Pub Date : 2024-02-26DOI: 10.22247/ijcna/2024/224433
Sampada H. K., S. K R
– Mobile Ad Hoc Network (MANET) devices are powered from battery and due to infrastructure-less feature, the security and energy consumption are major concerns. Most of the researchers have assumed that the Cluster Head (CH) nodes are benign and frequently undergo cluster re-election, which shortens the network lifetime. Smart & Secure Ad Hoc On-Demand Distance Vector algorithm (S 2 -AODV) is proposed with secondary CH (S-CH), primary CH (P-CH) and a super cluster head (SCH) node along with the other nodes. Modified-AODV (M-AODV) is used for neighbor discovery. Weight-based clustering algorithm is proposed, with the primary and a secondary CH node to enhance the network efficiency. S 2 -AODV enhances security using Honey-pot AODV (H-AODV) and avoids the CH re-election process enhancing the overall network lifetime. The proposed algorithm works in off-line mode and on-line mode. In off-line mode the various Wi-Fi parameters like Received Signal Strength Indicator (RSSI), transmission power, battery level, distance and number of transmissions retries are collected from each CH node in the network. A look-up table indicating the transmission power (TXP) to be set by the CH nodes is determined by machine learning (ML) algorithms. This table is circulated among every CH node by SCH node in the network. Due to this process the intermittent reelection of the P-CH and S-CH nodes can be avoided, enhancing the network lifetime. In on-line mode, SCH executes H-AODV to identify and remove the malicious CH (black hole / gray hole) nodes (ns-2.34).
- 移动 Ad Hoc 网络（MANET）设备由电池供电，由于其无基础设施的特点，安全性和能源消耗成为主要问题。大多数研究人员都假设簇头（CH）节点是良性的，并经常进行簇重选，从而缩短了网络寿命。智能安全 Ad Hoc 按需距离矢量算法（S 2 -AODV）被提出来，该算法包含二级 CH（S-CH）、一级 CH（P-CH）和一个超级簇头（SCH）节点以及其他节点。修改后的 AODV（M-AODV）用于发现邻居。提出了基于权重的聚类算法，通过主 CH 节点和副 CH 节点来提高网络效率。S 2 -AODV利用蜜罐AODV（H-AODV）增强了安全性，并避免了CH重选过程，从而提高了整体网络寿命。所提出的算法可在离线模式和在线模式下工作。在离线模式下，从网络中的每个 CH 节点收集各种 Wi-Fi 参数，如接收信号强度指示器（RSSI）、传输功率、电池电量、距离和传输重试次数。通过机器学习（ML）算法确定一个查找表，显示 CH 节点要设置的传输功率（TXP）。该表通过网络中的 SCH 节点在每个 CH 节点之间分发。通过这一过程，可以避免间歇性地重新选择 P-CH 和 S-CH 节点，从而提高网络寿命。在联机模式下，SCH 会执行 H-AODV 以识别并清除恶意 CH 节点（黑洞/灰洞）（ns-2.34）。
{"title":"Co-Ordinated Blackhole and Grayhole Attack Detection Using Smart & Secure Ad Hoc On-Demand Distance Vector Routing Protocol in MANETs","authors":"Sampada H. K., S. K R","doi":"10.22247/ijcna/2024/224433","DOIUrl":"https://doi.org/10.22247/ijcna/2024/224433","url":null,"abstract":"– Mobile Ad Hoc Network (MANET) devices are powered from battery and due to infrastructure-less feature, the security and energy consumption are major concerns. Most of the researchers have assumed that the Cluster Head (CH) nodes are benign and frequently undergo cluster re-election, which shortens the network lifetime. Smart & Secure Ad Hoc On-Demand Distance Vector algorithm (S 2 -AODV) is proposed with secondary CH (S-CH), primary CH (P-CH) and a super cluster head (SCH) node along with the other nodes. Modified-AODV (M-AODV) is used for neighbor discovery. Weight-based clustering algorithm is proposed, with the primary and a secondary CH node to enhance the network efficiency. S 2 -AODV enhances security using Honey-pot AODV (H-AODV) and avoids the CH re-election process enhancing the overall network lifetime. The proposed algorithm works in off-line mode and on-line mode. In off-line mode the various Wi-Fi parameters like Received Signal Strength Indicator (RSSI), transmission power, battery level, distance and number of transmissions retries are collected from each CH node in the network. A look-up table indicating the transmission power (TXP) to be set by the CH nodes is determined by machine learning (ML) algorithms. This table is circulated among every CH node by SCH node in the network. Due to this process the intermittent reelection of the P-CH and S-CH nodes can be avoided, enhancing the network lifetime. In on-line mode, SCH executes H-AODV to identify and remove the malicious CH (black hole / gray hole) nodes (ns-2.34).","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":"154 S316","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140428730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-26DOI: 10.22247/ijcna/2024/224434
S. Nandhini, K. S. Jeen Marseline
