Add to Chrome
Add to Firefox
Add to EdgeDanda B. Rawat
Metaverse Survey & Tutorial: Exploring Key Requirements, Technologies, Standards, Applications, Challenges, and Perspectives
May 07, 2024
In this paper, we present a comprehensive survey of the metaverse, envisioned as a transformative dimension of next-generation Internet technologies. This study not only outlines the structural components of our survey but also makes a substantial scientific contribution by elucidating the foundational concepts underlying the emergence of the metaverse. We analyze its architecture by defining key characteristics and requirements, thereby illuminating the nascent reality set to revolutionize digital interactions. Our analysis emphasizes the importance of collaborative efforts in developing metaverse standards, thereby fostering a unified understanding among industry stakeholders, organizations, and regulatory bodies. We extend our scrutiny to critical technologies integral to the metaverse, including interactive experiences, communication technologies, ubiquitous computing, digital twins, artificial intelligence, and cybersecurity measures. For each technological domain, we rigorously assess current contributions, principal techniques, and representative use cases, providing a nuanced perspective on their potential impacts. Furthermore, we delve into the metaverse's diverse applications across education, healthcare, business, social interactions, industrial sectors, defense, and mission-critical operations, highlighting its extensive utility. Each application is thoroughly analyzed, demonstrating its value and addressing associated challenges. The survey concludes with an overview of persistent challenges and future directions, offering insights into essential considerations and strategies necessary to harness the full potential of the metaverse. Through this detailed investigation, our goal is to articulate the scientific contributions of this survey paper, transcending a mere structural overview to highlight the transformative implications of the metaverse.
Trustworthy Artificial Intelligence Framework for Proactive Detection and Risk Explanation of Cyber Attacks in Smart Grid
Jun 12, 2023
The rapid growth of distributed energy resources (DERs), such as renewable energy sources, generators, consumers, and prosumers in the smart grid infrastructure, poses significant cybersecurity and trust challenges to the grid controller. Consequently, it is crucial to identify adversarial tactics and measure the strength of the attacker's DER. To enable a trustworthy smart grid controller, this work investigates a trustworthy artificial intelligence (AI) mechanism for proactive identification and explanation of the cyber risk caused by the control/status message of DERs. Thus, proposing and developing a trustworthy AI framework to facilitate the deployment of any AI algorithms for detecting potential cyber threats and analyzing root causes based on Shapley value interpretation while dynamically quantifying the risk of an attack based on Ward's minimum variance formula. The experiment with a state-of-the-art dataset establishes the proposed framework as a trustworthy AI by fulfilling the capabilities of reliability, fairness, explainability, transparency, reproducibility, and accountability.
Intrusion Detection Systems Using Support Vector Machines on the KDDCUP'99 and NSL-KDD Datasets: A Comprehensive Survey
Sep 12, 2022With the growing rates of cyber-attacks and cyber espionage, the need for better and more powerful intrusion detection systems (IDS) is even more warranted nowadays. The basic task of an IDS is to act as the first line of defense, in detecting attacks on the internet. As intrusion tactics from intruders become more sophisticated and difficult to detect, researchers have started to apply novel Machine Learning (ML) techniques to effectively detect intruders and hence preserve internet users' information and overall trust in the entire internet network security. Over the last decade, there has been an explosion of research on intrusion detection techniques based on ML and Deep Learning (DL) architectures on various cyber security-based datasets such as the DARPA, KDDCUP'99, NSL-KDD, CAIDA, CTU-13, UNSW-NB15. In this research, we review contemporary literature and provide a comprehensive survey of different types of intrusion detection technique that applies Support Vector Machines (SVMs) algorithms as a classifier. We focus only on studies that have been evaluated on the two most widely used datasets in cybersecurity namely: the KDDCUP'99 and the NSL-KDD datasets. We provide a summary of each method, identifying the role of the SVMs classifier, and all other algorithms involved in the studies. Furthermore, we present a critical review of each method, in tabular form, highlighting the performance measures, strengths, and limitations of each of the methods surveyed.
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions
Aug 15, 2022
With the advent of the IoT, AI, and ML/DL algorithms, the data-driven medical application has emerged as a promising tool for designing reliable and scalable diagnostic and prognostic models from medical data. This has attracted a great deal of attention from academia to industry in recent years. This has undoubtedly improved the quality of healthcare delivery. However, these AI-based medical applications still have poor adoption due to their difficulties in satisfying strict security, privacy, and quality of service standards (such as low latency). Moreover, medical data are usually fragmented and private, making it challenging to generate robust results across populations. Recent developments in federated learning (FL) have made it possible to train complex machine-learned models in a distributed manner. Thus, FL has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and security concerns. To this end, this survey paper highlights the current and future of FL technology in medical applications where data sharing is a significant burden. It also review and discuss the current research trends and their outcomes for designing reliable and scalable FL models. We outline the general FL's statistical problems, device challenges, security, privacy concerns, and its potential in the medical domain. Moreover, our study is also focused on medical applications where we highlight the burden of global cancer and the efficient use of FL for the development of computer-aided diagnosis tools for addressing them. We hope that this review serves as a checkpoint that sets forth the existing state-of-the-art works in a thorough manner and offers open problems and future research directions for this field.
