Department of Electrical and Electronics Engineering
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Item Generative AI for Cyber Security: Analyzing the Potential of ChatGPT, DALL-E, and Other Models for Enhancing the Security Space(IEEE, 2024-04) Chamola, VinayThis research paper intends to provide real-life applications of Generative AI (GAI) in the cybersecurity domain. The frequency, sophistication and impact of cyber threats have continued to rise in today’s world. This ever-evolving threat landscape poses challenges for organizations and security professionals who continue looking for better solutions to tackle these threats. GAI technology provides an effective way for them to address these issues in an automated manner with increasing efficiency. It enables them to work on more critical security aspects which require human intervention, while GAI systems deal with general threat situations. Further, GAI systems can better detect novel malware and threatening situations than humans. This feature of GAI, when leveraged, can lead to higher robustness of the security system. Many tech giants like Google, Microsoft etc., are motivated by this idea and are incorporating elements of GAI in their cybersecurity systems to make them more efficient in dealing with ever-evolving threats. Many cybersecurity tools like Google Cloud Security AI Workbench, Microsoft Security Copilot, SentinelOne Purple AI etc., have come into the picture, which leverage GAI to develop more straightforward and robust ways to deal with emerging cybersecurity perils. With the advent of GAI in the cybersecurity domain, one also needs to take into account the limitations and drawbacks that such systems have. This paper also provides some of the limitations of GAI, like periodically giving wrong results, costly training, the potential of GAI being used by malicious actors for illicit activities etc.Item Hardware Testbed based Analytical Performance Modelling for Mobile Task Offloading in UAV Edge Cloudlets(IEEE, 2021) Chamola, VinayIn recent times, there is a paradigm shift to cloud services that offer on-demand computer system resources, especially data storage and computing power. The main reason for the shift is that it removes the user's active participation to perform computationally intensive tasks. However, current cloud-based services incur high user latency as being deployed very far from the user. One alternative solution to the traditional cloud-based paradigm is drone-based edge computing. In drone edge computing, drones are located near the user and deployed to provide data offload services. There have been many works that have addressed the issue of efficient task assignment in edge devices. This paper presents a concrete analytical performance model for drone cloudlet networks and factors that influence the service response time to the user. The results can be helpful for network administrators to make the current edge computing paradigm faster, more robust and, cost-effective.Item MbRE IDS: An AI and Edge Computing Empowered Framework for Securing Intelligent Transportation Systems(IEEE, 2022-05) Chamola, VinayRecent years have seen a widespread growth of research in the Internet of Things (IoT). While mobility networks such as the Intelligent Transportation Systems (ITS) are being increasingly studied for their application in smart cities, there are numerous cyber threats that may disrupt the security and safety of the users of such networks. This study proposes an intelligent, statistical Intrusion Detection System (IDS) called Multi-branch Reconstruction Error (MbRE) for the long term security of ITS against known and unknown threats. The proposed IDS learns only from normal behavior, detects deviation of vehicular from it, and classifies it into eight generalized buckets based on the aspects of the data found to be malicious, i.e. frequency, identity and motion (speed and position). The results obtained show the success of the proposed IDS in detecting different threats with recall and accuracy scores between 97.5% to 100% without the need to train on them.Item CellularBlockB5G: A Blockchain-based Multi Operator Spectrum Sharing Simulator for 5G and Beyond Networks(IEEE, 2023) Chamola, VinayThe advancement of Fifth-generation networks has enabled service-specific resource provisioning through Network slicing. Moving forward, Beyond 5G (B5G) is the key enabling factor for the next generation of computing networks catering to the needs of seamless connectivity with ultra-reliable performance and security. But the deployment of such systems to provide various services through dynamic network slicing needs network densification, leading to increased operational cost. This requirement has bid to enable infrastructure sharing between multiple operators and HetNets through Blockchain as a promising solution with secure and distributed ledger-based operations. This work presents a comprehensive simulation environment providing blockchain integration with B5G networks. In particular, this work identifies key challenges to creating such a simulation environment and handles several operational details, including spectrum sharing, network slicing and dealing with orphan blocks. In the end, we have presented the evaluation of the simulator on 5G blockchain-based spectrum sharing. Furthermore, this work can facilitate further research on blockchain in B5G networks and help in providing a common framework for operators in analyzing such operations on a large scale.Item Ambient Intelligence for Securing Intelligent Vehicular Networks: Edge-Enabled Intrusion and Anomaly Detection Strategies(IEEE, 2023-03) Alladi, Tejasvi; Chamola, VinayThe Internet of Things (IoT) is increasingly being deployed in smart city applications such as vehicular networks. The presence of a large number of communicating vehicles greatly increases the number and types of possible anomalies in the network. These anomalies could range from faulty vehicular data being broadcast by the vehicles to more catastrophic attacks such as disruptive attacks and Denial of Service (DoS) attacks to name a few. This calls for a need to develop robust security schemes such as intrusion detection and anomaly detection schemes. With a humongous growth in the amount of vehicular traffic data expected, artificial intelligence (AI)-based detection strategies need to be developed to address this burgeoning demand. In this article, we propose three AI-based intrusion detection strategies for vehicular network applications, leading to an effective Ambient Intelligence based vehicular network paradigm. The detection tasks are run on local edge servers deployed at the network edge. By showing the prediction results on an experimental testbed emulating the edge servers, we show the feasibility of deploying the proposed strategies in the vehicular network scenario.Item Multibranch Reconstruction Error (MbRE) Intrusion Detection Architecture