– Flying Ad-Hoc Networks (FANETs) have gained prominence in various applications, ranging from surveillance to disaster response. Their dynamic and resource-constrained nature makes efficient energy utilization a paramount concern. One significant challenge in FANETs is minimizing energy consumption, which is essential for prolonging the network lifetime and ensuring continuous operation. This paper introduces the Resilient Artificial Bee Colony Optimized AODV Routing Protocol (RABCO-AODV-RP) to address this challenge. RABCO-AODV-RP leverages the Artificial Bee Colony optimization algorithm to enhance AODV routing, optimizing route selection to minimize energy consumption while maintaining network resilience. The working mechanism of RABCO-AODV-RP encompasses two primary phases: route discovery and route maintenance. During route discovery, the protocol intelligently selects energy-efficient paths using the optimization algorithm, reducing energy waste. In the route maintenance phase, RABCO-AODV-RP continuously adapts to network dynamics, updating routes to ensure efficient and resilient communication. Extensive simulations were conducted using the NS3 network simulator to assess its performance using packet delivery ratio, packet drop ratio, throughput, end-to-end delay, energy consumption and hop count as performance metrics. The results and discussions indicate that RABCO-AODV-RP outperforms traditional AODV routing protocol. It improves packet delivery, throughput and reduces packet drop ratio, end-to-end delay and hop count. This research underscores the potential of RABCO-AODV-RP as a promising solution for extending the operational lifetime of FANETs and ensuring reliable communication in demanding environments.
{"title":"Resilient Artificial Bee Colony Optimized AODV Routing Protocol (RABCO-AODV-RP) for Minimizing the Energy Consumption in Flying Ad-Hoc Network","authors":"S. Nandhini, K. S. Jeen Marseline","doi":"10.22247/ijcna/2024/224434","DOIUrl":"https://doi.org/10.22247/ijcna/2024/224434","url":null,"abstract":"– Flying Ad-Hoc Networks (FANETs) have gained prominence in various applications, ranging from surveillance to disaster response. Their dynamic and resource-constrained nature makes efficient energy utilization a paramount concern. One significant challenge in FANETs is minimizing energy consumption, which is essential for prolonging the network lifetime and ensuring continuous operation. This paper introduces the Resilient Artificial Bee Colony Optimized AODV Routing Protocol (RABCO-AODV-RP) to address this challenge. RABCO-AODV-RP leverages the Artificial Bee Colony optimization algorithm to enhance AODV routing, optimizing route selection to minimize energy consumption while maintaining network resilience. The working mechanism of RABCO-AODV-RP encompasses two primary phases: route discovery and route maintenance. During route discovery, the protocol intelligently selects energy-efficient paths using the optimization algorithm, reducing energy waste. In the route maintenance phase, RABCO-AODV-RP continuously adapts to network dynamics, updating routes to ensure efficient and resilient communication. Extensive simulations were conducted using the NS3 network simulator to assess its performance using packet delivery ratio, packet drop ratio, throughput, end-to-end delay, energy consumption and hop count as performance metrics. The results and discussions indicate that RABCO-AODV-RP outperforms traditional AODV routing protocol. It improves packet delivery, throughput and reduces packet drop ratio, end-to-end delay and hop count. This research underscores the potential of RABCO-AODV-RP as a promising solution for extending the operational lifetime of FANETs and ensuring reliable communication in demanding environments.","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":"173 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140428809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-26DOI: 10.22247/ijcna/2024/224437
V. M. Niaz Ahamed, K. Sivaraman
