International Journal of Computer Science and Technology

International Journal of Computer Science and Technology | Vol. 3, No. 12, December 2022 | pp. 1–8

DOI: 10.46882/2022/IJCST/000215

Article Type: Original Research Paper

Title: Automated Microservice Vulnerability Assessments via Contextual Resource Consumption Profiling

Names of Authors: Klaus Meyer¹, Rachel Green²

Authors’ Affiliations: ¹Institute for Software Systems Security, Munich Technical University, Munich, Germany; ²DevSecOps Research Center, CloudScale Innovations, Austin, USA

Abstract: Monolithic software applications are rapidly transitioning toward cloud-native microservice architectures to achieve higher scalability and deployment flexibility. However, tracking anomalies across hundreds of independent containers introduces significant security and operational complexity. This paper presents an automated security validation framework designed to identify microservice vulnerabilities through non-intrusive container profiling. The system tracks low-level kernel activities, specifically counting system calls alongside memory allocations and CPU usage patterns. It creates a dynamic behavior baseline using an isolation forest algorithm to identify anomalous infrastructure patterns. We tested the tracking mechanism across a continuous integration environment running 80 distinct service modules. The profiling framework successfully identified container escape activities and resource-exhaustion exploits with a 95.4% success rate. The tracking agent operates outside the main application container, maintaining a low resource footprint under 1.8% CPU overhead. This setup ensures continuous software vulnerability assessments without degrading application performance.

Keywords: Microservices, Cloud-Native Security, Container Profiling, System Call Analysis, Automated Detection, DevOps

Manuscript Timeline: Received: May 29, 2022; Revised: July 05, 2022; Accepted: July 29, 2022; Published: December 14, 2022

International Journal of Computer Science and Technology | Vol. 3, No. 11, November 2022 | pp. 1–8

DOI: 10.46882/2022/IJCST/000214

Article Type: Original Research Paper

Title: Lightweight Cross-Layer Congestion Management Protocol for UAV-Assisted Wireless Mesh Networks

Names of Authors: Carlos Silva¹, Fatima Al-Sayed²

Authors’ Affiliations: ¹Department of Mobile Communications, Federal University of Minas Gerais, Belo Horizonte, Brazil; ²Wireless Systems Laboratory, Gulf University for Science, Kuwait City, Kuwait

Abstract: Unmanned Aerial Vehicle (UAV) networks provide critical communication infrastructure during disaster recovery scenarios but frequently suffer from link instability and data congestion. Standard internet transport protocols fail to differentiate between physical channel packet dropouts and buffer congestion states. This research presents a cross-layer congestion routing protocol designed to optimize data transmission across aerial-to-ground communication networks. The protocol links link-layer queue lengths with transport-layer window configurations to estimate network density states accurately. A predictive tracking feature estimates spatial mobility patterns to update network paths before physical signal degradation occurs. We evaluated the communication protocol within a simulated 3D landscape utilizing 15 moving aerial nodes. The tracking results show a 31.5% enhancement in overall data delivery reliability over traditional routing protocols. The average end-to-end packet delivery delay was reduced to 64 ms, stabilizing critical voice and telemetry feeds. This cross-layer coordination prevents throughput degradation caused by rapid node movement in complex aerial environments.

Keywords: Unmanned Aerial Vehicles, Wireless Mesh Networks, Congestion Control, Cross-Layer Optimization, Routing Protocols, Throughput

Manuscript Timeline: Received: May 25, 2022; Revised: July 02, 2022; Accepted: July 26, 2022; Published: November 12, 2022

International Journal of Computer Science and Technology | Vol. 3, No. 10, October 2022 | pp. 1–8

DOI: 10.46882/2022/IJCST/000213

Article Type: Original Research Paper

Title: Quantifying Adversarial Vulnerabilities in Graph Neural Networks via Targeted Topology Perturbation

Names of Authors: Wei-Dong Zhang¹, Amara Okafor²

Authors’ Affiliations: ¹School of Computer Science, Yangtze University of Technology, Wuhan, China; ²Department of Systems Engineering, University of Nigeria, Nsukka, Nigeria

Abstract: Graph Neural Networks (GNNs) are widely used to analyze relational structured applications like financial fraud detection maps and biological interactions. However, their structural dependency makes them highly vulnerable to malicious structural manipulations. This paper introduces an adversarial testing mechanism designed to evaluate GNN resilience by introducing targeted perturbations to node connections. The algorithm calculates node-centrality values alongside structural gradients to find and modify high-impact link structures with minimal modifications. We tested this attack method on standard benchmark network configurations, focusing primarily on GCN and GraphSAGE architectures. The assessment reveals that modifying a tiny fraction (less than 2.5%) of network links degrades node-classification accuracy by 38.4%. This drop demonstrates that minor topology edits can severely bypass standard neural classification security steps. To mitigate this vulnerability, we outline a robust structural defense strategy rooted in localized neighborhood-degree validation. This defense effectively neutralizes up to 72.0% of adversarial edge modifications during training operations.

