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Application of Data Analysis to Network Security

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 11240

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Special Issue Editors

Dr. Daniel Ramotsoela

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Guest Editor

Department of Electrical Engineering, University of Cape Town, Cape Town 7700, South Africa
Interests: system security; machine learning; Internet of Things

Prof. Dr. Adnan M. Abu-Mahfouz

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Guest Editor

Council for Scientific and Industrial Research, Pretoria 0184, South Africa
Interests: wireless sensor and actuator networks; low-power wide-area networks; software-defined wireless sensor networks; cognitive radio; network security; network management; sensor/actuator node development

Dr. Bruno Silva

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Guest Editor

Department of Computer Science, City University of Hong Kong, Hong Kong, China
Interests: Internet of Things; mobile computing; indoor localization

Dr. Umair Mujtaba Qureshi

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Guest Editor

School of Professional Education and Executive Development (SPEED), The Hong Kong Polytechnic University, Hong Kong, China
Interests: cyber physical systems; Internet of Things; software-defined networks

Dr. Zuneera Umair

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Guest Editor

Computer Science, Hong Kong Baptist University, Hong Kong, China
Interests: data science and analytics; Internet of Things; software-defined networks

Special Issue Information

Dear Colleagues,

Access to internet technology has rapidly developed across the world, resulting in several new industries. As the world becomes more interconnected, our reliance on smart devices will increase and everyday tasks will be automated, spurred on by Internet of Things (IoT) technologies and machine learning. This will also improve the efficiency of the industrial sector where cyber-physical systems (CPS) and cloud technologies are increasingly being deployed.

The increased productivity and convenience provided by these technologies will however increase the vulnerability to cyber-attacks from intruders with malicious intent. This is because these systems depend on reliable data for their operations and, if compromised, this could result in disastrous consequences in the applications where they are deployed.

Thus, there is a need for new and intelligent approaches to protecting system resources. Consequently, the purpose of this Special Issue is to invite the most recent advancements, fresh views, problems, and future directions in the application of data analysis to network security applications.

The topics of interest include, but are not limited to:

Intrusion detection and prevention systems;
Honeypots;
Distributed denial of service attack (DDoS) detection and prevention;
Block chain technology;
Applied cryptography;
Machine learning for cyber security;
Attack surface analysis;
Secure key distribution;
Statistical anomaly detection;
Cyber security in telecommunication networks.

Dr. Daniel Ramotsoela
Prof. Dr. Adnan M. Abu-Mahfouz
Dr. Bruno Silva
Dr. Umair Mujtaba Qureshi
Dr. Zuneera Umair
Guest Editors

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

machine learning
network security
intrusion detection and prevention systems
Internet of Things security
cyber-physical security
blockchain

Published Papers (5 papers)

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Research

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18 pages, 561 KiB

Open AccessArticle

Robust Intrusion Detection for Industrial Control Systems Using Improved Autoencoder and Bayesian Gaussian Mixture Model

by Chao Wang, Hongri Liu, Chao Li, Yunxiao Sun, Wenting Wang and Bailing Wang

Mathematics 2023, 11(9), 2048; https://doi.org/10.3390/math11092048 - 26 Apr 2023

Viewed by 1023

Abstract

Machine learning-based intrusion detection systems are an effective way to cope with the increasing security threats faced by industrial control systems. Considering that it is hard and expensive to obtain attack data, it is more reasonable to develop a model trained with only normal data. However, both high-dimensional data and the presence of outliers in the training set result in efficiency degradation. In this research, we present a hybrid intrusion detection method to overcome these two problems. First, we created an improved autoencoder that incorporates the deep support vector data description (Deep SVDD) loss into the training of the autoencoder. Under the combination of Deep SVDD loss and reconstruction loss, the novel autoencoder learns a more compact latent representation from high-dimensional data. The density-based spatial clustering of applications with noise algorithm is then used to remove potential outliers in the training data. Finally, a Bayesian Gaussian mixture model is used to identify anomalies. It learns the distribution of the filtered training data and uses the probabilities to classify normal and anomalous samples. We conducted a series of experiments on two intrusion detection datasets to assess performance. The proposed model performs better than other baseline methods when dealing with high-dimensional and contaminated data. Full article

