Browsing by Author "Vatambeti, R."

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An Efficient Optimized Neural Network System for Intrusion Detection in Wireless Sensor Networks
(Modern Education and Computer Science Press, 2024) Sanshi, S.; Vatambeti, R.; Revathi, V.; Rahman, S.Z.
In the realm of wireless network security, the role of intrusion detection cannot be overstated in identifying and thwarting malicious activities within communication channels. Despite the existence of various intrusion detection system (IDS) approaches, challenges persist in terms of accurate classification and specification. Consequently, this article introduces a novel and innovative approach, the African Vulture-based Modular Neural System (AVbMNS), to address these issues. This research aims to detect and categorize malicious events in wireless networks effectively. The methodology begins with preprocessing the dataset and extracting relevant features. These extracted features are then subjected to a novel training technique to enhance the detection and classification of network attacks. The integration of African Vulture optimization significantly enhances the detection rate, leading to more precise attack identification. The research's effectiveness is demonstrated through validation using the NSL-KDD dataset, with impressive results. The performance analysis reveals that the developed model achieves a remarkable 99.87% detection rate and 99.92% accuracy when applied to the NSL-KDD dataset. Furthermore, the outcomes of this novel model are compared with existing approaches to gauge the extent of improvement. The comparative assessment affirms that the developed model outperforms its counterparts, underscoring its effectiveness in addressing the challenges of intrusion detection in wireless networks. © 2024, Modern Education and Computer Science Press. All rights reserved.
IoT energy efficiency routing protocol using FHO-based clustering and improved CSO model-based routing in MANET
(John Wiley and Sons Ltd, 2024) Sanshi, S.; Karthik, N.; Vatambeti, R.
Many protocols, services, and electrical devices with built-in sensors have been developed in response to the rapid expansion of the Internet of Things. Mobile ad hoc networks (MANETs) consist of a collection of autonomous mobile nodes that can form an ad hoc network in the absence of any pre-existing infrastructure. System performance may suffer due to the changeable topology of MANETs. Since most mobile hosts operate on limited battery power, energy consumption poses the biggest challenge for MANETs. Both network lifetime and throughput improve when energy usage is reduced. However, existing approaches perform poorly in terms of energy efficiency. Scalability becomes a significant issue in large-scale networks as they grow, leading to overhead associated with routing updates and maintenance that can become unmanageable. This article employs a MANET routing protocol combined with an energy conservation strategy. The clustering hierarchy is used in MANETs to maximize the network's lifespan, considering its limited energy resources. In the MANET communication process, the cluster head (CH) is selected using Fire Hawk Optimization (FHO). When choosing nodes to act as a cluster for an extended period, CH election factors in connectivity, mobility, and remaining energy. This process is achieved using an optimized version of the Ad hoc On-Demand Distance Vector (AODV) routing protocol, utilizing Improved Chicken Swarm Optimization (ICSO). In comparison to existing protocols and optimization techniques, the proposed method offers an extended network lifespan ranging from 90 to 160 h and reduced energy consumption of 80 to 110 J, as indicated by the implementation results. © 2024 John Wiley & Sons Ltd.