– Wireless communication between moving cars and stationary structures is made possible by Vehicular Ad Hoc Networks (VANETs). The goal is to communicate traffic data so that accidents can be avoided and resources can be used most effectively in current traffic conditions. There are several methods for enhancing VANETs' communicative efficacy; one is clustering in-vehicle networks. One CH assigned to each cluster and is in charge of the cluster as a whole. The CHs are responsible for all communications, both those between clusters and those within a single cluster. Vehicles in this study are organized into groups called clusters and information is relayed from one CH to another. Several different routing algorithms may be used to send data from one vehicle to another to improve the network's performance as a whole. Many reliable and safe routing systems for VANETs have been presented in the past decade. These protocols have several drawbacks, including their complexity, inability to scale to extensive networks, increased transportation costs, etc. Several bio-inspired strategies for optimal packet routing among vehicle nodes have been proposed to overcome these restrictions. Hence, this paper presents the efficient optimization of vehicular ad hoc networks assisted by a clustering nodular [EO-CN] framework to solve the abovementioned issues. The proposed method drastically reduced network overhead in settings with varying densities of nodes. Numerous experiments were conducted with various parameters, including cluster size, network area, node density, and transmission distance. These findings demonstrated that [EO-CN] performed better than competing approaches.
- 通过车载 Ad Hoc 网络（VANET），行驶中的汽车和固定建筑物之间可以进行无线通信。其目标是通信交通数据，从而避免事故，并在当前交通条件下最有效地利用资源。有几种方法可以提高 VANET 的通信效率，其中一种是对车载网络进行聚类。每个集群分配一个 CH，负责整个集群。CH负责所有通信，包括集群之间的通信和单个集群内部的通信。本研究中的车辆被组织成称为簇的群组，信息从一个 CH 中转到另一个 CH。可以使用几种不同的路由算法将数据从一辆车发送到另一辆车，以提高网络的整体性能。在过去的十年中，出现了许多可靠、安全的 VANET 路由系统。这些协议有几个缺点，包括复杂性、无法扩展到广泛的网络、运输成本增加等。为了克服这些限制，人们提出了几种生物启发策略来优化车辆节点间的数据包路由。因此，本文提出了由聚类节点[EO-CN]框架辅助的车载 ad hoc 网络高效优化方法，以解决上述问题。在节点密度不同的情况下，所提出的方法大大降低了网络开销。实验中使用了各种参数，包括簇大小、网络区域、节点密度和传输距离。这些研究结果表明，[EO-CN] 的性能优于其他竞争方法。
{"title":"Vehicular Ad Hoc Networks Assisted Clustering Nodular Framework for Optimal Packet Routing and Scaling","authors":"V. M. Niaz Ahamed, K. Sivaraman","doi":"10.22247/ijcna/2024/224437","DOIUrl":"https://doi.org/10.22247/ijcna/2024/224437","url":null,"abstract":"– Wireless communication between moving cars and stationary structures is made possible by Vehicular Ad Hoc Networks (VANETs). The goal is to communicate traffic data so that accidents can be avoided and resources can be used most effectively in current traffic conditions. There are several methods for enhancing VANETs' communicative efficacy; one is clustering in-vehicle networks. One CH assigned to each cluster and is in charge of the cluster as a whole. The CHs are responsible for all communications, both those between clusters and those within a single cluster. Vehicles in this study are organized into groups called clusters and information is relayed from one CH to another. Several different routing algorithms may be used to send data from one vehicle to another to improve the network's performance as a whole. Many reliable and safe routing systems for VANETs have been presented in the past decade. These protocols have several drawbacks, including their complexity, inability to scale to extensive networks, increased transportation costs, etc. Several bio-inspired strategies for optimal packet routing among vehicle nodes have been proposed to overcome these restrictions. Hence, this paper presents the efficient optimization of vehicular ad hoc networks assisted by a clustering nodular [EO-CN] framework to solve the abovementioned issues. The proposed method drastically reduced network overhead in settings with varying densities of nodes. Numerous experiments were conducted with various parameters, including cluster size, network area, node density, and transmission distance. These findings demonstrated that [EO-CN] performed better than competing approaches.","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140429881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-26DOI: 10.22247/ijcna/2024/224438
Jeya Rani D., Nagarajan Munusamy
