International Journal of communication and computer Technologies

Harnessing Quantum Computing and Generative AI for Next-Generation Credit Fraud Detection: Real-Time Anomaly Detection and Adversarial Risk Mitigation

Puneet Pahuja — 2025-11-04

The increasing complexity of financial crimes has revealed severe weaknesses in the conventional credit fraud detection systems. With the future and development of the digital payment ecosystem expanding rapidly around the world, there is an immediate necessity of smart, adaptive, and dynamic solutions, able to intercept the growing trends of fraud. The paper will apply a hybrid model based on combining both Quantum Computing and Generative Artificial Intelligence (GenAI) to transform credit fraud detection. Quantum Computing opens up initial brand-new levels of computational power and parallel processing with an understanding of mass data analysis within transactions. Meanwhile, GenAI advances fraud detection because GenAI can produce fake cases of fraud, simulate human behaviours, and increase model resilience. The combination of these technologies provides the basis for proactive detection of anomalies and dynamic risk evaluation. It is based on a conceptual architecture, which fuses quantum-enhanced learning models with adversarially trained GenAI systems. The framework helps detect anomalies in real-time, adapt to learn a greater diversity of fraud patterns, and mitigate risks adversarial through a convenient methodology based on federated learning and model training secrets. This is a multidisciplinary approach that is natural to both traditional systems based upon rules and establishes the way to a scalable, smart, and regulations-adherent fraud detection infrastructure. Our results indicate a transformative possibility of the integration of emerging technologies to construct next-generation security systems that can predict and defend credit fraud at a greater pace and accuracy.

Protecting Distributed Ledgers from Advanced Persistent Threats Using SVM-Based Blockchain Security

Kiruba Buri R — 2025-11-04

This work focuses on multi-dimensional approach to incorporate the Support Vector Machine (SVM) models with Blockchain to secure distributed ledger against APTs. The classifying and high pattern recognition ability of SVM makes the proposed framework easily capture and neutralize malicious activities in the blockchain networks in realtime. The distribution of the blockchain technology and use of machine learning for predictive modeling guarantees a hard-coded countermeasure against new forms of cyber threats. As such, this work is centered on how these technologies can be integrated in harmony: attempting to enhance the accuracy of threat identification without compromising the functionality of the blockchain. This implementation shows the possibility of achieving strong, secure and scalable applications in different applications domains, and so make a way forward for upcoming decentralized cybersecurity solutions.

Adaptive Network Monitoring Framework for IoT Communication Protocols

Srikanth Reddy Keshireddy — 2025-06-15

The fast development of heterogeneous Internet of Things (IoT) ecosystems has turned real-time network monitoring into a necessary element to be taken into consideration to guarantee reliability and security. In this paper, a flexible monitoring system is described that uses passive metering methods to examine communication protocols like MQTT, CoAP and AMQP in large internet of things implementation. The framework has applied flow level metadata, time based correlation, and profiling of devices to identify anomalies without probing. Experimental testing of 1,200 IoT devices incorporates 92 percent accuracy in detecting the aberrant behaviour and protocol abuse. The presented solution offers a protocol-agnostic network intelligence base and enables the following-generation IoT traffic auditing and intrusion prevention system.

A Global Internet Performance Visualization Framework Using Passive Network Telemetry

Harsha Vardhan Reddy Kavuluri — 2025-08-20

The increased complexity and physical dispersal of the world-scale Internet ecosystem require new systems of monitoring and visualization of real-time performance indicators. In this paper, a visualization framework that is scalable and adaptive is provided and incorporates passive network telemetry such as latency, throughput, and packet loss at distributed monitoring nodes on a global scale. The framework utilizes a map-based, dynamically rendered interface to display the time-sensitive Internet health measurements of regions enabling stakeholders to realize patterns of congestion, routing anomalies, and transcontinental asymmetries. We examine longitudinal network weather patterns with twelve months of global traceroute and flow-level data to determine the temporal patterns of correlation between geopolitical and geopolitical-connectivity-related events. The model utilizes an edge-cloud hybrid model that uses multi-tier architecture to facilitate data ingestion, extract feature, and render data through the design. Findings reveal that the framework is able to show performance degradation at spatial and temporal resolutions, which improves the Internet observability of network operators and policy researchers. The proposed solution offers a protocol-agnostic base of performance analytics that is capable of supporting massive decision support in world network operations centres and giant Internet exchanges.

