Susheela Dahiya

Background: In the field of digital forensics, the proliferation of Internet of Things (IoT) devices within intelligent residences has presented both new opportunities and challenges. Every gadget, including smart thermostats, security cameras, lighting controls, and even washing machines or refrigerators is equipped by these installers in the range of gadgets offered by manufacturers. Methods: This research conducts a comprehensive investigation and classification of contemporary forensic tools designed for IoT devices in smart homes, highlighting their characteristics, methods of data collection, types of target devices, analytical methodologies, practical applications, and capabilities for integration. This generally involves a comparison of different forensic products or solutions and evaluation on various criteria such as cost, supportability, maintainable architecture-designs (integration), speedy acquisition speed/performance effectiveness without compromising quality, ease-of-use, and consistency. Results: A Comparative Analysis with Detailed Tables & Radar Charts Identifying the detailed pros and cons of each tool, our findings help forensic professionals understand when to use them for effective decisions. The results show that XRY and UFED by Cellebrite both scored 5/5 in each criterion, showing the best performance in mobile device forensics. Wireshark and tcpdump also have high rates for the accuracy and reliability criteria, with results of 5/5, and are therefore also highly recommended in the area of analysis of network traffic. Magnet AXIOM and NetworkMiner graded evenly well, with a usability rating of four out of five and an integration mark of 4 out of five, which diversified them for computer and mobile forensics. Splunk and ELK Stack scored topping the scalability category, with each scoring out of five, which further confirmed the analysis of logs well for large data sets. These numerical results further underline that the choice of the tool depends on specific forensic requirements. Conclusion: The authors examine future IoT forensics in smart homes which highlights the necessity of devices working with each other through a standard and sophisticated analysis to deal with dynamic complexity development within this field.

Traditional logic gate simulators mainly offer a graphical design environment while they usually do not offer real time interfacing to hardware devices and are still restricted for their use in educational applications. To solve all these limitations, we introduce PLUX, a single unified framework of automatic logic gate simulation that combines on-line Internet of Things (IoT) hardware communication, Artificial Intelligence (AI) based on circuit optimization, and code generation to digital designs. PLUX is a class of hardware-in-the-loop debugging system that permits simulation engineers to easily integrate hardware with software, students/researchers to simulate advanced circuits, hardware grippers/developers to validate circuits in context and verify performance in the real world. The framework operates with microcontroller platforms such as Arduino, Raspberry Pi, ESP32, and Micro Python, and can provide live feedback, fault detection, adaptive optimization, and is able to string of generic and self-written certain program branches. Bekritz's flexible and scalable architecture allows for long-term adaptability to emerging technologies, while AI algorithms embedded in the design minimize design inefficiencies as well as debugging time. The framework was evaluated on publicly available benchmark datasets as ISCAS-85 and ISCAS-89 benchmarking circuits, MCNC Benchmark suite, EPFL combinational benchmark suite and open source IoT traffic datasets. Experimental results show the framework achieves up to 35% faster simulation speed and 20% less CPU utilization compared to the normal simulators with a Transmission Reliability Index (TRI) of 0.96, for USB communication and other communication means like Bluetooth and Wi-Fi communication.

The advent of advanced computational methodologies has made for a galaxy of operations for users which has precipitated an exponential growth of malicious works like spoofing, poisoning, unsolicited actions, data exfiltration, etc. The present manuscript provides an understanding of Emergent Cyberattacks namely Cross-Site Scripting (XSS) and Cross-Site Request Forgery (CSRF). These attack vectors are constantly evolving and adapting in sync with new technologies such as agentic- based Artificial Intelligence (AI) browsers and adaptive learning browsers. An in-depth review of these cyberattacks is covered in the rest of the paper. The research shows the design and implementation of a proposed framework, which is a multi-layered architecture with Machine Learning (ML) techniques which is designed to analyses, detect, and provide countermeasures to the aforementioned cyberattacks. The framework includes hybrid supervised Deep Learning (DL) models ranging from Temporal Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), Variational Autoencoders, and pretrained linguistic models. When combined these models make up a novel methodology for evaluating attack vectors and provide early warning capabilities. The architecture was tested on publicly available datasets such as CSIC 2010, Kaggle XSS datasets, fawaz2015, Malicious and Benign Webpages, etc., which gave an accurate value of 83.21 percent. The model constituents are trained to appraise vulnerability sequence post-preprocessing in order to mitigate noise residuals. Additional essential parts make the framework stand out in its significance and efficacy in real-world deployments. As a result, the framework offers a smart, proactive, dependable, and scalable augmentation to the cybersecurity infrastructure.

The development of digital applications has provided ease in human tasks and diverse capabilities. Industrialists and certain organizations have provided Application Programming Interfaces (abbreviated as APIs). This terminology helps developers in adaptive learning and the development of their tools and applications. APIs are generally a package given by various organizations to authorize private or public users to access specific services; they behave like a two-way medium where the user sends a request and the receiver accepts the request, provides the service, and manages the number of requests. This paper demonstrates the working of an API to understand the proposed framework. Today, digital advancement provides various tools to speed up tasks. Looking at this issue, the proposed framework provides a novel inventive application which allows publicly open-access application Uniform Resource Locators (abbreviated as URL) to be converted into a Cryptographic-enabled API key that works normally like a general public API. The detailed methodology is discussed throughout the paper; many applications provide their API keys that work perfectly and without any obstruction, but during model training, they fetch unrelated data that affects the false positive rate. To address this emerging limitation, our proposed framework allows the user to input a specific URL, which is converted into an API key equipped with a cryptography mechanism to limit data tampering and injection, or other related miscellaneous activities. The proposed framework shows that there has been good advancement in mechanically computing and also offers an adaptive and scalable answer to present-day developer infrastructure.