– Wireless Sensor Networks (WSNs) have been more popular for a wide range of applications due to research ability to monitor and gather data from a variety of situations. However, it remains challenging to achieve Quality of Service (QoS) while maintaining energy efficiency. In the context of QoS optimization for energy-efficient WSNs, this study investigates the crucial issues of localization and deployment concerns. Localization the precise positions of sensor nodes are crucial for effective data fusion and routing algorithms that rely on localization. This study compares and contrasts several localization methods, including range-based and range-free approaches, and explains benefits and drawbacks. The study also investigates the effects on QoS and energy savings of various deployment strategies, including optimizing node location, boosting coverage, and increasing node density. The goal of this research is to find out how to optimize QoS in low-power wireless networks by including latency, throughput, and stability, among other quality of service characteristics, into the design of routing algorithms. Current routing protocols, like Low-Energy Adaptive Clustering Hierarchy (LEACH), are assessed for ability to optimize quality of service while minimizing energy consumption. In addition, this study explores several approaches that might help enhance QoS while reducing energy consumption, such as energy-aware routing, adaptive duty cycling, and data aggregation methods. By thoroughly examining and evaluating localization algorithms, deployment concerns, and routing protocols, this study offers practical and theoretical insights for researchers and practitioners aiming to optimize quality of service in energy-efficient WSNs. Useful and dependable WSN deployments in a wide variety of domains possible with the help of the presented results and suggestions.
{"title":"Localization and Deployment Considerations into Quality of Service Optimization for Energy-Efficient Wireless Sensor Networks","authors":"Jeya Rani D., Nagarajan Munusamy","doi":"10.22247/ijcna/2024/224438","DOIUrl":"https://doi.org/10.22247/ijcna/2024/224438","url":null,"abstract":"– Wireless Sensor Networks (WSNs) have been more popular for a wide range of applications due to research ability to monitor and gather data from a variety of situations. However, it remains challenging to achieve Quality of Service (QoS) while maintaining energy efficiency. In the context of QoS optimization for energy-efficient WSNs, this study investigates the crucial issues of localization and deployment concerns. Localization the precise positions of sensor nodes are crucial for effective data fusion and routing algorithms that rely on localization. This study compares and contrasts several localization methods, including range-based and range-free approaches, and explains benefits and drawbacks. The study also investigates the effects on QoS and energy savings of various deployment strategies, including optimizing node location, boosting coverage, and increasing node density. The goal of this research is to find out how to optimize QoS in low-power wireless networks by including latency, throughput, and stability, among other quality of service characteristics, into the design of routing algorithms. Current routing protocols, like Low-Energy Adaptive Clustering Hierarchy (LEACH), are assessed for ability to optimize quality of service while minimizing energy consumption. In addition, this study explores several approaches that might help enhance QoS while reducing energy consumption, such as energy-aware routing, adaptive duty cycling, and data aggregation methods. By thoroughly examining and evaluating localization algorithms, deployment concerns, and routing protocols, this study offers practical and theoretical insights for researchers and practitioners aiming to optimize quality of service in energy-efficient WSNs. Useful and dependable WSN deployments in a wide variety of domains possible with the help of the presented results and suggestions.","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":"37 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140431696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-26DOI: 10.22247/ijcna/2024/224431
Aristide Ndayikengurukiye, Abderrahmane Ez-Zahout, F. Omary