Performance Evaluation of SSL/TLS Handshake Latency in Distributed Web Service Architectures

T M Sathish Kumar — 2025-08-30

With encrypted communication becoming widespread in the Internet, the performance of the SSL/TLS handshakes has become critical to ensuring the fast and secure web experience. This paper is a detailed analysis of handshake latency between distributed web service systems with modern transport protocols (HTTP/2 and QUIC) with particular references to the choice of cryptographic algorithms, key exchange, as well as key resumption. Tests were conducted on 100 globally spread cloud servers and handshake behaviour was tested in different key exchange cryptography, such as RSA-2048, ECDHE-RSA, and ECDHE-ECDSA. As it has been found, TLS 1.3 leads to a shorter handshake latency than TLS 1.2 by about 35 percent, mainly because of its more streamlined handshake structure and fewer round trips. Also, session ticket reuse and 0-RTT handshakes obtained sub-30 ms negotiation times, which proves that they are economical in applications with a high latency concern and edge-based content delivery. The paper discusses the trade-off between the robustness of security and latency efficiency and presents optimization strategies in the case of distributed systems that trade-off between cryptographic overhead, handshake resumption and global deployment complexity.

Empirical Evaluation of IP Geolocation Accuracy Using Global Network Measurement Datasets

Charpe Prasanjeet Prabhakar — 2025-07-15

Proper IP geolocation is the key to cybersecurity, optimization of content delivery and digital forensics. This paper gives a mass empirical analysis of IP geolocation accuracy on 120,000 Internet hosts found in over 90 countries. The performance of major enterprise and open-source databases was tested on the basis of the active delay-based probing, DNS analysis, and RIPE Atlas measurement records. Findings show that median location errors of urban networks lie in the range of 25 to 120km with much more loss of accuracy in mobile and carrier-grade NAT networks. The study recognizes the geographic variables as well as network-layer variables (that have an effect on location precision) such as routing asymmetry, infrastructure centralization and latency variance. The results indicate that hybrid solutions of geolocation based on delay-based triangulation, complemented by regional measurement anchoring, are more effective than brand-new database-based solutions. The paper ends by suggesting adaptive, measurement-inspired frameworks of geolocation that are inclusive of the network information gathered by crowdsourcing to have better spatial precision to support more robust geolocation-dependent apps in digital security and cyber services.

Comparative Analysis of Network Measurement Tools for QoS Evaluation in 5G Communication Environments

Moti Ranjan Tandi — 2025-07-30

The Quality of Service (QoS) testing in 5G networks necessitates the use of very sophisticated measurement instruments that are able to control high throughput, low latency, and heterogeneous traffic patterns. In this paper, a thorough comparative analysis of user-friendly network measurement models iPerf3, OWAMP, NetPerf, and 5G-MONarch will be discussed based on both simulated and real-world testbeds. The metrics that are used in the analysis include latency, jitter, bandwidth usage, and the ratio of packet delivery to different network slicing specifications. Findings show that active probing tools are more accurate in their latency and jitter measurements whereas passive flow-based analytics are more scalable and less overhead in a dense communication setup. This paper has found that there are key trade-offs between accuracy, system load, and adaptability, and that hybrid measurement strategies can achieve a trade-off between monitoring fidelity and scalability. Its results can be discussed as the next step in the development of 5G QoS assessment because they indicate researchers and network operators about the feasibility of the existing measurement frameworks in the conditions of high-performance and heterogeneous network environments.

Active Network Probing Framework for Real-Time Detection of Anomalous Internet Traffic Patterns

P. Kharabi — 2025-08-13

The growing size and sophistication of the worldwide Internet systems require effective techniques of real-time monitoring of abnormal network activity. The given paper presents an active network probing framework which repeatedly measures the quality of end-to-end Internet paths with lightweight ICMP and TCP-based measurement packets. The framework is dynamically tweaked to either shorten or lengthen its probing interval, based on the volatility of the network that it monitors, allowing it to distinguish between a benign congestion event and an anomaly caused by an attack, e.g. by Distributed Denial-of-Service (DDoS) amplification or route hijacking. The probes are spread on ten geographically different vantage points that offer coverage and a high level of temporal granularity. Information regarding the collected data is analysed with a hybrid statistical-machine learning model that matches traffic deviations on real-time. Experimental tests had 92% accuracy in anomaly detection using less than 1% bandwidth footprint, which means that it does not cause much interference to the traffic being carried out. Findings prove the scalability, flexibility and efficiency of the framework in improving the visibility of the performance of the global Internet. The study will help in the development of non-invasive, proactive, and distributed network monitoring systems to ensure service reliability and safety in the current communication infrastructures.

Comparative Performance Evaluation of Throughput in Wireless Mesh and Cellular Networks Using Empirical Measurement Models

Cristine Re-Ann — 2025-08-24

It is a comparative performance analysis of throughput dynamics in wireless mesh network (WMNs) and 4G/5G cellular communication system concerning various mobility, topology and load conditions. A hybrid measurement system involving empirical field testing and NS-3 simulation was created to determine the effect of parameters of node density, handover frequency, interference and scheduling mechanism on end-to-end throughput performance. The results indicate that WMNs ensure a greater throughput stability and link stability in the case of a topology that is either static or semi-static because of good route reuse and peer-assisted relaying. Conversely, the 4G/ 5G cellular networks are superior to the WMNs in high mobility cases due to adaptive modulation, spectrum reuse, and handover optimization. Comparison of results brings out the trade-offs between the diversity of the multi-hop paths and centralized scheduling efficiency. The paper also suggests a hybrid system applying WMN backhaul and 5G edge nodes in order to combine the complementary benefits of both paradigms. This integrated approach presents a positive change in throughput and spectral efficiency as well as reliability, which gives the insights to design a next-generation heterogeneous communication infrastructure.