(This article belongs to the Special Issue Application of Data Analysis to Network Security)

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24 pages, 4504 KiB

Open AccessArticle

Intelligent Proof-of-Trustworthiness-Based Secure Safety Message Dissemination Scheme for Vehicular Ad Hoc Networks Using Blockchain and Deep Learning Techniques

by Fuad A. Ghaleb, Waleed Ali, Bander Ali Saleh Al-Rimy and Sharaf J. Malebary

Mathematics 2023, 11(7), 1704; https://doi.org/10.3390/math11071704 - 02 Apr 2023

Cited by 1 | Viewed by 1366

Abstract

Vehicular ad hoc networks have emerged as the main building block for the future cooperative intelligent transportation system (cITS) to improve road safety and traffic efficiency and to provide passenger comfort. However, vehicular networks are decentralized, characterized by high mobility and dynamicity, and vehicles move in a hostile environment; such characteristics make VANET applications suffer many security and communication issues. Recently, blockchain has been suggested to solve several VANET issues including the dissemination of trustworthy life-threatening information. However, existing dissemination schemes are inefficient for safety messages and are vulnerable to malicious nodes and rely on the majority of honest assumptions. In the VANET context, adversaries may collude to broadcast false information causing serious safety threats. This study proposes an intelligent proof-of-trustworthiness-based secure safety message dissemination scheme (PoTMDS) to efficiently share only trustworthy messages. The consistency and plausibility of the message were evaluated based on a predictive model developed using a convolutional neural network and signal properties such as the received signal strength and angle of arrival. A blockchain-based data dissemination scheme was developed to share critical messages. Each vehicle calculates the proof of trustworthiness of the disseminated messages by comparing the received message with the output of the prediction model. The results showed that the proposed scheme reduced the consensus delay by 58% and improved the detection accuracy by 7.8%. Therefore, the proposed scheme can have an important role in improving the applications of future cITS. Full article

(This article belongs to the Special Issue Application of Data Analysis to Network Security)

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22 pages, 973 KiB

Open AccessArticle

Deep Learning-Based Malicious Smart Contract and Intrusion Detection System for IoT Environment

by Harshit Shah, Dhruvil Shah, Nilesh Kumar Jadav, Rajesh Gupta, Sudeep Tanwar, Osama Alfarraj, Amr Tolba, Maria Simona Raboaca and Verdes Marina

Mathematics 2023, 11(2), 418; https://doi.org/10.3390/math11020418 - 12 Jan 2023

Cited by 14 | Viewed by 3527

Abstract

The Internet of Things (IoT) is a key enabler technology that recently received significant attention from the scientific community across the globe. It helps transform everyone’s life by connecting physical and virtual devices with each other to offer staggering benefits, such as automation and control, higher productivity, real-time information access, and improved efficiency. However, IoT devices and their accumulated data are susceptible to various security threats and vulnerabilities, such as data integrity, denial-of-service, interception, and information disclosure attacks. In recent years, the IoT with blockchain technology has seen rapid growth, where smart contracts play an essential role in validating IoT data. However, these smart contracts can be vulnerable and degrade the performance of IoT applications. Hence, besides offering indispensable features to ease human lives, there is also a need to confront IoT environment security attacks, especially data integrity attacks. Toward this aim, this paper proposed an artificial intelligence-based system model with a dual objective. It first detects the malicious user trying to compromise the IoT environment using a binary classification problem. Further, blockchain technology is utilized to offer tamper-proof storage to store non-malicious IoT data. However, a malicious user can exploit the blockchain-based smart contract to deteriorate the performance IoT environment. For that, this paper utilizes deep learning algorithms to classify malicious and non-malicious smart contracts. The proposed system model offers an end-to-end security pipeline through which the IoT data are disseminated to the recipient. Lastly, the proposed system model is evaluated by considering different assessment measures that comprise the training accuracy, training loss, classification measures (precision, recall, and F1 score), and receiver operating characteristic (ROC) curve. Full article

