ORCID Profile
0000-0002-8784-0154
Current Organisation
Deakin University
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Publisher: Elsevier BV
Date: 12-2021
Publisher: MDPI AG
Date: 14-10-2021
DOI: 10.3390/S21206832
Abstract: Internet of Things (IoT) applications and services are becoming more prevalent in our everyday life. However, such an interconnected network of intelligent physical entities needs appropriate security to sensitive information. That said, the need for proper authentication and authorization is paramount. Access control is in the front line of such mechanisms. Access control determines the use of resources only to the specified and authorized users based on appropriate policy enforcement. IoT demands more sophisticated access control in terms of its usability and efficiency in protecting sensitive information. This conveys the need for access control to serve system-specific requirements and be flexibly combined with other access control approaches. In this paper, we discuss the potential for employing protocol-based and hybrid access control for IoT systems and examine how that can overcome the limitations of traditional access control mechanisms. We also focus on the key benefits and constraints of this integration. Our work further enhances the need to build hierarchical access control for large-scale IoT systems (e.g., Industrial IoT (IIoT) settings) with protocol-based and hybrid access control approaches. We, moreover, list the associated open issues to make such approaches efficient for access control in large-scale IoT systems.
Publisher: MDPI AG
Date: 19-10-2020
DOI: 10.3390/S20205897
Abstract: There has been a tremendous growth in the number of smart devices and their applications (e.g., smart sensors, wearable devices, smart phones, smart cars, etc.) in use in our everyday lives. This is accompanied by a new form of interconnection between the physical and digital worlds, commonly known as the Internet of Things (IoT). This is a paradigm shift, where anything and everything can be interconnected via a communication medium. In such systems, security is a prime concern and protecting the resources (e.g., applications and services) from unauthorized access needs appropriately designed security and privacy solutions. Building secure systems for the IoT can only be achieved through a thorough understanding of the particular needs of such systems. The state of the art is lacking a systematic analysis of the security requirements for the IoT. Motivated by this, in this paper, we present a systematic approach to understand the security requirements for the IoT, which will help designing secure IoT systems for the future. In developing these requirements, we provide different scenarios and outline potential threats and attacks within the IoT. Based on the characteristics of the IoT, we group the possible threats and attacks into five areas, namely communications, device/services, users, mobility and integration of resources. We then examine the existing security requirements for IoT presented in the literature and detail our approach for security requirements for the IoT. We argue that by adhering to the proposed requirements, an IoT system can be designed securely by achieving much of the promised benefits of scalability, usability, connectivity, and flexibility in a practical and comprehensive manner.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: MDPI AG
Date: 18-08-2021
DOI: 10.3390/S21165554
Abstract: With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.
Publisher: Springer International Publishing
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-12-2022
Publisher: ACM
Date: 10-07-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: ACM
Date: 10-07-2023
Publisher: Springer International Publishing
Date: 2022
Publisher: ACM
Date: 10-07-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Publisher: Elsevier BV
Date: 07-2022
Publisher: MDPI AG
Date: 02-2023
DOI: 10.3390/S23031561
Abstract: Industrial Control Systems (ICSs) were initially designed to be operated in an isolated network. However, recently, ICSs have been increasingly connected to the Internet to expand their capability, such as remote management. This interconnectivity of ICSs exposes them to cyber-attacks. At the same time, cyber-attacks in ICS networks are different compared to traditional Information Technology (IT) networks. Cyber attacks on ICSs usually involve a sequence of actions and a multitude of devices. However, current anomaly detection systems only focus on local analysis, which misses the correlation between devices and the progress of attacks over time. As a consequence, they lack an effective way to detect attacks at an entire network scale and predict possible future actions of an attack, which is of significant interest to security analysts to identify the weaknesses of their network and prevent similar attacks in the future. To address these two key issues, this paper presents a system-wide anomaly detection solution using recurrent neural networks combined with correlation analysis techniques. The proposed solution has a two-layer analysis. The first layer targets attack detection, and the second layer analyses the detected attack to predict the next possible attack actions. The main contribution of this paper is the proof of the concept implementation using two real-world ICS datasets, SWaT and Power System Attack. Moreover, we show that the proposed solution effectively detects anomalies and attacks on the scale of the entire ICS network.