– In a cloud computing environment, good resource management remains a major challenge for its good operation. Implementing virtual machine placement (VMP) on physical machines helps to achieve various objectives, such as resource allocation, load balancing, energy consumption, and quality of service. VMP (virtual machine placement) in the cloud is critical, so it's important to audit its implementation. It must take into account the resources of the physical server, including CPU, RAM, and storage. In this paper, a metaheuristic algorithm based on the Grey Wolf Optimization (GWO) method is used to optimize the placement of virtual machines in a cloud environment, effectively minimizing the number of active virtual machines used to host virtual servers. Experimental results demonstrate the effectiveness of the proposed method, called Grey Wolf Optimization for Virtual Machine Placement (GWOVMP). The method reduces power consumption by 20.99 and resource wastage by 1.80 compared with existing algorithms.
{"title":"Optimizing Virtual Machines Placement in a Heterogeneous Cloud Data Center System","authors":"Aristide Ndayikengurukiye, Abderrahmane Ez-Zahout, F. Omary","doi":"10.22247/ijcna/2024/224431","DOIUrl":"https://doi.org/10.22247/ijcna/2024/224431","url":null,"abstract":"– In a cloud computing environment, good resource management remains a major challenge for its good operation. Implementing virtual machine placement (VMP) on physical machines helps to achieve various objectives, such as resource allocation, load balancing, energy consumption, and quality of service. VMP (virtual machine placement) in the cloud is critical, so it's important to audit its implementation. It must take into account the resources of the physical server, including CPU, RAM, and storage. In this paper, a metaheuristic algorithm based on the Grey Wolf Optimization (GWO) method is used to optimize the placement of virtual machines in a cloud environment, effectively minimizing the number of active virtual machines used to host virtual servers. Experimental results demonstrate the effectiveness of the proposed method, called Grey Wolf Optimization for Virtual Machine Placement (GWOVMP). The method reduces power consumption by 20.99 and resource wastage by 1.80 compared with existing algorithms.","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":"50 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140431349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-26DOI: 10.22247/ijcna/2024/224436
J. A. Rathod, Manjunath Kotari
– Several practical applications are combined in a new paradigm known as 5G-based mobile ad hoc networks (MANET) with cloud. Numerous existing works perform trust assessment, intrusion detection, and route discovery to improve secure data transmission in MANET. Route maintenance was not carried out in several of the existing works, and the absence of enumerating link status and node reliability during route maintenance results in link failure and increases packet loss. By considering the existing issues, a novel Kangaroo-based intrusion detection system was proposed to eliminate malicious nodes from the network using Bidirectional-Long Short-Term Memory (Bi-LSTM). This increases data transmission security. For graphical user authentication, encryption based on ASCII values of the Reflection tree (E-ART algorithm) is employed. In this paper, a divide well merge algorithm was implemented, which is a better approach for hierarchical clustering. This method consists of two phases: a Division and Merging phase. The effective route identification and route maintenance in MANET are implemented by using an Advanced Ad-hoc On-demand Distance Vector Protocol (Advanced AODV), which discovers the route using the Fire Hawk Optimization Algorithm (FHO) to obtain optimal multipath by contemplating trust, node connectivity, throughput, node degree, bandwidth, energy and distance where this protocol offers loop-free operation and enhance its scalability to numerous numbers of terminals. In this way, route discovery and route maintenance are established to enhance secure data transmission, thereby reducing packet loss. The modified blockchain called TriChain is proposed for enhancing data transmission security. For the Proof of Work based on Reputation (PoWR) consensus algorithm is used to reduce transaction confirmation latency and block creation time thereby increasing security. In this way, route discovery and route maintenance are established to enhance secure data transmission thereby reducing packet loss. The proposed work is evaluated using detection rate, energy consumption, packet delivery rate, throughput, authentication rate and delay.
