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Future Internet
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Privacy and Cybersecurity in the Artificial Intelligence Age
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Privacy and Cybersecurity in the Artificial Intelligence Age

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 16204

Special Issue Editor

Dr. Ishaani Priyadarshini

E-Mail Website
Guest Editor
School of Information, University of California, Berkeley, CA 94720-5800, USA
Interests: applied machine learning; technological singularity; deep learning; cybersecurity; internet of things

Special Issue Information

Dear Colleagues,

Many recent advancements in technology may be attributed to the ever-dynamic and fast-growing field of artificial intelligence. The unparalleled growth in AI-related technology promises improved quality of life and efficiency and spans several fields like healthcare, education, business, entertainment, engineering, and many more emerging fields. It encompasses studies such as applied machine learning, natural language processing, quantum computing, evolutionary algorithms, computer vision, deep learning and the internet of things among several other topics. While the progression in artificial intelligence certainly means new inventions and faster decisions, it also invites certain threats and risks associated not only with security and privacy but also with respect to trust and fairness in artificial intelligence. Information misuse and algorithmic irregularities raise serious questions on the ethical aspects of AI, and anticipate the possibility of increased rogue behavior among AI in the future. Many AI researchers foresee the oncoming of technological singularity in the future, and AI gone rogue could be catastrophic for society.

It is necessary to introduce assurances, solutions, trust and fairness across many applications of artificial intelligence. Fair data usage, explainable AI and privacy preservation measures can help minimize risks and establish reliability and integrity for such AI-based applications.

This Special Issue ‘Privacy and Cybersecurity in the Artificial Intelligence Age’ of Future Internet (ISSN 1999-5903) aims to gather research contributions from a wide range of disciplines concerning AI singularity, fairness, privacy and trust. Investigators are encouraged to contribute their original research articles with an emphasis on real life applications and review articles that may stimulate further research in this area to identify and address key scientific problems.

Topics include, but are not limited to:

  • Trustworthy artificial intelligence and machine learning;
  • AI Ethics
  • Privacy, trust, and fairness in AI
  • Machine Learning Applications for privacy, trust, and fairness;
  • Privacy preservation in AI
  • Explainable AI
  • AI Singularity
  • AI gone rogue
  • Societal implications of autonomous experimentation
  • Deploying machine learning and AI to enhance privacy
  • Accountable Machines
  • AI Safety and Privacy

Dr. Ishaani Priyadarshini
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. Future Internet 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

  • technological singularity
  • deep learning
  • machine learning
  • trustworthy AI
  • AI fairness
  • AI safety
  • accountable machines

Published Papers (4 papers)

