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Machine Learning for Cyber-Physical Security
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Machine Learning for Cyber-Physical Security

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (15 May 2020) | Viewed by 17518

Special Issue Editor

Dr. Muhammad Ismail

E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Engineering, Tennessee Tech University, Cookeville, TN, USA
Interests: smart grids; networking; cyber-physical security; blockchain; resource allocation; machine learning; optimization; stochastic modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Powered by advanced communication and computation technologies, our world is steadily transforming into an inter-connected cyber-physical system, which manifests itself in many domains including smart power grids, smart transportation systems, autonomous vehicles, industrial automation, health monitoring, etc. Such progress results in two implications. On one hand, this advancement has dramatically increased the attack surface and introduced new damaging types of cyber-attacks. On the other hand, data-driven techniques have been popular in detecting such cyber-attacks because of the vast streams of data available from the cyber-physical systems.

The adoption of machine learning techniques in cyber-security is highly motivated by the recent advancement in computational power and processing speed. However, this adoption is challenged by several issues. The first challenge is the limited access to benchmark datasets needed to develop and compare data-driven solutions. Furthermore, unified security measures need to be introduced to assess and compare various data-driven solutions. In addition, more attention should be given to developing privacy-preserving machine-learning models. Moreover, further investigations are required on the adoption of machine learning techniques to introduce novel attack and threat models.

This Special Issue aims to promote research in developing new machine-learning models for the security and privacy of cyber-physical systems and introducing new threat and attack models based on machine-learning techniques. Submissions can include original research, dataset collection and benchmarking, or surveys and tutorials. The research topics to be covered in this Special Issue include but are not limited to: 

  • Deep machine learning for security and privacy;
  • Privacy-preserving machine learning;
  • Adversarial machine learning in cyber security;
  • Reinforcement learning for security and privacy;
  • Data-driven access control;
  • Authentication using machine learning;
  • Cryptographic analysis with machine learning;
  • Malware, intrusion, spam, and phishing detection using machine learning ;
  • Threat and attack model generation using machine learning;
  • Penetration testing using machine learning;

Dr. Muhammad Ismail
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep machine learning
  • reinforcement learning
  • generative adversarial networks
  • privacy and security

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Published Papers (3 papers)

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Research

17 pages, 1726 KiB  
Article
Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption
by Dabeeruddin Syed, Shady S. Refaat and Othmane Bouhali
Information 2020, 11(7), 357; https://doi.org/10.3390/info11070357 - 8 Jul 2020
Cited by 19 | Viewed by 4745
Abstract
Deep learning models have been applied for varied electrical applications in smart grids with a high degree of reliability and accuracy. The development of deep learning models requires the historical data collected from several electric utilities during the training of the models. The [...] Read more.
Deep learning models have been applied for varied electrical applications in smart grids with a high degree of reliability and accuracy. The development of deep learning models requires the historical data collected from several electric utilities during the training of the models. The lack of historical data for training and testing of developed models, considering security and privacy policy restrictions, is considered one of the greatest challenges to machine learning-based techniques. The paper proposes the use of homomorphic encryption, which enables the possibility of training the deep learning and classical machine learning models whilst preserving the privacy and security of the data. The proposed methodology is tested for applications of fault identification and localization, and load forecasting in smart grids. The results for fault localization show that the classification accuracy of the proposed privacy-preserving deep learning model while using homomorphic encryption is 97–98%, which is close to 98–99% classification accuracy of the model on plain data. Additionally, for load forecasting application, the results show that RMSE using the homomorphic encryption model is 0.0352 MWh while RMSE without application of encryption in modeling is around 0.0248 MWh. Full article
(This article belongs to the Special Issue Machine Learning for Cyber-Physical Security)
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15 pages, 4236 KiB  
Article
Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques
by Ameema Zainab, Shady S. Refaat and Othmane Bouhali
Information 2020, 11(7), 344; https://doi.org/10.3390/info11070344 - 2 Jul 2020
Cited by 30 | Viewed by 6505
Abstract
The number of Internet of Things (IoT) devices is growing at a fast pace in smart homes, producing large amounts of data, which are mostly transferred over wireless communication channels. However, various IoT devices are vulnerable to different threats, such as cyber-attacks, fluctuating [...] Read more.
The number of Internet of Things (IoT) devices is growing at a fast pace in smart homes, producing large amounts of data, which are mostly transferred over wireless communication channels. However, various IoT devices are vulnerable to different threats, such as cyber-attacks, fluctuating network connections, leakage of information, etc. Statistical analysis and machine learning can play a vital role in detecting the anomalies in the data, which enhances the security level of the smart home IoT system which is the goal of this paper. This paper investigates the trustworthiness of the IoT devices sending house appliances’ readings, with the help of various parameters such as feature importance, root mean square error, hyper-parameter tuning, etc. A spamicity score was awarded to each of the IoT devices by the algorithm, based on the feature importance and the root mean square error score of the machine learning models to determine the trustworthiness of the device in the home network. A dataset publicly available for a smart home, along with weather conditions, is used for the methodology validation. The proposed algorithm is used to detect the spamicity score of the connected IoT devices in the network. The obtained results illustrate the efficacy of the proposed algorithm to analyze the time series data from the IoT devices for spam detection. Full article
(This article belongs to the Special Issue Machine Learning for Cyber-Physical Security)
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13 pages, 1115 KiB  
Article
Ensemble-Based Online Machine Learning Algorithms for Network Intrusion Detection Systems Using Streaming Data
by Nathan Martindale, Muhammad Ismail and Douglas A. Talbert
Information 2020, 11(6), 315; https://doi.org/10.3390/info11060315 - 11 Jun 2020
Cited by 25 | Viewed by 5613
Abstract
As new cyberattacks are launched against systems and networks on a daily basis, the ability for network intrusion detection systems to operate efficiently in the big data era has become critically important, particularly as more low-power Internet-of-Things (IoT) devices enter the market. This [...] Read more.
As new cyberattacks are launched against systems and networks on a daily basis, the ability for network intrusion detection systems to operate efficiently in the big data era has become critically important, particularly as more low-power Internet-of-Things (IoT) devices enter the market. This has motivated research in applying machine learning algorithms that can operate on streams of data, trained online or “live” on only a small amount of data kept in memory at a time, as opposed to the more classical approaches that are trained solely offline on all of the data at once. In this context, one important concept from machine learning for improving detection performance is the idea of “ensembles”, where a collection of machine learning algorithms are combined to compensate for their individual limitations and produce an overall superior algorithm. Unfortunately, existing research lacks proper performance comparison between homogeneous and heterogeneous online ensembles. Hence, this paper investigates several homogeneous and heterogeneous ensembles, proposes three novel online heterogeneous ensembles for intrusion detection, and compares their performance accuracy, run-time complexity, and response to concept drifts. Out of the proposed novel online ensembles, the heterogeneous ensemble consisting of an adaptive random forest of Hoeffding Trees combined with a Hoeffding Adaptive Tree performed the best, by dealing with concept drift in the most effective way. While this scheme is less accurate than a larger size adaptive random forest, it offered a marginally better run-time, which is beneficial for online training. Full article
(This article belongs to the Special Issue Machine Learning for Cyber-Physical Security)
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