This research paper introduces and discusses deeply an approach to the real-time IP reputation (IPR) concept and its validation process for an Amazon Web Services Web Application Firewall (AWS WAF) backend application safeguarding using intelligence (AI) technologies. Also, the study examines existing IP reputation solutions over AWS WAF which Evaluates methodologies highlighting the difficulties faced and real-world challenges in validating IPR while utilizing OpenAI’s generative AI language models the framework aims to automate the extraction and interpretation of IP-related information from AWS S3 real-time log storage sources such as logs, and natural language reports based on JSON structure. These dedicated algorithms developed, and AI model concepts are powered by processing language enabling them to identify incidents and detect patterns of IP behavior that should indicate security risks. Also, models do not directly access databases, as they can analyze data from APIs featured and with local maintenance database such that AbuseIPDB to evaluate the reputation of IP addresses Integrating AI into the process of validating IPs can greatly improve cybersecurity operations by summarizing findings and providing insights ultimately saving time and resources.

This research presents a pioneering investigation into the tangible outcomes of implementing an Artificial Intelligence (AI) driven network security strategy, with a specific emphasis on dynamic threat landscape adaptation and the integration of time series analysis algorithms. The study focuses on the innovative fusion of adaptive mechanisms to address the ever-evolving threat landscape, coupled with the application of the Autoregressive Integrated Moving Average (ARIMA) time series analysis algorithm. Real-world case studies are employed to provide concrete evidence of the efficacy of these strategies in fortifying network defenses and responding dynamically to cyber threats. Novelty is introduced through the unified integration of dynamic threat landscape adaptation mechanisms that continuously learn and evolve. The paper details adaptive access controls, showcasing how the security system dynamically adjusts permissions in real time to respond to emerging threats. Additionally, the application of the ARIMA time series analysis algorithm represents a pioneering contribution to the field of cybersecurity. By unveiling temporal patterns in security incidents, ARIMA adds a predictive element to network defense strategies, offering valuable insights into potential future threats and enabling a proactive response. The findings underscore the practical impact of the applied strategies, with real-world case studies demonstrating substantial improvements in threat detection rates, the effectiveness of adaptive responses, and the predictive capabilities facilitated by ARIMA. This research contributes to the advancement of AI in network security by providing tangible evidence of the innovative and effective nature of the integrated approach. The outcomes bridge the gap between theoretical concepts and practical applications, offering valuable insights for organizations seeking adaptive and predictive strategies to enhance their cybersecurity resilience in dynamic threat environments.

The Internet of Things (IoT) driven Industrial Revolution 4.0 (IR4.0) and this is impacting every sector of the global economy. With IoT devices, everything is computerized. Today's digital forensics is no longer limited to computers, mobiles, or networks. The current digital forensics landscape demands a significantly different approach. The traditional digital forensics frameworks no longer meet the current requirements. Therefore, in this paper, we propose a novel framework called “Multi-level Artifact of Interest Digital Forensics Framework for IoT” (MAoIDFF-IoT). The keynote "Multi-level" aims to cover all levels of the IoT architecture. Our novel IoT digital forensics framework focuses on the Artifact of Interest (AoI). Additionally, it proposes the action/detection matrix. It encompasses the advantages of the previous frameworks while introducing new features specifically designed to make the framework suitable for current and future IoT investigation scenarios. The MAoIDFF-IoT framework is designed to face the challenges of IoT forensic analysis and address the diverse architecture of IoT environments. Our proposed framework was evaluated through real scenario experiments. The evaluation of the experimental results reveals the superiority of our framework over existing frameworks in terms of usability, inclusivity, focus on the (AoI), and acceleration of the investigation process.

In the dynamic realm of Smart Healthcare Systems (SHS), the integration of IoT devices has revolutionized conventional practices, ushering in an era of real-time data collection and seamless communication across the healthcare ecosystem. Amidst this technological shift, the paramount concern remains the security of sensitive healthcare data within intricate networks. Several cryptographic algorithms have been proposed for smart healthcare systems for the protection of critical and sensitive data in SHS, however, the majority of newly proposed algorithms have shortcomings in terms of resource utilization and the level of security that they provide. Our research delves into the existing highly secure cryptographic algorithms and provides a comparative analysis of two popular and secure cryptographic algorithms viz N-th Degree Truncated Polynomial Ring (NTRU) and Elliptic Curve Cryptography (ECC) and verifies their applicability in SHS. Recognizing ECC's compact key sizes and its vulnerability to quantum computing threats, our study finds NTRU as a resilient and quantum-resistant alternative, providing a robust defense mechanism in the evolving landscape of healthcare cybersecurity. Key findings underscore the efficacy of NTRU in safeguarding healthcare data, emphasizing its superior performance compared to ECC, especially in the face of emerging quantum computing challenges. The comparative analysis depicts that ECC excels in key generation speed, delivering efficient and swift key creation. However, it requires larger keys to withstand potential quantum computing vulnerabilities. On the other hand, the key generation time in NTRU is slightly more than ECC but being quantum-resistant, it provides high security.

Designing a microstrip patch antenna at 3GHz frequency is very interesting task to the researchers due to wide applicability for IoT, body wearable antenna, WLAN, ISM communication etc. In this paper, a circular microstrip patch antenna with resonance at a frequency of 3GHz has been designed using FR4 substrate and inset feeding using CST studio. Initially, the antenna is manually tuned at the 3GHz operating frequency and obtain S11 is -38dB which is best compare to the existing works at this frequency. Next, the parametric influences have been observed by varying several antenna design parameters (i.e. patch radius and substrate height) and observing the respective variation in output like S11, bandwidth. In the next phase of this work, surface fitting technique has been used to model the 3GHz circular patch antenna that helps to predict the output S11, resonance frequency and bandwidth with satisfactory prediction accuracy. Surface-fitting model will help to reduce the effort and time required for redesigning and simulation of this type of circular patch antenna in future.