Keywords: Graph Neural Networks, Adversarial Attacks, Topology Perturbation, Node Classification, Network Robustness, Machine Learning

Manuscript Timeline: Received: May 20, 2022; Revised: June 28, 2022; Accepted: July 22, 2022; Published: October 08, 2022

International Journal of Computer Science and Technology | Vol. 3, No. 9, September 2022 | pp. 1–8

DOI: 10.46882/2022/IJCST/000212

Article Type: Original Research Paper

Title: Minimizing Query Delays in Geographically Replicated NoSQL Systems via Dynamic Consensus Scaling

Names of Authors: Elena Rostova¹, Jean-Luc Picard²

Authors’ Affiliations: ¹Data Engineering Laboratory, NordTech University, Oslo, Norway; ²Distributed Systems Architecture Division, SynthData Systems, Paris, France

Abstract: Maintaining strict transactional consistency across globally distributed database systems typically leads to high query latency and performance bottlenecks. This research proposes an optimized database replication framework that uses an adaptive variant of the Raft consensus algorithm. The algorithm dynamically measures regional round-trip network performance and modifies quorum execution targets based on localized traffic requirements. When operational volatility is minimal, the system relaxes global synchronization states down to localized clusters. It returns to full multi-region confirmation phases exclusively during high write-concurrency periods. We deployed the framework across a distributed cloud testbed containing 12 distinct geographic nodes to evaluate performance. The experimental findings indicate a 42.1% reduction in mean write latency compared to standard, non-adaptive Raft configurations. The transactional throughout achieved a sustainable capacity of 18,500 operations per second under complex read-to-write workloads. This dynamic adjustment methodology minimizes global operational staleness without compromising overall transactional durability metrics.

Keywords: NoSQL Databases, Raft Consensus, Data Replication, Query Latency, Distributed Systems, Transactional Consistency

Manuscript Timeline: Received: May 15, 2022; Revised: June 24, 2022; Accepted: July 19, 2022; Published: September 05, 2022

International Journal of Computer Science and Technology | Vol. 3, No. 8, August 2022 | pp. 57–64

DOI: 10.46882/2022/IJCST/000211

Article Type: Original Research Paper

Title: Real-Time Edge Analytics for Automated Fault Detection in Industrial Cyber-Physical Systems

Names of Authors: Siddharth Mehta¹, Hiroshi Tanaka²

Authors’ Affiliations: ¹Department of Automation and Robotics, Tech-Vanguard Institute, Mumbai, India; ²Industrial IoT Solutions Group, Nippon Automation Corp, Yokohama, Japan

Abstract: High-speed assembly operations within modern manufacturing frameworks require instant anomaly recognition to protect equipment and avoid operational pauses. Traditional architectures transmit raw sensory feeds directly into central databases, inducing substantial network latency delays. This article introduces an adaptive edge-computing framework optimized to run real-time anomaly assessments directly at the device layer. The system relies on a structural One-Class Support Vector Machine (1-SVM) engine designed to flag voltage, thermal, and vibration anomalies. The algorithm monitors machine telemetry patterns continuously and estimates deviation vectors over a moving observation frame. We validated this decentralized architecture on a live multi-axis CNC testbed containing 45 high-frequency sensors. Empirical results prove the system identifies early degradation indicators within a strict latency threshold of ±12 ms. Operating at the device layer reduced the baseline outbound telemetry bandwidth consumption by 74.5%. This shift effectively minimizes cloud storage overhead requirements while preserving edge-layer processing accuracy. A standard t-test confirmed the consistency of the classification precision across multi-day manufacturing runs (p < 0.01).

Keywords: Cyber-Physical Systems, Edge Computing, Fault Detection, Industrial Automation, One-Class SVM, Real-Time Analytics

Manuscript Timeline: Received: May 11, 2022; Revised: June 20, 2022; Accepted: July 15, 2022; Published: August 04, 2022

International Journal of Computer Science and Technology | Vol. 3, No. 7, July 2022 | pp. 49–56

Research Article

Title: A Decentralized Federated Learning Architecture with Non-IID Data Mitigation for Distributed Edge Computing

Names of Authors: Chao Zhang¹, Xianfeng Wang², and Jun Lin³

Authors’ Affiliations:
¹School of Computer Science, Wuhan University, Wuhan, China
²Department of Electronic Information, Huazhong University of Science and Technology, Wuhan, China
³State Key Laboratory of Software Development Environment, Beihang University, Beijing, China

Abstract: Abstract: Centralized federated learning architectures rely heavily on a single orchestrating server to aggregate local gradients, introducing structural single points of failure and significant wide-area network bottlenecks. Furthermore, data collected across heterogeneous edge devices is often non-independently and identically distributed (non-IID), which compromises global model accuracy and decelerates algorithm convergence. This paper introduces an optimized, completely decentralized peer-to-peer federated learning framework designed for edge computing environments without central server dependencies. Individual edge nodes train deep neural network parameters locally on native datasets and exchange model weights exclusively with single-hop topological neighbors using an asynchronous gossip consensus protocol. To counter client-side data divergence, a localized dynamic regularization factor is integrated into the native objective function, punishing severe weight drift away from neighboring consensus baselines. Communication overhead is minimized through an adaptive gradient quantization routine that compresses transmission payload sizes dynamically based on instantaneous wireless link states. Matrix simulations using skewed MNIST and CIFAR-10 data distributions demonstrate that the proposed decentralized protocol achieves a high classification accuracy of 92.4%, matching centralized baseline performance while lowering total wide-area network data transmission volume by 46.2%. The framework provides stable, private machine learning capabilities for high-mobility mobile ad-hoc environments.

Keywords: Federated learning, Edge computing, Gossip protocol, Non-IID data, Gradient compression, Decentralized optimization

Manuscript Timeline: Received: April 14, 2022; Revised: May 20, 2022; Accepted: June 15, 2022; Published: July 1, 2022

Citation: Zhang, C., Wang, X., & Lin, J. (2022). A decentralized federated learning architecture with non-IID data mitigation for distributed edge computing. International Journal of Computer Science and Technology, 3(7), 49–56. DOI: 10.46882/2022/IJCST/000031