International Journal of Computer Science and Technology

International Journal of Computer Science and Technology | Vol. 11, No. 9, September 2009 | pp. 65–72

DOI: 10.46882/2009/IJCST/000018

Article Type: Original Research

Title: Performance Analysis of Ad-Hoc On-Demand Distance Vector Routing Protocol Under Blackhole Attacks

Names of Authors: Samuel K. Mensah¹, Kwame Opoku²

Authors’ Affiliations: ¹Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; ²School of Engineering and Technology, University of Ghana, Legon, Ghana

Abstract: Mobile Ad-Hoc Networks (MANETs) are intrinsically vulnerable to security exploits due to their decentralized architecture and dynamic topology. This study systematically analyzes the degradation of the Ad-Hoc On-Demand Distance Vector (AODV) routing protocol when subjected to single and collaborative malicious blackhole nodes. In a blackhole attack, a malicious node falsely claims the shortest route to drop traffic packets completely. Using NS-2 simulations, we evaluated performance matrices including packet delivery ratio (PDR), end-to-end delay, and routing overhead across networks varying from 20 to 100 mobile nodes. The data reveals that a single blackhole node can degrade network PDR by up to 62.5%, while a team of three collaborative blackhole nodes can collapse the PDR to less than 12.0%. The average end-to-end network latency escalated by an factor of 2.4±0.3 seconds under high adversarial presence. These quantitative evaluations underscore the critical need for cryptographic path validation mechanisms within reactive routing structures.

Keywords: Mobile Ad-Hoc Networks, AODV Protocol, Blackhole Attack, Network Security, Packet Delivery Ratio, NS-2 Simulations

Manuscript Timeline: Received: January 28, 2009; Revised: March 10, 2009; Accepted: April 5, 2009; Published: September 12, 2009

Citation: Mensah, S. K., & Opoku, K. (2009). Performance Analysis of Ad-Hoc On-Demand Distance Vector Routing Protocol Under Blackhole Attacks. International Journal of Computer Science and Technology, 11(9), 65–72.

International Journal of Computer Science and Technology | Vol. 11, No. 7, July 2009 | pp. 49–56

DOI: 10.46882/2009/IJCST/000016

Article Type: Original Research Paper

Title: An Efficient Load Balancing Algorithm for Distributed Cloud Environments

Names of Authors: Hiroshi Tanaka¹, Kenji Sato²

Authors’ Affiliations: ¹Department of Information and Computer Sciences, Osaka University, Osaka, Japan; ²Research & Development Division, NextGen Cloud Solutions, Tokyo, Japan

Abstract: Dynamic resource allocation and workload distribution are critical for maintaining high performance in cloud computing networks. This study presents a Dynamic Threshold-based Load Balancing (DT-LB) algorithm designed to mitigate server congestion and minimize task allocation delays in heterogeneous cloud environments. The proposed model continuously monitors virtual machine parameters including CPU utilization, memory residency, and network bandwidth consumption to migrate tasks proactively before a node reaches full capacity. We evaluated the algorithm via CloudSim simulations across a network topology containing 500 virtual machines under variable workloads. The experimental results show that DT-LB reduces the average response time by 22.4% and drops the task rejection rate to less than 1.5% under peak traffic loads. Statistical validation underscores a significant enhancement in cluster utilization efficiency compared to standard Round-Robin and least-connection techniques (p < 0.01). We conclude that our thresholding mechanism provides steady operational scaling for enterprise data centers experiencing highly fluctuating demand profiles.

Keywords: Cloud Computing, Load Balancing, Virtual Machine Migration, Resource Allocation, Distributed Systems, CloudSim

Manuscript Timeline: Received: December 5, 2008; Revised: January 20, 2009; Accepted: February 12, 2009; Published: July 8, 2009

Citation: Tanaka, H., & Sato, K. (2009). An Efficient Load Balancing Algorithm for Distributed Cloud Environments. International Journal of Computer Science and Technology, 11(7), 49–56.