(This article belongs to the Special Issue Application of Data Analysis to Network Security)

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19 pages, 5994 KiB

Open AccessArticle

Intelligent Deep Learning for Anomaly-Based Intrusion Detection in IoT Smart Home Networks

by Nazia Butt, Ana Shahid, Kashif Naseer Qureshi, Sajjad Haider, Ashraf Osman Ibrahim, Faisal Binzagr and Noman Arshad

Mathematics 2022, 10(23), 4598; https://doi.org/10.3390/math10234598 - 04 Dec 2022

Cited by 8 | Viewed by 2075

Abstract

The Internet of Things (IoT) is a tremendous network based on connected smart devices. These networks sense and transmit data by using advanced communication standards and technologies. The smart home is one of the areas of IoT networks, where home appliances are connected to the internet and smart grids. However, these networks are at high risk in terms of security violations. Different kinds of attacks have been conducted on these networks where the user lost their data. Intrusion detection systems (IDSs) are used to detect and prevent cyberattacks. These systems are based on machine and deep learning techniques and still suffer from fitting or overfitting issues. This paper proposes a novel solution for anomaly-based intrusion detection for smart home networks. The proposed model addresses overfitting/underfitting issues and ensures high performance in terms of hybridization. The proposed solution uses feature selection and hyperparameter tuning and was tested with an existing dataset. The experimental results indicated a significant increase in performance while minimizing misclassification and other limitations as compared to state-of-the-art solutions. Full article

(This article belongs to the Special Issue Application of Data Analysis to Network Security)

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Review

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19 pages, 9356 KiB

Open AccessReview

A Comprehensive Analysis of LoRaWAN Key Security Models and Possible Attack Solutions

by Koketso Ntshabele, Bassey Isong, Naison Gasela and Adnan M. Abu-Mahfouz

Mathematics 2022, 10(19), 3421; https://doi.org/10.3390/math10193421 - 21 Sep 2022

Viewed by 1987

Abstract

Low-Power Wide-Area Network (LPWAN) is a wireless WAN technology that connects low-powered and low-bandwidth devices with low bit rates atop Long Ranges (LoRa). It is characterized by improved scalability, wide area coverage, and low power consumption, which are beneficial to resource-constrained devices on the Internet of Things (IoT) for effective communication and security. Security in Long-Range Wide-Area Networks (LoRaWAN) widely employs Advanced Encryption Standard (AES) 128-bit symmetric encryption as the accepted security standard for a key generation that secures communication and entities. However, designing an efficient key manifestation and management model is still a challenge as different designs are based on different research objectives. To date, there is no global and well-accepted LoRaWAN security model for all applications. Thus, there is a need to continually improve the LoRaWAN security model. This paper, therefore, performed an in-depth analysis of some existing LoRaWAN key security models to identify security challenges affecting these security models and assess the strengths and weaknesses of the proposed solutions. The goal is to improve some of the existing LoRaWAN security models by analysing and bringing together several challenges that affect them. Several relevant studies were collected and analysed; the analysis shows that though there are few research works in this area, several existing LoRaWAN security models are not immune to attacks. Symmetry encryption is found to be the most used approach to manage key security due to its less computational operations. Moreover, it is possible to improve existing key security models in LPWAN with consideration of the resource constrained. Again, trusted third parties for key management were also widely used to defend against possible attacks and minimize operational complexities. We, therefore, recommend the design of lightweight and less complex LPWAN security models to sustain the lifespan of LPWAN devices. Full article

(This article belongs to the Special Issue Application of Data Analysis to Network Security)

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