for Intelligent Edge-Based Policing in Vehicular Ad-Hoc Networks(IEEE, 2022-09) Chamola, VinayThere has been a notable increase in the research and development of Vehicular Ad-hoc Networks (VANETs) to efficiently and safely manage large amounts of traffic. Such networks are, however, also prone to various cyber threats to data integrity, privacy, authentication, and network availability, and given the potential risk to life under the event of a malfunction and misinformation, it is important to provide security measures against such threats. This paper presents the Multi-branch Reconstruction Error (MbRE) Intrusion Detection System (IDS) for edge-based anomaly detection in VANETs for data integrity, network availability and user authentication-based misbehaviors without the need to train on them. Vehicular data is first sequenced and separated into three data branches -frequency (F) derived from the message timestamps, pseudo-identities (I), and the motion data (M) i.e. position and velocity. The proposed model comprises of three Convolutional Neural Networks (CNN)-based reconstruction models trained to reconstruct normal F-I-M vehicular behavior. The IDS classifies each branch of a sequence as 0/1 based on the reconstruction error threshold for the respective branch and, therefore, has the ability to detect 8 possible binary encoded behaviors for each sequence of vehicular data. These results are then used to find the overall behavior of each vehicle using carefully selected detection thresholds. MbRE is able to classify frequency, identity and motion-based behavior samples with an accuracy of 100%, 98.5-100%, and 95.4-100%, respectively, without the need to train on such behaviors. The study also emulates the IDS on Google Colaboratory and Jetson Nano to show its practicality in cloud and edge environments.Item UAV SECaaS: Game-Theoretic Formulation for Security as a Service in UAV Swarms(IEEE, 2022-12) Chamola, VinayUnmanned aerial vehicles, popularly known as UAVs, have been used in many applications in the recent past. UAVs have also been recently used to provide security as a service (SECaaS). SECaaS involves technical solutions, like antivirus and antispam software, firewalls, using secure operating systems, etc. UAVs are resource-constrained entities, and thus, they avail the computational facilities of the base station (BS) to serve the users in their range. At times, several UAVs cooperatively come together to serve a given region, and such a group of UAVs is called a swarm of UAVs. Generally, a group/swarm of UAVs connect themselves to the BS through cluster head (CH) UAVs, which are intermediary nodes. In real-world scenarios, many stakeholders come together to form a UAV swarm configuration providing services to users. Each stakeholder wants to maximize gains. This work proposes a pricing Stackelberg game among the UAVs, CHs, and the BS by formulating their behavioral utilities. Using particle swarm optimization on each entity’s utility functions, we create an optimal price strategy to maximize profit.Item Uniting cyber security and machine learning: Advantages, challenges and future research(Elsevier, 2022-09) Chamola, VinayMachine learning (ML) is a subset of Artificial Intelligence (AI), which focuses on the implementation of some systems that can learn from the historical data, identify patterns and make logical decisions with little to no human interventions. Cyber security is the practice of protecting digital systems, such as computers, servers, mobile devices, networks and associated data from malicious attacks. Uniting cyber security and ML has two major aspects, namely accounting for cyber security where the machine learning is applied, and the use of machine learning for enabling cyber security. This uniting can help us in various ways, like it provides enhanced security to the machine learning models, improves the performance of the cyber security methods, and supports effective detection of zero day attacks with less human intervention. In this survey paper, we discuss about two different concepts by uniting cyber security and ML. We also discuss the advantages, issues and challenges of uniting cyber security and ML. Furthermore, we discuss the various attacks and provide a comprehensive comparative study of various techniques in two different considered categories. Finally, we provide some future research directions.Item Secure Lending: Blockchain and Prospect Theory-Based Decentralized Credit Scoring Model(IEEE, 2020-10) Chamola, VinayCredit scoring is a rigorous statistical analysis carried out by lenders and other third parties to access an individual's creditworthiness. Lenders use credit scoring to estimate the degree of risk in lending money to an individual. However, credit score evaluation is primarily based on a transaction record, payment history, professional background, etc. sourced from different credit bureaus. So, evaluating a credit score is a laborious and tedious task involving a lot of paperwork. In this paper, we propose how blockchain can provide the solution to decentralized credit scoring evaluation and reducing the amount of dependence of paperwork. Lending money is not always objective but subjective to every lender. The decision of lending involves different levels of risk and uncertainty, depending on their perspective. This paper uses the prospect theory to model the optimal investment strategy for different risk vs. return scenarios.Item Machine-Learning-Assisted Security and Privacy Provisioning for Edge Computing: A Survey(IEEE, 2021-07) Chamola, VinayEdge computing (EC), is a technological game changer that has the ability to connect millions of sensors and provide services at the device end. The broad vision of EC integrates storage, processing, monitoring, and control of operations in the Edge of the network. Though EC provides end-to-end connectivity, speeds up operation, and reduces latency of data transfer, security is a major concern. The tremendous growth in the number of Edge Devices and the amount of sensitive information generated at the device and the cloud creates a broad surface of attack and therefore, the need to secure the static and mobile data is imperative. This article is a comprehensive survey that describes the security and privacy issues in various layers of the EC architecture that result from the networking of heterogeneous devices. Second, it discusses the wide range of machine learning and deep learning algorithms that are applied in EC use cases. Following this, this article broadly details the different types of attacks that the Edge network confronts, and the intrusion detection systems and the corresponding machine learning algorithms that overcome these security and privacy concerns. The details of machine learning and deep learning techniques for EC security are tabulated. Finally, the open issues in securing Edge networks and future research directions are provided.