International Journal of Computer Science and Technology | Vol. 11, No. 6, June 2009 | pp. 41–48

DOI: 10.46882/2009/IJCST/000015

Article Type: Review Article

Title: A Comprehensive Review of Semantic Web Ontologies and Knowledge Representation Techniques

Names of Authors: Amelie Dubois¹, Hans-Dieter Schmidt²

Authors’ Affiliations: ¹Laboratoire d’Informatique, Universite Paris-Saclay, Paris, France; ²Institute for Applied Computer Science, Technical University of Munich, Munich, Germany

Abstract: The Semantic Web paradigm transforms unstructured web data into machine-readable knowledge frameworks to allow intelligent agent interoperability. This review paper provides a structured evaluation of state-of-the-art knowledge representation languages and ontologies developed over the past decade, including RDF, RDFS, and OWL2 profiles. We analyze their underlying description logics, expressiveness boundaries, and computational complexities during reasoning tasks. The paper details the practical limitations of current reasoners like Pellet and HermiT when executing large-scale web graphs containing over 100,000 assertions. Key architectural challenges regarding ontology alignment, semantic mapping, and schema reconciliation within decentralized federated databases are thoroughly categorized. Finally, we highlight emerging trends, such as the convergence of deep learning models with structural symbolic knowledge bases to improve natural language understanding. This synthesis provides developers and researchers with a clear roadmap for selecting optimal ontological frameworks based on scale and reasoning constraints.

Keywords: Semantic Web, Resource Description Framework, Web Ontology Language, Knowledge Representation, Ontology Alignment, Description Logics

Manuscript Timeline: Received: September 15, 2008; Revised: November 30, 2008; Accepted: January 5, 2009; Published: February 25, 2009

Citation: Dubois, A., & Schmidt, H. D. (2009). A Comprehensive Review of Semantic Web Ontologies and Knowledge Representation Techniques. International Journal of Computer Science and Technology, 11(6), 41–48.

International Journal of Computer Science and Technology | Vol. 11, No. 5, May 2009 | pp. 33–40

DOI: 10.46882/2009/IJCST/000014

Article Type: Research Paper

Title: An Empirical Study of Software Fault Prediction Models Using Object-Oriented Metrics

Names of Authors: Sarah Jenkins¹, Carlos Mendez²

Authors’ Affiliations: ¹Department of Software Engineering, University of Waterloo, Ontario, Canada; ²Department of Computing, Universidad de Buenos Aires, Buenos Aires, Argentina

Abstract: Early detection of software bugs is critical to minimizing maintenance costs and improving overall software reliability. This study investigates the performance of software fault prediction models utilizing Chidamber and Kemerer (CK) object-oriented metrics. We evaluated six core metrics, including Depth of Inheritance Tree (DIT) and Number of Children (NOC), across five open-source Java projects containing a total of over 12,500 classes. Logistic regression, random forests, and naive Bayes algorithms were applied to categorize fault-prone modules. The Random Forest model demonstrated the highest predictive capability, yielding an Area Under the Curve (AUC) value of 0.892 and a sensitivity score of 84.6%. The empirical findings show that Coupling Between Object classes (CBO) and Response For a Class (RFC) are the strongest statistical indicators of software defects (p < 0.01). Integrating these metrics into automated continuous integration pipelines allows engineering teams to identify problematic code components prior to production deployment.

Keywords: Software Fault Prediction, Object-Oriented Metrics, CK Metrics, Software Quality Assurance, Random Forest, Empirical Evaluation

Manuscript Timeline: Received: October 20, 2008; Revised: December 10, 2008; Accepted: January 22, 2009; Published: February 22, 2009

Citation: Jenkins, S., & Mendez, C. (2009). An Empirical Study of Software Fault Prediction Models Using Object-Oriented Metrics. International Journal of Computer Science and Technology, 11(5), 33–40.

