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International Journal of Computer Networks and Applications

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2021
2023

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Geetha, S.

Towards Improved Detection of Intrusions with Constraint-Based Clustering (CBC)

Abstract Views :219 | PDF Views:0

Authors

J. Rene Beulah ¹, C. Pretty Diana Cyril ¹, S. Geetha ², D. Shiny Irene ³

Affiliations
1 Department of Computer Science and Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Kanchipuram, Chennai, Tamil Nadu, IN
2 Department of Information Science and Engineering, CMR Institute of Technology, Bangalore, IN
3 Department of Computer Science and Engineering, SRM Institute of Science and Technology, SRM Nagar, Chennai, Tamil Nadu, IN

Source

International Journal of Computer Networks and Applications, Vol 8, No 1 (2021), Pagination: 28-43

Abstract

The modern society is greatly benefited by the advancement of the Internet. The quick surge in the number of connections and the ease of access to the Internet have given rise to tremendous security threat to individuals and organizations. In addition to intrusion prevention techniques like firewalls, intrusion detection systems (IDS) are an obligatory level of safety for establishments to identify insiders and outsiders with malicious intentions. Anomaly-based IDS is in the literature for the last few decades, but still the existing methods lack in three main aspects – difficulty in handling mixed attribute types, more dependence on input parameters and incompetence in maintaining a good balance between detection rate (DR) and false alarm rate (FAR). The research work proposed in this paper proposes a semi supervised IDS based on outlier detection which first selects the important features that help in identifying intrusive events and then applies a constraint-based clustering algorithm to closely learn the properties of normal connections. The proposed method can handle data with mixed attribute types efficiently, requires less number of parameters and maintains a good balance between DR and FAR. The standard NSL-KDD benchmark dataset is used for performance evaluation and the experimental results yielded an overall DR of 99.52% and FAR of 1.15%. It is successful in identifying 99.81% of DoS attacks, 99.71% of Probe attacks, 98.73% of R2L attacks and 96.50% of U2R attacks.

Keywords

Anomaly, Classification, Feature Extraction, NSL-KDD Dataset, Outlier, Intrusion Detection.

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Decisiveness PSO-Based Gaussian AOMDV (DPSO-GAOMDV) Routing Protocol: Smart Routing for Dynamic Traffic Conditions in Stochastic Vehicular Ad Hoc Network

Abstract Views :81 | PDF Views:1

Authors

M. Kayalvizhi ¹, S. Geetha ²

Affiliations
1 Department of Computer Science, Sri Krishna Adithya College of Arts and Science, Coimbatore, Tamil Nadu, IN
2 Department of Computer Science, Government Arts and Science College for Women, Coimbatore, Tamil Nadu, IN

Source

International Journal of Computer Networks and Applications, Vol 10, No 5 (2023), Pagination: 850-867

Abstract

Vehicular Ad Hoc Networks (VANETs) have gained prominence in vehicular communication due to their potential to enhance road safety, traffic efficiency, and infotainment services. However, the evolution of Stochastic VANETs (SVANETs) has introduced a layer of uncertainty, where vehicular interactions are influenced by dynamic factors such as varying traffic conditions, changing communication environments, and unpredictable link qualities. Routing within SVANETs presents distinct challenges stemming from the stochastic nature of the environment. Traditional routing protocols struggle to maintain reliable connections amidst fluctuating link conditions, leading to increased latency, dropped packets, and inefficient route utilization. The novel “Decisiveness PSO-Based Gaussian AOMDV (DPSO-GAOMDV) Routing Protocol” is introduced to address these challenges. This innovative protocol combines the predictive power of Gaussian-Anticipatory On-Demand Distance Vector (GAOMDV) routing with the dynamic adaptability of Particle Swarm Optimization (PSO). GAOMDV’s ability to anticipate link stability using Gaussian distribution is integrated with DPSO’s agility in optimizing routing decisions. The simulation phase of the study evaluates the DPSO-GAOMDV protocol under various stochastic vehicular scenarios. The protocol’s performance is thoroughly analyzed by emulating real-world traffic conditions and communication dynamics. The simulation results underscore the protocol’s efficacy in reducing route maintenance overhead, improved packet delivery ratios, and enhanced network stability. The predictive insights and dynamic optimization mechanisms showcase its potential to drive innovative, resilient and efficient routing strategies in the face of stochastic vehicular conditions.

Keywords

Ad Hoc Network, Bio-Inspired Optimization, Routing, Stochastic, VANET, Vehicle.

Full Text

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Efficacy Artificial Bee Colony Optimization-Based Gaussian AOMDV (EABCO-GAOMDV) Routing Protocol for Seamless Traffic Rerouting in Stochastic Vehicular Ad Hoc Network

Abstract Views :55 | PDF Views:1

Authors

M. Kayalvizhi ¹, S. Geetha ²

Source

International Journal of Computer Networks and Applications, Vol 10, No 6 (2023), Pagination: 993-1014

Abstract

Vehicular Ad Hoc Networks (VANETs) have emerged as a dynamic communication paradigm enabling vehicles to form temporary Ad Hoc networks for seamless information exchange. Stochastic VANETs (SVANETs) introduce complexities due to their stochastic nature, necessitating innovative strategies to handle dynamic traffic conditions and intermittent connectivity. Routing within SVANETs presents unique challenges arising from uncertainties inherent in real-world scenarios. The stochastic environment gives rise to intermittent connectivity, dynamic traffic conditions, and varying network topologies. Traditional routing protocols struggle to provide efficient and reliable solutions under these challenging circumstances. This paper introduces the Efficacy Artificial Bee Colony Optimization-Based Gaussian AOMDV (EABCO-GAOMDV) routing protocol as a promising solution for the routing challenges in SVANETs. The protocol integrates the intelligence of Artificial Bee Colony Optimization (EABCO) with the adaptive characteristics of Gaussian AOMDV, aiming to enhance the efficiency of route discovery and rerouting. Through extensive simulations encompassing diverse SVANET scenarios, EABCO-GAOMDV is rigorously evaluated for performance and effectiveness. The protocol substantially improves route stability, packet delivery ratio, and end-to-end delay. The simulation results unequivocally validate the protocol’s ability to adapt to stochastic conditions, ensuring effective traffic rerouting and heightened network resilience. EABCO-GAOMDV showcases its potential as a robust routing solution for SVANETs, effectively addressing the challenges of stochastic conditions.

Keywords

Ad Hoc Network, Bio-inspired Optimization, Routing, Stochastic, VANET, AOMDV, SVANET, EABCO-GAOMDV, Vehicle.

Full Text

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