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Research

18 pages, 7232 KiB  
Article
Exploiting Misconfiguration Vulnerabilities in Microsoft’s Azure Active Directory for Privilege Escalation Attacks
by Ibrahim Bu Haimed, Marwan Albahar and Ali Alzubaidi
Future Internet 2023, 15(7), 226; https://doi.org/10.3390/fi15070226 - 23 Jun 2023
Cited by 2 | Viewed by 2851
Abstract
Cloud services provided by Microsoft are growing rapidly in number and importance. Azure Active Directory (AAD) is becoming more important due to its role in facilitating identity management for cloud-based services. However, several risks and security issues have been associated with cloud systems [...] Read more.
Cloud services provided by Microsoft are growing rapidly in number and importance. Azure Active Directory (AAD) is becoming more important due to its role in facilitating identity management for cloud-based services. However, several risks and security issues have been associated with cloud systems due to vulnerabilities associated with identity management systems. In particular, misconfigurations could severely impact the security of cloud-based systems. Accordingly, this study identifies and experimentally evaluates exploitable misconfiguration vulnerabilities in Azure AD which can eventually lead to the risk of privilege escalation attacks. The study focuses on two scenarios: dynamic group settings and the activation of the Managed Identity feature on virtual devices. Through experimental evaluation, the research demonstrates the successful execution of these attacks, resulting in unauthorized access to sensitive information. Finally, we suggest several approaches to prevent such attacks by isolating sensitive systems to minimize the possibility of damage resulting from a misconfiguration accident and highlight the need for further studies. Full article
(This article belongs to the Special Issue Privacy and Cybersecurity in the Artificial Intelligence Age)
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15 pages, 6409 KiB  
Article
A Hybrid Deep Learning Model with Self-Improved Optimization Algorithm for Detection of Security Attacks in IoT Environment
by Amit Sagu, Nasib Singh Gill, Preeti Gulia, Jyotir Moy Chatterjee and Ishaani Priyadarshini
Future Internet 2022, 14(10), 301; https://doi.org/10.3390/fi14100301 - 19 Oct 2022
Cited by 12 | Viewed by 2172
Abstract
With the growth of the Internet of Things (IoT), security attacks are also rising gradually. Numerous centralized mechanisms have been introduced in the recent past for the detection of attacks in IoT, in which an attack recognition scheme is employed at the network’s [...] Read more.
With the growth of the Internet of Things (IoT), security attacks are also rising gradually. Numerous centralized mechanisms have been introduced in the recent past for the detection of attacks in IoT, in which an attack recognition scheme is employed at the network’s vital point, which gathers data from the network and categorizes it as “Attack” or “Normal”. Nevertheless, these schemes were unsuccessful in achieving noteworthy results due to the diverse necessities of IoT devices such as distribution, scalability, lower latency, and resource limits. The present paper proposes a hybrid model for the detection of attacks in an IoT environment that involves three stages. Initially, the higher-order statistical features (kurtosis, variance, moments), mutual information (MI), symmetric uncertainty, information gain ratio (IGR), and relief-based features are extracted. Then, detection takes place using Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (Bi-LSTM) to recognize the existence of network attacks. For improving the classification accuracy, the weights of Bi-LSTM are optimally tuned via a self-upgraded Cat and Mouse Optimizer (SU-CMO). The improvement of the employed scheme is established concerning a variety of metrics using two distinct datasets which comprise classification accuracy, and index, f-measure and MCC. In terms of all performance measures, the proposed model outperforms both traditional and state-of-the-art techniques. Full article
(This article belongs to the Special Issue Privacy and Cybersecurity in the Artificial Intelligence Age)
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18 pages, 2718 KiB  
Article
Automated Penetration Testing Framework for Smart-Home-Based IoT Devices
by Rohit Akhilesh, Oliver Bills, Naveen Chilamkurti and Mohammad Jabed Morshed Chowdhury
Future Internet 2022, 14(10), 276; https://doi.org/10.3390/fi14100276 - 27 Sep 2022
Cited by 13 | Viewed by 5619
Abstract
Security testing is fundamental to identifying security vulnerabilities on smart home-based IoT devices. For this, penetration testing is the most prominent and effective solution. However, testing the IoT manually is cumbersome and time-consuming. In addition, penetration testing requires a deep knowledge of the [...] Read more.
Security testing is fundamental to identifying security vulnerabilities on smart home-based IoT devices. For this, penetration testing is the most prominent and effective solution. However, testing the IoT manually is cumbersome and time-consuming. In addition, penetration testing requires a deep knowledge of the possible attacks and the available hacking tools. Therefore, this study emphasises building an automated penetration testing framework to discover the most common vulnerabilities in smart home-based IoT devices. This research involves exploring (studying) different IoT devices to select five devices for testing. Then, the common vulnerabilities for the five selected smart home-based IoT devices are examined, and the corresponding penetration testing tools required for the detection of these vulnerabilities are identified. The top five vulnerabilities are identified from the most common vulnerabilities, and accordingly, the corresponding tools for these vulnerabilities are discovered. These tools are combined using a script which is then implemented into a framework written in Python 3.6. The selected IoT devices are tested individually for known vulnerabilities using the proposed framework. For each vulnerability discovered in the device, the Common Vulnerability Scoring System (CVSS) Base score is calculated and the summation of these scores is taken to calculate the total score (for each device). In our experiment, we found that the Tp-Link Smart Bulb and the Tp-Link Smart Camera had the highest score and were the most vulnerable and the Google Home Mini had the least score and was the most secure device of all the devices. Finally, we conclude that our framework does not require technical expertise and thus can be used by common people. This will help improve the field of IoT security and ensure the security of smart homes to build a safe and secure future. Full article
(This article belongs to the Special Issue Privacy and Cybersecurity in the Artificial Intelligence Age)
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20 pages, 16086 KiB  
Article
Blockchain-Based Cloud-Enabled Security Monitoring Using Internet of Things in Smart Agriculture
by Rajasekhar Chaganti, Vijayakumar Varadarajan, Venkata Subbarao Gorantla, Thippa Reddy Gadekallu and Vinayakumar Ravi
Future Internet 2022, 14(9), 250; https://doi.org/10.3390/fi14090250 - 24 Aug 2022
Cited by 47 | Viewed by 4390
Abstract
The Internet of Things (IoT) has rapidly progressed in recent years and immensely influenced many industries in how they operate. Consequently, IoT technology has improved productivity in many sectors, and smart farming has also hugely benefited from the IoT. Smart farming enables precision [...] Read more.
The Internet of Things (IoT) has rapidly progressed in recent years and immensely influenced many industries in how they operate. Consequently, IoT technology has improved productivity in many sectors, and smart farming has also hugely benefited from the IoT. Smart farming enables precision agriculture, high crop yield, and the efficient utilization of natural resources to sustain for a longer time. Smart farming includes sensing capabilities, communication technologies to transmit the collected data from the sensors, and data analytics to extract meaningful information from the collected data. These modules will enable farmers to make intelligent decisions and gain profits. However, incorporating new technologies includes inheriting security and privacy consequences if they are not implemented in a secure manner, and smart farming is not an exception. Therefore, security monitoring is an essential component to be implemented for smart farming. In this paper, we propose a cloud-enabled smart-farm security monitoring framework to monitor device status and sensor anomalies effectively and mitigate security attacks using behavioral patterns. Additionally, a blockchain-based smart-contract application was implemented to securely store security-anomaly information and proactively mitigate similar attacks targeting other farms in the community. We implemented the security-monitoring-framework prototype for smart farms using Arduino Sensor Kit, ESP32, AWS cloud, and the smart contract on the Ethereum Rinkeby Test Network and evaluated network latency to monitor and respond to security events. The performance evaluation of the proposed framework showed that our solution could detect security anomalies within real-time processing time and update the other farm nodes to be aware of the situation. Full article
(This article belongs to the Special Issue Privacy and Cybersecurity in the Artificial Intelligence Age)
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