International Journal of Computer Science and Technology | Vol. 11, No. 4, April 2009 | pp. 25–32

DOI: 10.46882/2009/IJCST/000013

Article Type: Original Research

Title: A Novel Data Compression Algorithm for Cloud Data Repositories

Names of Authors: Li Wei¹, David Chang²

Authors’ Affiliations: ¹School of Software Engineering, Tsinghua University, Beijing, China; ²Department of Computer Science, Stanford University, Stanford, USA

Abstract: Exponential growth in corporate data generation requires efficient storage techniques to optimize bandwidth and minimize cloud hosting infrastructure costs. This article introduces the Bit-Level Adaptive Compression (BLAC) algorithm, a lossless data compression technique tailored specifically for large-scale structured cloud repositories. BLAC dynamically switches encoding strategies between modified Huffman coding and run-length encoding based on the real-time entropy calculation of data blocks. We benchmarked the algorithm against standard GZIP and LZW formats using text, XML, and numeric databases. Results indicate that BLAC achieves a compression ratio improvement of 1.45:1 over traditional LZW while maintaining comparable CPU execution overhead. The average throughput reached 450 MB/s during decompression cycles on standard hardware configurations. This balanced efficiency drastically lowers data transmission latencies across cloud environments, proving highly beneficial for multi-tenant data centers handling petabyte-scale transactions.

Keywords: Data Compression, Lossless Encoding, Cloud Storage, Huffman Coding, Entropy Analysis, Throughput Optimization

Manuscript Timeline: Received: November 1, 2008; Revised: December 15, 2008; Accepted: January 18, 2009; Published: February 18, 2009

Citation: Wei, L., & Chang, D. (2009). A Novel Data Compression Algorithm for Cloud Data Repositories. International Journal of Computer Science and Technology, 11(4), 25–32.

International Journal of Computer Science and Technology | Vol. 11, No. 3, March 2009 | pp. 17–24

DOI: 10.46882/2009/IJCST/000012

Article Type: Research Article

Title: Enhancing Network Security via Hybrid Intrusion Detection Systems with Machine Learning

Names of Authors: Robert L. Vance¹, Sanjay Kumar², Elena Rostova³

Authors’ Affiliations: ¹Cybersecurity Research Center, Tech University, Austin, USA; ²Department of Information Technology, National Institute of Technology, New Delhi, India; ³Faculty of Computer Science, Saint Petersburg State University, St. Petersburg, Russia

Abstract: The proliferation of sophisticated cyber threats necessitates advanced security frameworks capable of identifying novel attack vectors. This study proposes a Hybrid Intrusion Detection System (HIDS) combining anomaly-based and signature-based detection mechanisms powered by a Support Vector Machine (SVM) and a Decision Tree classifier. By fusing these approaches, the system maintains high accuracy for known signatures while identifying zero-day variations. The model was evaluated using the benchmark KDD Cup 99 dataset, achieving an overall detection rate of 98.6% and a low false-positive rate of 0.42%. Experimental analysis indicates that the hybrid model outperforms standalone classifiers by a margin of 3.5% in multi-class classification tasks. Computational latency tests revealed an average processing time of 12.3 microseconds per packet, rendering it suitable for real-time intrusion screening in gigabit networks. The findings establish that integrating machine learning into hybrid setups significantly mitigates network vulnerability with minimal performance overhead.

Keywords: Intrusion Detection System, Hybrid Machine Learning, Support Vector Machine, Cyber Security, Packet Analysis, Network Anomaly

Manuscript Timeline: Received: October 5, 2008; Revised: December 2, 2008; Accepted: January 10, 2009; Published: February 12, 2009

Citation: Vance, R. L., Kumar, S., & Rostova, E. (2009). Enhancing Network Security via Hybrid Intrusion Detection Systems with Machine Learning. International Journal of Computer Science and Technology, 11(3), 17–24.