{"title":"TriChain: Kangaroo-Based Intrusion Detection for Secure Multipath Route Discovery and Route Maintenance in MANET Using Advanced Routing Protocol","authors":"J. A. Rathod, Manjunath Kotari","doi":"10.22247/ijcna/2024/224436","DOIUrl":"https://doi.org/10.22247/ijcna/2024/224436","url":null,"abstract":"– Several practical applications are combined in a new paradigm known as 5G-based mobile ad hoc networks (MANET) with cloud. Numerous existing works perform trust assessment, intrusion detection, and route discovery to improve secure data transmission in MANET. Route maintenance was not carried out in several of the existing works, and the absence of enumerating link status and node reliability during route maintenance results in link failure and increases packet loss. By considering the existing issues, a novel Kangaroo-based intrusion detection system was proposed to eliminate malicious nodes from the network using Bidirectional-Long Short-Term Memory (Bi-LSTM). This increases data transmission security. For graphical user authentication, encryption based on ASCII values of the Reflection tree (E-ART algorithm) is employed. In this paper, a divide well merge algorithm was implemented, which is a better approach for hierarchical clustering. This method consists of two phases: a Division and Merging phase. The effective route identification and route maintenance in MANET are implemented by using an Advanced Ad-hoc On-demand Distance Vector Protocol (Advanced AODV), which discovers the route using the Fire Hawk Optimization Algorithm (FHO) to obtain optimal multipath by contemplating trust, node connectivity, throughput, node degree, bandwidth, energy and distance where this protocol offers loop-free operation and enhance its scalability to numerous numbers of terminals. In this way, route discovery and route maintenance are established to enhance secure data transmission, thereby reducing packet loss. The modified blockchain called TriChain is proposed for enhancing data transmission security. For the Proof of Work based on Reputation (PoWR) consensus algorithm is used to reduce transaction confirmation latency and block creation time thereby increasing security. In this way, route discovery and route maintenance are established to enhance secure data transmission thereby reducing packet loss. The proposed work is evaluated using detection rate, energy consumption, packet delivery rate, throughput, authentication rate and delay.","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":"42 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140429568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-26DOI: 10.22247/ijcna/2024/224435
M. Sasikumar, K. Rohini
– Mobile Ad hoc networks (MANETs) represent dynamic, self-configuring network environments that provide flexible connectivity but are highly susceptible to security threats. Intrusion detection systems in MANETs need to continuously monitor network traffic for potential intrusions and anomalies. This constant monitoring can be energy-intensive, requiring network nodes to process, analyze, and transmit data. Excessive energy consumption by IDS can deplete node batteries quickly, leading to network disruptions. This research focuses on developing and evaluating an efficient IDS proposed for MANETs called Robust Dragonfly-Optimized Naive Bayes (RDO-ENB). RDO-ENB operates by fusing the simplicity and efficiency of the Enhanced Naive Bayes algorithm with the adaptive capabilities of robust Dragonfly Optimization. This synergy enables RDO-ENB to continuously and dynamically adjust its internal parameters, optimizing its intrusion detection performance in real time. It enhances accuracy and reduces false positives, making it proficient in identifying and mitigating intrusions within the complex and ever-evolving environment of MANETs. The dataset employed for evaluation is NSL-KDD, a widely used dataset for intrusion detection. The results of the IDS implementation demonstrate its proficiency in accurately identifying and mitigating intrusions while minimizing false positives and conserving valuable energy resources.