Temporal Modeling and Forecasting of Internet Routing Table Growth Using Time-Series Analysis

L.K. Pamije — 2025-08-10

The ongoing growth of Internet routing tables in the world presents severe problems to the scalability, convergence time, and stability of the inter-domain routing systems. This study examines time series routing table growth patterns with time-series modelling tools, such as Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), and Prophet regression models. Border Gateway Protocol (BGP) snapshot data, ten years of they were gathered in RouteViews and RIPE RIS repositories in order to analyse the temporal changes, volatility, and prediction performance. The decomposition of the statistical trend showed a steady exponential increase in prefix advertisements, which were closely related to the use of IPv6, multi-homing, and the use of traffic engineering. The forecasting models predict that it will keep growing and this may impose a strain on router memory and forwarding information base (FIBs) in the next five years unless the current policies are changed. High predictive reliability is confirmed by model validation based on root mean square error (RMSE) and mean absolute percentage error (MAPE), which indicates that route aggregation and policy optimization are necessary. These anticipative outlooks can inform network operators and protocol developers to increase the capacity of routing and proactive planning of capacity against default-free zones.

Comparative Analysis of Real-Time Communication Protocols for Reliable Smart Factory IoT Architectures

R. Rudevdagva — 2025-08-24

The fast progress of Smart Factory ecosystems within the Industry 4.0 and the new Industry 5.0 paradigms requires communication infrastructures capable of establishing reliable and deterministic and ultra-low-latency information exchange among the heterogeneous devices of the Industrial Internet of Things (IIoT). With the implementation of more and more cyber-physical production systems, autonomous robotics, digital twins, and predictive maintenance systems into modern manufacturing settings, the need to choose the right communication protocol will become vital to ensuring continuity in operational processes and real-time responsiveness. The paper will be a comparative overview of significant real-time communication protocols used in industrial automation such as MQTT, CoAP, AMQP, OPC UA PubSub, Data Distribution Service (DDS) and Time-Sensitive Networking (TSN). This is evaluated in key areas of performance, such as latency, jitter, reliability, bandwidth efficiency, scalability, interoperability, and security overheads, and at realistically applicable Smart Factory workloads. Performance modeling using simulations, use-case benchmarking, and evaluating hybrid architecture are some of the applications used to determine protocol behavior in robotic arm control, automated guided vehicle (AGV) coordination, equipment health monitoring, and real-time digital twins synchronization. The findings show that TSN and DDS provide superior determinism, timing precision in safety critical control loops whereas OPC UA PubSub has a high interoperability capability, which is appropriate in cross vendor integration. MQTT and AMQP are both scalable and robust to large scale monitoring but do not provide any fast timing guarantees needed in highly critical systems. The results indicate that there is no particular protocol that will suit the entire industrial communications requirements; a layered hybrid system involving TSN-basis deterministic transport and DDS middleware with OPC UA middleware provides the best performance under high demands conditions such as manufacturing. The study will offer a structured protocol-selection scheme and practical evidence to the system architects so that they can use trusty, effective, and future-oriented communication infrastructure in next-generation Smart Factory IoT architectures.

Communication-Aware Data Contract Architecture for Secure, Reliable, and Privacy-Preserving AI Pipelines

Haitham M. Snousi — 2025-08-24

AI workflows are becoming progressively based on a distributed data ecosystem whereby various parties share, process, and use data with heterogeneous infrastructures. These environments increase the weaknesses of inefficiencies in communication, privacy, lack of consistency in trust boundaries, and breach of contract concerning data management. The present paper suggests a communication-conscious data contract architecture which combines verifiable data exchange rules, multi-layer privacy controls, and dynamic reliability controls towards trustful AI pipelines. The architecture uses an official data contract interface that characterizes semantics, transport demands, lineage regulations, accuracy limitations and communication guarantees between manufacturers, processors as well as AI models. Communication-aware orchestration model is a dynamically optimized pipeline model with network telemetry, trust signals and privacy budgets, used to dynamically optimize the behaviour of a pipeline. An environment of security implements compliance with the contracts on the basis of cryptographic proofs, policy compilers, and lightweight federated execution sandboxes. Through experimental analysis of simulated multi-party AI workflows, it is shown that there are improvements in communication cost predictability, policy violation detection, data lineage transparency and privacy budget stability. The implications of these findings include the fact that communication-conscious, contract-based data governance must be the focus of the AI systems design in order to make them scalable, secure, and privacy-conservative.