End-end Delay Research Articles

A secure routing and black hole attack detection system using coot Chimp Optimization Algorithm-based Deep Q Network in MANET

A Mobile Ad hoc Network (MANET) is a widely used and vibrant network, which is unevenly distributed in the environment. It is a set of self-organized independent mobile nodes interconnected without any centralized infrastructure. However, this topology nature makes the network prompt to various network security attacks. To address this issue, this paper proposes a Coot Chimp Optimization Algorithm- Deep Q-Network (CChOA-DQN) for detecting the black hole attacks in MANET. Here, the designed CChOA is used for the identification of the optimal route in the MANET for transmitting data, which takes into fitness parameters, such as energy, distance, neighbourhood quality, link quality, and trust. The features are extracted using the Fisher score and augmented using the over-sampling technique, which is further allowed for the detection process using DQN. Also, the weights of the DQN are enhanced using the CChOA algorithmic technique to enhance the detection performance. Additionally, the results gathered from the experiment revealed that CChOA attained high performance with a maximum of 0.983 Mbps throughput, 93.70 % Packet Delivery Ratio (PDR), and minimum end-end delay of 0.096Sec, Residual energy of 0.119 J, and Control overhead of 4473.11. Also, the CChOA-DQN technique achieved the minimum False Positive Rate (FPR) of 0.122, False Negative Rate (FNR) of 0.121, Computation time of 0.153 and Run time of 0.094.

Self configuring mobile agent-based intrusion detection using hybrid optimized with Deep LSTM

Sensor nodes can be deployed in harsh or hostile environments in many applications, making these nodes more prone to failure. The illegal movement monitoring within the sensor networks is a most challenging problem. The mobile malicious nodes are preferred by the attacker to maximize his impact. For a dynamic environment, a promising technology of sensor networks is expected to Intrusion detection. Multi-mobile agents utilize many approaches, after verification that collects data from sensor nodes. However, these approaches are inefficient to verify all the sensor nodes (SNs) of the network, due to its high delay, energy consumption, and mobility. The proposed Dunnock Ibis optimization LSTM model (DIO opt LSTM) solves this problem. Here, the sensor nodes are grouped into clusters; hence, mobile agent performs verification only the cluster heads instead of verifying all the SNs. The proposed DIO optimization combines the unique behavior of Egret Swam and Ibis optimization algorithm which efficiently tunes the LSTM classifier, resulting in the model providing better convergence. The simulation results show the proposed system shows a better result than the existing system by utilizing the database IDS 2018 Intrusion CSVs, the analysis is done based on performance metrics such as End-end-delay (ED), normalized energy (NE), and throughput. At 200 nodes and 1500 rounds, the DIO opt LSTM method has efficiently performed 146 numbers of alive nodes, 0.46 ms of delay, 0.15 J of normalized energy, and 0.89 bps of throughput.

Business simulation analysis based on leadership development with crisis management using gaming techniques and machine learning model

The reality of the modern, fast-paced business world is crisis. Instability in today’s business environment is brought on by rapid organisational change, shifting economic situations, people issues, unanticipated technological advancements, and political ramifications. The state is unable to control this instability, which manifests as economic disruptions leading to crises. In addition to harming those who have lost their employment, the crisis that is impacting our nation and every other nation in the world also affects others who are worried about losing their work. The aim of this research is to develop business intelligence simulation model with their leadership enhancement and crisis management using gaming model based on cloud network. Here the crisis management with leadership analysis is carried out using variational recurrent Boltzmann learning model (VRBL). the experimental analysis based on cloud network modelling in terms of execution time, network performance, overall complexity, data optimization, end-end delay. ML offers a more accurate and efficient prediction method than standard econometric models, making it more useful. In terms of prediction performance, the final fusion model performs better than the voting and averaging techniques.

An efficient self-attention-based conditional variational auto-encoder generative adversarial networks based multipath cross-layer design routing paradigm for MANET

Battery life is often a helpful resource for portable devices in MANET (Mobile Ad Hoc Network). When creating control methods, energy efficiency with network lifespan is considered. However, creating a routing method that is energy-efficient requires taking into account a variety of nodes from various layers, including channel assumptions, total traffic load, and remaining energy. The traditional layered technique cannot tackle power-relevant problems that may affect every stack layers. In this manuscript, Self-Attention-Based Conditional Variational Auto-Encoder Generative Adversarial Networks is proposed for enabling cross-layer routing over Mobile Ad Hoc Network (SACVAEGAN-MCLD-MANET). This Self-Attention-Based Conditional Variational Auto-Encoder Generative Adversarial Networks (SACVAEGAN) choose the optimum path by deeming given objective functions: residual energy cost of communication, Data Success Rate (DSR), mobility. In which, energy, DSR, mobility are computed through network layer. MAC layer's hesitating fuzzy linguistic bi-objective clustering technique (HFLBC) reduces the energy consumption brought on by contentions. The nodes with lesser residual energy must complete the immediate data transfer, while the path with greater contention must wait. As a result, this HFLBC is used to reduce energy use and enhance data transmission. Adaptability is produced using the knowledge that is shared among the different layers. The proposed method is done in Network Simulator-2.34, its efficacy is analyzed under certain performance metrics, like energy consumption, Packet Delivery Ratio, end-end delay, routing overhead, network lifetime, PLR, throughput. The proposed approach provides 23.21%, 15.64%, 19.96% higher packet delivery ratio and 16.63%, 17.29%, 6.47% lower delay compared with existing methods, like Sustainable Multiple path Routing for Enhancing Cross-Layer Presentation utilizing Energy Centric Tunicate Swarm Approach in MANET (ECTSA-MCLD-MANET), Cross-Layer adaptive data scheduling policy in mobile ad-hoc networks (ADSP-MCLD-MANET) and Cross-Layer Design Based N-ary Huffman Coding for DSDV Routing Protocol Analysis in mobile ad-hoc networks (NAHC-MCLD-MANET) respectively.