- 移动 Ad hoc 网络（MANET）是一种动态的、可自我配置的网络环境，可提供灵活的连接，但极易受到安全威胁。城域网中的入侵检测系统需要持续监控网络流量，以发现潜在的入侵和异常情况。这种持续监控可能是能源密集型的，需要网络节点处理、分析和传输数据。IDS 的能耗过高会迅速耗尽节点电池，导致网络中断。本研究的重点是开发和评估一种适用于城域网的高效 IDS，名为 "强力蜻蜓优化奈何贝叶斯"（RDO-ENB）。RDO-ENB 融合了增强型 Naive Bayes 算法的简单性和高效性，以及稳健蜻蜓优化的自适应能力。这种协同作用使 RDO-ENB 能够持续、动态地调整其内部参数，实时优化其入侵检测性能。它提高了准确性，减少了误报，使其能够在复杂且不断变化的城域网环境中熟练地识别和缓解入侵。用于评估的数据集是 NSL-KDD，这是一个广泛用于入侵检测的数据集。入侵检测系统的实施结果表明，该系统能够准确识别和缓解入侵，同时最大限度地减少误报，节约宝贵的能源资源。
{"title":"Expedient Intrusion Detection System in MANET Using Robust Dragonfly-Optimized Enhanced Naive Bayes (RDO-ENB)","authors":"M. Sasikumar, K. Rohini","doi":"10.22247/ijcna/2024/224435","DOIUrl":"https://doi.org/10.22247/ijcna/2024/224435","url":null,"abstract":"– Mobile Ad hoc networks (MANETs) represent dynamic, self-configuring network environments that provide flexible connectivity but are highly susceptible to security threats. Intrusion detection systems in MANETs need to continuously monitor network traffic for potential intrusions and anomalies. This constant monitoring can be energy-intensive, requiring network nodes to process, analyze, and transmit data. Excessive energy consumption by IDS can deplete node batteries quickly, leading to network disruptions. This research focuses on developing and evaluating an efficient IDS proposed for MANETs called Robust Dragonfly-Optimized Naive Bayes (RDO-ENB). RDO-ENB operates by fusing the simplicity and efficiency of the Enhanced Naive Bayes algorithm with the adaptive capabilities of robust Dragonfly Optimization. This synergy enables RDO-ENB to continuously and dynamically adjust its internal parameters, optimizing its intrusion detection performance in real time. It enhances accuracy and reduces false positives, making it proficient in identifying and mitigating intrusions within the complex and ever-evolving environment of MANETs. The dataset employed for evaluation is NSL-KDD, a widely used dataset for intrusion detection. The results of the IDS implementation demonstrate its proficiency in accurately identifying and mitigating intrusions while minimizing false positives and conserving valuable energy resources.","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":"23 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140429864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-30DOI: 10.22247/ijcna/2023/223693
S. N, Rajesh. A, K. S. Archana
{"title":"ANFIS-RSOA Approach for Detecting and Preventing Network Layer Attacks in MANET","authors":"S. N, Rajesh. A, K. S. Archana","doi":"10.22247/ijcna/2023/223693","DOIUrl":"https://doi.org/10.22247/ijcna/2023/223693","url":null,"abstract":"","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":" 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139140030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-30DOI: 10.22247/ijcna/2023/223684
Omar M. Mohamed, Tarek M. Mahmoud, Abdelmgeid A. Ali
{"title":"Assessing a Real-time Adaptive Traffic Route Based on Ranking Software Defined Networking (SDN) Cluster of Controllers in a Datacenter","authors":"Omar M. Mohamed, Tarek M. Mahmoud, Abdelmgeid A. Ali","doi":"10.22247/ijcna/2023/223684","DOIUrl":"https://doi.org/10.22247/ijcna/2023/223684","url":null,"abstract":"","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139138707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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