user attribution in digital forensics: Are we there yet?, Digital Invest., 30, 73, 10.1016/j.diin.2019.07.003