Syed Rizvi

Deepfake technology has yielded both benefits and harms; however, the negatives raise serious concerns. This article explores deepfake impacts, detection methods, and the need for improved policies to safeguard digital integrity.

The widespread use of Internet of Things (IoT) devices offers life-changing advantages, but this convenience comes at a steep cost. Since IoT networks lack a universal privacy standard, users are susceptible to unprecedented risks.

The rapid adoption of cloud-integrated Internet of Things devices has greatly outpaced the development of adequate security practices. In critical domains, this can lead to life-threatening consequences.

The Internet of Things (IoT), as a technology, transforms every day, consumer devices into devices capable of collecting and transmitting data. This momentous progress highlights the need for reliable risk management frameworks that address the potential risks associated with IoT devices across all aspects of life. Traditionally, the feasibility of IoT was limited by the high costs of sensors and their connectivity. Although, recent advancements have greatly reduced costs, enabling widespread connectivity of devices to the Internet. Consequently, numerous IoT devices and networks are left vulnerable without a comprehensive framework for managing these risks effectively. This paper introduces a more systematic framework designed to counter security risks and safeguard IoT devices. This framework takes a step-by-step approach for focusing on mitigating risks arising from inadequate security designs for IoT devices. It involves procedures for risk identification, evaluation, and prioritization which is followed by comprehensive risk analysis and control measures, and because risk evaluation is tedious, we suggest using machine learning (ML) to accelerate the risk evaluation process, boost the precision and consistency of risk assessments, and provide valuable insight, which ultimately enables risk analysts to make better informed and timely decisions. Through multiple case studies, we demonstrate the practicality and efficacy of the framework for evaluating IoT device risks and guiding the implementation of appropriate controls to safeguard devices and networks. We also develop security dashboards that provide visual summaries comparing device values, threat assessments, and risk mitigation costs, aiding in clear communication and prioritization of security measures.

With the development of information society and network technology, people increasingly depend on information found on the Internet. At the same time, the models of information diffusion on the Internet are changing as well. However, these models experience the problem due to the fast development of network technologies. There is no thorough research in regards to the latest models and their applications and advantages. As a result, it is essential to have a comprehensive study of information diffusion models.

Purchasing items on the Internet with a credit card can be extremely risky. To mitigate this risk, we propose a new Multi-Layer Defense (MLD) scheme which uses two-factor authentication by instituting a random number generator that is only valid for a preset amount of time. For two-factor authentication, we use an eight-digit code that serves as a redundant private key to authenticate the purchases. This can be utilized to identify users of credit cards and establish a more secure way of purchasing items via various marketplaces on the Internet. Specifically, the MLD scheme uses electronic devices to log into the credit card accounts via an application to view a randomly generated code. This is then inputted on an Online Retailer’s website to authorize the use of the credit card. This ensures that a legitimate user is attempting to make a purchase and is not fraudulent. This redundancy, if used, can mitigate the circumstance of lost or stolen credit card information. The use of our proposed MLD scheme, specifically randomly generated code, when making a credit card purchase online could drastically cut down on fraudulent credit card purchases.

Cloud computing is a model for on-demand delivery of IT resources (e.g., servers, storage, databases, etc.) over the Internet with pay-as-you-go pricing. Although it provides numerous benefits to cloud service users (CSUs) such as flexibility, elasticity, scalability, and economies of scale, there is a large trust deficit between CSUs and cloud service providers (CSPs) that prevents the widespread adoption of this computing paradigm. While some businesses have slowly started adopting cloud computing with careful considerations, others are still reluctant to migrate toward it due to several data security and privacy issues. Therefore, the creation of a trust model that can evolve to reflect the true assessment of CSPs in terms of either a positive or a negative reputation as well as quantify trust level is of utmost importance to establish trust between CSUs and CSPs. In this paper, we propose a fuzzy-logic based approach that allows the CSUs to determine the most trustworthy CSPs. Specifically, we develop inference rules that will be applied in the fuzzy inference system (FIS) to provide a quantitative security index to the CSUs. One of the main advantages of the FIS is that it considers the uncertainties and ambiguities associated with measuring trust. Moreover, our proposed fuzzy-logic based trust model is not limited to the CSUs as it can be used by the CSPs to promote their services through self-evaluation. To demonstrate the effectiveness of our proposed fuzzy-based trust model, we present case studies where several CSPs are evaluated and ranked based on the security index.