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Intelligent Sensing Techniques for Detection of Attacks against Public Infrastructure
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Intelligent Sensing Techniques for Detection of Attacks against Public Infrastructure

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 3128

Special Issue Editor

Dr. Ankur Chowdhary

E-Mail Website
Guest Editor
Information Assurance Center, Arizona State University, 699 S Mill Ave, Tempe, AZ 85281, USA
Interests: cloud security; software defined networks; application of artificial intelligence and machine learning in the field of cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed a surge in advanced cyberattacks targeting public infrastructure such as Internet of Things (IoT) networks. These attacks involve the use of adaptive phishing toolkits, adaptive malware, and ransomware. While previous research has explored this topic, the practical implementation of AI and ML solutions to address advanced cyber-attacks targeting public infrastructure remains limited. This Special Issue delves into the utilization of artificial intelligence and machine learning techniques specifically tailored to detect and mitigate advanced cyber-attacks.

The scope of the Special Issue includes:

  • AI/ML algorithms that improve detection against advanced cyber-attacks.
  • Practical learnings in large-scale applications of AI and ML against advanced cyber-attacks.
  • Use of AI/ML for detection of cyber-attacks on Internet of Things (IoT) networks.
  • Practical considerations for detection of attacks at scale.
  • Challenges in the detection of attacks against IoT networks.
  • Cost–benefit analysis of AI/ML techniques used.

Dr. Ankur Chowdhary
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. Sensors 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

  • artificial intelligence
  • machine learning
  • Internet of Things (IoT)

Published Papers (2 papers)

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Research

24 pages, 3307 KiB  
Article
LazyFrog: Advancing Security and Efficiency in Commercial Wireless Charging with Adaptive Frequency Hopping
by Sungkyu Ahn, Hyelim Jung and Ki-Woong Park
Sensors 2024, 24(8), 2571; https://doi.org/10.3390/s24082571 - 17 Apr 2024
Viewed by 357
Abstract
With the proliferation of electronic devices and electricity-based mobility solutions, the significance of wireless power transfer technology has increased substantially. However, ensuring secure and reliable power transmission to authorized users remains a significant challenge. Addressing this complex issue requires an integrated approach that [...] Read more.
With the proliferation of electronic devices and electricity-based mobility solutions, the significance of wireless power transfer technology has increased substantially. However, ensuring secure and reliable power transmission to authorized users remains a significant challenge. Addressing this complex issue requires an integrated approach that balances efficiency, stability, and security considerations. While current efforts primarily focus on improving charging efficiency and user convenience, integrating robust security measures into wireless charging infrastructure is challenging due to its inherently open nature and susceptibility to external interference. Technical advancements are required to strengthen the security of the wireless charging infrastructure; however, these should be balanced with power loss management. This study tackles two core issues: the increasing hardware requirements for billing system authentication protocols and the interception of wireless charging signals by unauthorized users, leading to power theft and subsequent losses. To address these challenges, we propose a mechanism termed “LazyFrog”. This mechanism dynamically adjusts the frequency hopping schedule, activating frequency changes only in response to detected threats during remote charging or upon identifying unauthorized access attempts. The proposed mechanism compares the expected power reception at the device with the actual power supplied by the charging station, enabling the detection of abnormal power losses. By minimizing unnecessary frequency changes and optimizing energy consumption, LazyFrog reduces hardware requirements. Moreover, we have implemented a relative distance estimation mechanism to facilitate efficient power transfer as wireless devices move within the charging environment. With these features, LazyFrog demonstrates a secure, flexible, and energy-efficient wireless charging system ready for practical application. Full article
(This article belongs to the Special Issue Intelligent Sensing Techniques for Detection of Attacks against Public Infrastructure)
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18 pages, 546 KiB  
Article
Generative Adversarial Network (GAN)-Based Autonomous Penetration Testing for Web Applications
by Ankur Chowdhary, Kritshekhar Jha and Ming Zhao
Sensors 2023, 23(18), 8014; https://doi.org/10.3390/s23188014 - 21 Sep 2023
Cited by 1 | Viewed by 2357
Abstract
The web application market has shown rapid growth in recent years. The expansion of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has created new web-based communication and sensing frameworks. Current security research utilizes source code analysis and manual exploitation of [...] Read more.
The web application market has shown rapid growth in recent years. The expansion of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has created new web-based communication and sensing frameworks. Current security research utilizes source code analysis and manual exploitation of web applications, to identify security vulnerabilities, such as Cross-Site Scripting (XSS) and SQL Injection, in these emerging fields. The attack samples generated as part of web application penetration testing on sensor networks can be easily blocked, using Web Application Firewalls (WAFs). In this research work, we propose an autonomous penetration testing framework that utilizes Generative Adversarial Networks (GANs). We overcome the limitations of vanilla GANs by using conditional sequence generation. This technique helps in identifying key features for XSS attacks. We trained a generative model based on attack labels and attack features. The attack features were identified using semantic tokenization, and the attack payloads were generated using conditional sequence GAN. The generated attack samples can be used to target web applications protected by WAFs in an automated manner. This model scales well on a large-scale web application platform, and it saves the significant effort invested in manual penetration testing. Full article
(This article belongs to the Special Issue Intelligent Sensing Techniques for Detection of Attacks against Public Infrastructure)
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