Publisher: MDPI AG
Date: 13-01-2022
DOI: 10.3390/S22020608
Abstract: Nowadays, there is tremendous growth in the Internet of Things (IoT) applications in our everyday lives. The proliferation of smart devices, sensors technology, and the Internet makes it possible to communicate between the digital and physical world seamlessly for distributed data collection, communication, and processing of several applications dynamically. However, it is a challenging task to monitor and track objects in real-time due to the distinct characteristics of the IoT system, e.g., scalability, mobility, and resource-limited nature of the devices. In this paper, we address the significant issue of IoT object tracking in real time. We propose a system called ‘TrackInk’ to demonstrate our idea. TrackInk will be capable of pointing toward and taking pictures of visible satellites in the night sky, including but not limited to the International Space Station (ISS) or the moon. Data will be collected from sensors to determine the system’s geographical location along with its 3D orientation, allowing for the system to be moved. Additionally, TrackInk will communicate with and send data to ThingSpeak for further cloud-based systems and data analysis. Our proposed system is lightweight, highly scalable, and performs efficiently in a resource-limited environment. We discuss a detailed system’s architecture and show the performance results using a real-world hardware-based experimental setup.
Publisher: Association for Computing Machinery (ACM)
Date: 28-09-2023
DOI: 10.1145/3597306
Abstract: The devices that can read Electroencephalography (EEG) signals have been widely used for Brain-Computer Interfaces (BCIs). Popularity in the field of BCIs has increased in recent years with the development of several consumer-grade EEG devices that can detect human cognitive states in real-time and deliver feedback to enhance human performance. Several previous studies have been conducted to understand the fundamentals and essential aspects of EEG in BCIs. However, the significant issue of how consumer-grade EEG devices can be used to control mechatronic systems effectively has been given less attention. In this paper, we have designed and implemented an EEG BCI system using the OpenBCI Cyton headset and a user interface running a game to explore the concept of streamlining the interaction between humans and mechatronic systems with a BCI EEG-mechatronic system interface. Big Multimodal Social Data (BMSD) analytics can be applied to the high-frequency and high-volume EEG data, allowing us to explore aspects of data acquisition, data processing, and data validation and evaluate the Quality of Experience (QoE) of our system. We employ real-world participants to play a game to gather training data that was later put into multiple machine learning models, including a linear discriminant analysis (LDA), k-nearest neighbours (KNN), and a convolutional neural network (CNN). After training the machine learning models, a validation phase of the experiment took place where participants tried to play the same game but without direct control, utilising the outputs of the machine learning models to determine how the game moved. We find that a CNN trained to the specific user was able to control the game and performed with the highest activation accuracy from the machine learning models tested, along with the highest user rated QoE, which gives us significant insight for future implementation with a mechatronic system.
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: MDPI AG
Date: 10-10-2021
DOI: 10.3390/APP11209393
Abstract: Industrial Internet of Things (IIoT) can be seen as an extension of the Internet of Things (IoT) services and applications to industry with the inclusion of Industry 4.0 that provides automation, reliability, and control in production and manufacturing. IIoT has tremendous potential to accelerate industry automation in many areas, including transportation, manufacturing, automobile, marketing, to name a few places. When the benefits of IIoT are visible, the development of large-scale IIoT systems faces various security challenges resulting in many large-scale cyber-attacks, including fraudulent transactions or damage to critical infrastructure. Moreover, a large number of connected devices over the Internet and resource limitations of the devices (e.g., battery, memory, and processing capability) further pose challenges to the system. The IIoT inherits the insecurities of the traditional communication and networking technologies however, the IIoT requires further effort to customize the available security solutions with more focus on critical industrial control systems. Several proposals discuss the issue of security, privacy, and trust in IIoT systems, but comprehensive literature considering the several aspects (e.g., users, devices, applications, cascading services, or the emergence of resources) of an IIoT system is missing in the present state of the art IIoT research. In other words, the need for considering a vision for securing an IIoT system with broader security analysis and its potential countermeasures is missing in recent times. To address this issue, in this paper, we provide a comparative analysis of the available security issues present in an IIoT system. We identify a list of security issues comprising logical, technological, and architectural points of view and consider the different IIoT security requirements. We also discuss the available IIoT architectures to examine these security concerns in a systematic way. We show how the functioning of different layers of an IIoT architecture is affected by various security issues and report a list of potential countermeasures against them. This study also presents a list of future research directions towards the development of a large-scale, secure, and trustworthy IIoT system. The study helps understand the various security issues by indicating various threats and attacks present in an IIoT system.
No related grants have been discovered for Shantanu Pal.