Gradient Ascent Algorithm for Enhancing Secrecy Rate in Wireless Communications for Smart Grid
Aug 11, 2021
The emerging Internet of Things (IoT) and bidirectional communications in smart grid are expected to improve smart grid capabilities and electricity management. Because of massive number of IoT devices in smart grid, size of the data to be transmitted increases, that demands a high data rate to meet the real-time smart grid communications requirements. Sub-6 GHz, millimeter-wave (mmWave) technologies, and massive multiple-input multiple-output (MIMO) technologies can meet high data rate demands. However, IoT enabled smart grid is still subject to various security challenges such as eavesdropping, where attackers attempt to overhear the transmitted signals and the jamming attack, where the attacker perturbs the received signals at the receiver. In this paper, our goal is to investigate jamming and eavesdropping attacks while improving secrecy capacity for smart grid communications. Specifically, we propose to employ a hybrid beamforming design for wireless communications in smart energy grid. In previous works, the secrecy capacity is increased by randomly augmenting the source power or setting the system combiners. Unlike state-of-the-art, we design and evaluate the Gradient Ascent algorithm to search for the best combiners/waveform that maximizes the secrecy capacity in smart grid communications. We also study two different optimization scenarios by considering both fixed and variable transmit power. Numerical results are used for performance evaluation and supporting our formal analysis.
Reinforcement Learning for IoT Security: A Comprehensive Survey
Feb 14, 2021
The number of connected smart devices has been increasing exponentially for different Internet-of-Things (IoT) applications. Security has been a long run challenge in the IoT systems which has many attack vectors, security flaws and vulnerabilities. Securing billions of B connected devices in IoT is a must task to realize the full potential of IoT applications. Recently, researchers have proposed many security solutions for IoT. Machine learning has been proposed as one of the emerging solutions for IoT security and Reinforcement learning is gaining more popularity for securing IoT systems. Reinforcement learning, unlike other machine learning techniques, can learn the environment by having minimum information about the parameters to be learned. It solves the optimization problem by interacting with the environment adapting the parameters on the fly. In this paper, we present an comprehensive survey of different types of cyber-attacks against different IoT systems and then we present reinforcement learning and deep reinforcement learning based security solutions to combat those different types of attacks in different IoT systems. Furthermore, we present the Reinforcement learning for securing CPS systems (i.e., IoT with feedback and control) such as smart grid and smart transportation system. The recent important attacks and countermeasures using reinforcement learning B in IoT are also summarized in the form of tables. With this paper, readers can have a more thorough understanding of IoT security attacks and countermeasures using Reinforcement Learning, as well as research trends in this area.
Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS
Feb 14, 2021
Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This paper is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this paper, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.
Securing V2I Communications in 5G and Beyond Wireless System Using Gradient Ascent Approach
Feb 05, 2021
The 5G and beyond wireless systems, instead of being just an extension of 4G, are regarded as `network of networks' which is expected to integrate heterogeneous wireless networks including wireless vehicular networks (WVN). The WVN promises to solve many issues such as reducing road accidents, traffic jams, fuel consumption and commute time, which is subject to various security issues such as eavesdropping, where attackers attempt to overhear the secret transmitted signal passively (which makes the detection/defense very difficult). In this work, our objective is to implement a security scheme by improving the secrecy capacity in Sub-6 GHz and millimeter-wave (mmWave) bands. Vehicle-to-Infrastructure (V2I) for 5G and beyond wireless network. Unlike previous works where the secrecy capacity is increased by randomly augmenting the source power or setting the system combiners/precoders, in this work we employ the Gradient Ascent algorithm to search for the best combiners/precoders that maximize the secrecy rate performance. We further present two different optimization scenarios: fixed and variable transmission power.
Temporal CSI Correlation in Mixed RF/FSO Cooperative Relaying Systems Under Joint Effects of HPA Nonlinearities and IQ Imbalance
Feb 03, 2021
In this paper, we present the performance analysis of mixed RF/FSO system with multiple relays. To select the best relay, we adopt partial relay selection with outdated CSI wherein we investigate the effect of the temporal correlation of the channels. Unlike the vast majority of work, we introduce the impairments to the relays and the destination and we compare the performance against conventional RF relaying systems. We further derive the expressions of the outage probability and the ergodic capacity as well as the bounds to unpack engineering insights into the system robustness.