Authors - The Quan Trong, Nguyen Trong Nhan Abstract - An autocorrelation receiver can be employed in surveillance and communication systems to identify the type and operating mode of radiation sources in the absence of a priori signal information. In this work, the autocorrelation receiver is defined as a system comprising a broadband analog front-end with frequency conversion to the intermediate frequency range and a narrowband processing unit based on autocorrelation. The performance of signal processing is governed by the received pulse duration and the length of the fast Fourier transform (FFT) window. The study provides an estimate of the signal-to-noise ratio (SNR) required to achieve a specified probability of correct classification of simple radio pulses at a fixed false alarm rate. The results show that increasing the pulse duration while maintaining a fixed FFT window (i.e., reducing the ratio of FFT window length to pulse duration) decreases the required SNR. Consequently, the probability of correct classification is improved under these conditions. Furthermore, high receiver efficiency is achieved when the ratio of the FFT window length to the pulse duration is kept below 10. If the number of FFT samples is fixed, further improvement in classification performance requires increasing both the sampling frequency and the processing rate.
Authors - Uphaar Goyal, Chirag Patadia, Nitinkumar Leuva Abstract - Modern software development environments depend on cloudnative infrastructure, automated CI/CD pipelines, and distributed DevSecOps workflows. These environments improve delivery speed but expand the attack surface through privilege escalation, credential compromise, insider threats, and misconfigured pipeline permissions. Traditional Role-Based Access Control (RBAC) improves least-privilege enforcement, but static RBAC does not adequately respond to changing runtime context such as business hours, network trust, multi-factor authentication status, and deployment pipeline state. This paper proposes an enhanced cybersecurity framework that integrates RBAC, contextaware policy enforcement, and Agentic AI-based autonomous auditing. The proposed agent observes access events, learns behavioral patterns, detects anomalous requests, and generates explainable audit evidence without replacing deterministic access control. Experimental evaluation in a simulated CI/CD environment shows that the RBAC with Agentic AI framework achieves 98.21% accuracy, improves anomaly detection compared with traditional access-control models, and maintains low enforcement latency under increasing concurrency. The results indicate that Agentic AI can strengthen secure software development by adding adaptive audit intelligence while preserving the predictability and administrative clarity of RBAC.
Authors - Thokozani Nkosinathi Hlubi, Nazeer Joseph Abstract - Digital transformation is reshaping the auditing profession by introducing advanced digital tools, automation, and data‑driven processes that redefine how audits are planned, executed, and evaluated. This study examines the effect of digital transformation on auditing processes, focusing on how digital tools, automation technologies, and shifting skill requirements influence audit effectiveness and efficiency. Using a qualitative research design, Rich Picture workshops were conducted with practicing auditors to explore how emerging technologies are integrated into real-world audit environments. The findings reveal three key themes. First, digital tools enhance real‑time access to information, improve collaboration, and deepen auditors’ understanding of complex IT environments. Second, automation significantly improves audit effectiveness by streamlining routine tasks, supporting anomaly detection, and enabling more robust risk assessments—while still requiring professional judgment. Third, efficiency gains emerge through time savings, resource optimization, and evolving competency requirements, underscoring the need for continuous upskilling. Building on these insights, the study proposes a four‑stage implementation framework consisting of strategic alignment, workforce development, workflow redesign, and ethical governance safeguards. The research contributes to both theory and practice by demonstrating how digital transformation reshapes audit work and offering a structured roadmap for organizations seeking to modernize their audit functions responsibly and sustainably.
