Cybersecurity in SMEs: The Smart-Home/Office Use Case

Today, small and medium-sized enterprises (SME) can be considered as the new big target for cyber attacks, while the cybercrime prevention is often neglected within their environment. This paper aims to investigate the characteristics of cybersecurity threats in the Digital Innovation Hub (DIH) ecosystem of a Smart-Home/Office environment being constituted by SMEs that contains various smart-devices and IoT equipment, smart-grid components, employees' workstations and medium sized networking equipment. As the Cyber-security in such an ecosystem is greatly demanding and challenging because of the various communication layers and the different supported IoT devices, we introduce a more robust, resilient and effective cybersecurity solution that can be effortlessly tailored to each individual enterprise's evolving needs and can also speedily adapt/respond to the changing cyber threat landscape. Thus, this Cyber-security framework will be evaluated through three major types of Smart-Home/Office datasets and will be supported from SME/ICT clusters under the framework of the Secure and Private Smart Grid (SPEAR) H2020 project. The first promising results of our work indicate the potential of implementing strong defence mechanisms for SMEs' environments within DIHs.

[1]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[2]  Wojciech Zaremba,et al.  An Empirical Exploration of Recurrent Network Architectures , 2015, ICML.

[3]  Thomas H. Morris,et al.  Developing a Hybrid Intrusion Detection System Using Data Mining for Power Systems , 2015, IEEE Transactions on Smart Grid.

[4]  Ali A. Ghorbani,et al.  Characterization of Tor Traffic using Time based Features , 2017, ICISSP.

[5]  Lovekesh Vig,et al.  Long Short Term Memory Networks for Anomaly Detection in Time Series , 2015, ESANN.

[6]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[7]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[8]  Fu Xiao,et al.  Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data , 2018 .

[9]  Christian S. Jensen,et al.  Outlier Detection for Multidimensional Time Series Using Deep Neural Networks , 2018, 2018 19th IEEE International Conference on Mobile Data Management (MDM).

[10]  Thomas H. Morris,et al.  Classification of Disturbances and Cyber-Attacks in Power Systems Using Heterogeneous Time-Synchronized Data , 2015, IEEE Transactions on Industrial Informatics.

[11]  Jürgen Schmidhuber,et al.  Recurrent nets that time and count , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[12]  Ali A. Ghorbani,et al.  Characterization of Encrypted and VPN Traffic using Time-related Features , 2016, ICISSP.

[13]  Thomas H. Morris,et al.  Machine learning for power system disturbance and cyber-attack discrimination , 2014, 2014 7th International Symposium on Resilient Control Systems (ISRCS).

[14]  Dimitrios Tzovaras,et al.  Acceleration at the Edge for Supporting SMEs Security: The FORTIKA Paradigm , 2019, IEEE Communications Magazine.

[15]  Mark A. Buckner,et al.  An Evaluation of Machine Learning Methods to Detect Malicious SCADA Communications , 2013, 2013 12th International Conference on Machine Learning and Applications.

[16]  Oliver Niggemann,et al.  LSTM for Model-based Anomaly Detection in Cyber-Physical Systems , 2020, DX.

[17]  Julian Togelius,et al.  Evolving Memory Cell Structures for Sequence Learning , 2009, ICANN.

[18]  Salah Kabanda,et al.  How South African SMEs address cyber security: The case of web server logs and intrusion detection , 2016, 2016 IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies (EmergiTech).

[19]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[20]  Sridha Sridharan,et al.  Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection , 2017, Neural Networks.

[21]  Lovekesh Vig,et al.  LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection , 2016, ArXiv.

[22]  Mark Button,et al.  Cyber Security Breaches Survey 2018: Statistical Release , 2018 .

[23]  Benjamin Aziz,et al.  Predicting CyberSecurity Incidents using Machine Learning Algorithms: A Case Study of Korean SMEs , 2019, ICISSP.

[24]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[25]  Salah Kabanda,et al.  Exploring SME cybersecurity practices in developing countries , 2018, J. Organ. Comput. Electron. Commer..

[26]  Nathalie Japkowicz,et al.  Anomaly Detection in Automobile Control Network Data with Long Short-Term Memory Networks , 2016, 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[27]  Murat Aydos,et al.  A review on cyber security datasets for machine learning algorithms , 2017, 2017 IEEE International Conference on Big Data (Big Data).