Authors - Zumna Usman, Madiha Khalid, Weiwei Jiang, Momina Shaheen, Umar Mujahid, Muhammad Najam-ul-Islam Abstract - The Internet of Things (IoT) networks operate under strict resource constraints having limited computational capability, memory, bandwidth, and energy, while still being required to combine essential security goals such as confidentiality and mutual authentication with efficiency, particularly in Radio-Frequency Identification (RFID)-based systems. For this reason, Ultra-Lightweight authentication protocols are commonly used, where traditional cryptographic techniques are often too demanding. The Random Rearrangement Block Matrix-Based Ultra- Lightweight RFID Authentication Protocol (RUAP) was introduced to strengthen security. In this paper, RUAP is analyzed and shown not to eliminate fundamental weaknesses. By applying a probabilistic disclosure attack, it is shown that public messages leak exploitable statistical information, making it possible to fully recover the identifier in reduced configurations and to recover about 71.77% of a 96-bit identifier. It is further shown that RUAP’s asymmetric key update mechanism allows adversaries to trigger desynchronization, resulting in denial of service.
Authors - Rajiv Ghai, Anil Kumar Bisht, Akash Sanghi Abstract - Agricultural supply chains face a fundamental tension consumers require transparency for provenance verification while commercial stakeholders demand confidentiality of pricing, buyer identities and quality scores. This paper introduces SelEncChain-Agri, a selective field encryption (SFE) framework that resolves these conflicting demands on a single fully decentralised Solana blockchain. Supply chain data is partitioned into public fields stored in plaintext and confidential fields encrypted with ECIES combined with Multi-Party Key Encapsulation (MP-KEM). Authorised parties decrypt fields using existing Solana wallet keypairs via a formally specified Ed25519-to-X25519 key derivation (libsodium convention), requiring no additional key material or trusted third party. Three technical contributions are made: (1) SFE field encryption with per-capsule independently randomised nonces preventing GCM keystream reuse (2) SFE decryption and (3) a key revocation protocol providing post-revocation forward secrecy. Security analysis under the Dolev-Yao model provides a constructionlevel IND-CPA argument under the DDH assumption on Curve25519. Comparative evaluation shows SelEncChain-Agri satisfies all eight stakeholder requirements from the agricultural blockchain literature, versus at most seven for dualchain alternatives. A Solana devnet prototype confirms functional correctness: end-to-end latency was 3,691 ms for batch creation and 2,111 ms for event submission; client-side SFE overhead was 74.59 ms (Python), with estimated pure cryptographic cost of 3.3 ms (Rust/WebAssembly). Keywords: Agricultural supply chain · Anchor framework · DPDP Act 2023 · ECIES · Food traceability · Key revocation · MP-KEM · Privacy-preserving · RFC 7748 · Selective field encryption · Solana blockchain
Authors - Austin jojo Jallah, Reena Satpute Abstract - As the transformer language models were developed, it was possible to introduce one of the significant technological revolutions in conversational AI. Basically, the understanding and production of human language has been enhanced by transformers. Transformer models make it possible to build dialogue systems that provide natural, complex, and context-sensitive communication to develop further customer support technologies, healthcare technologies, and virtual assistance services. In the current paper, this paper will assess advanced transformer systems, BERT, GPT, and T5, as well as their variants, assessing their performance capabilities in the use of dialogue. All the models are evaluated in terms of performance quality, which is judged by its fluency production and also gauges of coherence and contextual accuracy and its general ability to pro-cess computations. This review talks about the prompt engineering approaches and the human feedback-enhanced learning through reinforcement (RLHF) and the adapter transfer learning methods, to improve the flexibility and quality of the model. The paper presents the fresh trends in Conversational AI with multi-modal learning as well as retrieval-enhanced generation and factuality-based coherence knowledge application. Nevertheless, transformer-based models have three key weaknesses, among which, there are bias and hallucinations, and the computing requirements are very high. These weaknesses are analyzed and we discuss the potential remedies that involve symbolic deep learning combinations and efficient compression methods, such as quantification and pruning. We have found out the extent to which each system can do and the things that they cannot accomplish, and hence can help to determine the right applications of each of the frameworks. We introduce an evaluation comparison as a scholarly resource to the practitioners of dialogue system development so they can perform better with moral and effective models of operations. The article concentrates on the devel-opment of transformer-based conversational AI and its estimated impact on hu-man-computer dialogue systems