The measurement and monitoring of Quality of service based on security analysis in wireless sensor network using deep learning architecture

Wireless sensor networks comprise of unattended little sensor hubs having low energy and low scope of correspondence. Machine learning has demonstrated its effectiveness in creating proficient cycles to deal with complex issues in different organization perspectives. This exploration propose novel procedure in wireless sensor network routing and security improvement by network observing utilizing profound learning model. The organization routing is done utilizing cluster based scalable hierarchical energy routing protocol (CSHERP) and the organization checking is completed utilizing fuzzy Boltzmann adversarial learning (FBAL). This routing protocol adopts an information driven strategy, with moderate hubs collecting information and sending it to a sink hub. The arrangement gave is valuable to additional investigation of expanded WSN networks. The trial examination is completed as far as energy efficiency, throughput, end-end delay, latency, QoS. The proposed technique attained energy efficiency of 96%, throughput of 91%, end-end delay of 86%, latency of 85%, QoS of 96%.

Energy Analysis-Based Cyber Attack Detection by IoT with Artificial Intelligence in a Sustainable Smart City

Cybersecurity continues to be a major issue for all industries engaged in digital activity given the cyclical surge in security incidents. Since more Internet of Things (IoT) devices are being used in homes, offices, transportation, healthcare, and other venues, malicious attacks are happening more frequently. Since distance between IoT as well as fog devices is closer than distance between IoT devices as well as the cloud, attacks can be quickly detected by integrating fog computing into IoT. Due to the vast amount of data produced by IoT devices, ML is commonly employed for attack detection. This research proposes novel technique in cybersecurity-based network traffic analysis and malicious attack detection using IoT artificial intelligence techniques for a sustainable smart city. A traffic analysis has been carried out using a kernel quadratic vector discriminant machine which enhances the data transmission by reducing network traffic. This enhances energy efficiency with reduced traffic. Then, the malicious attack detection is carried out using adversarial Bayesian belief networks. The experimental analysis has been carried out in terms of throughput, data traffic analysis, end-end delay, packet delivery ratio, energy efficiency, and QoS. The proposed technique attained a throughput of 98%, data traffic analysis of 74%, end-end delay of 45%, packet delivery ratio of 92%, energy efficiency of 92%, and QoS of 79%.

Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning (ML) models. Due to attackers’ (and/or benign equivalents’) dynamic behavior changes, testing data distribution frequently diverges from original training data over time, resulting in substantial model failures. Due to their dispersed and dynamic nature, distributed denial-of-service attacks pose a danger to cybersecurity, resulting in attacks with serious consequences for users and businesses. This paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service (DDOS) in the network. The goal of this architecture combination is to accurately represent data and create an effective cyber security prediction agent. The intrusion detection system and concept drift of the network has been analyzed using secure adaptive windowing with website data authentication protocol (SAW_WDA). The network has been analyzed by authentication protocol to avoid malware attacks. The data of network users will be collected and classified using multilayer perceptron gradient decision tree (MLPGDT) classifiers. Based on the classification output, the decision for the detection of attackers and authorized users will be identified. The experimental results show output based on intrusion detection and concept drift analysis systems in terms of throughput, end-end delay, network security, network concept drift, and results based on classification with regard to accuracy, memory, and precision and F-1 score.

Enhanced algorithmic modelling and architecture in deep reinforcement learning based on wireless communication Fintech technology

Financial technology” or “FinTech” refers to use of information technologies to derive financial solutions. FinTech is now widely regarded as a hotly debated blend of financial services and information technology. A combination of AI and IoT approaches will significantly increase the extraction of valuable financial data as well as provide better services to customers. This research proposed a novel protocol in data transmission using fuzzy rule-based secure transmission with data optimization technique using deep reinforcement neural network by wireless communication. This technique for data transmission will reduce the credit risk and enhances the data optimization by fuzzy rule-based protocol with DRNN. The logic utilized is fuzzy logic, which is a multi-valued logic with truth values for variables ranging from 0 to 1. When truth values range from entirely true to completely false, it is used. The end user's searching experience is improved by fuzzy logic-based semantic search, which finds and retrieves exact matching files for relevant search files provided by user. When exact matches aren't possible, method leverages semantic similarities to determine most relevant matches. A grading method is utilized to minimize number of false positives. For this, proposed technique was used, which may decide on a collection of suitable storage servers on which the data must be saved and resulting in a reduction in execution time while maintaining a higher level of security. The experimental results show the execution time of 54 %, network performance of 96 %, the overall complexity of 71 %, data optimization of 95 % and end-end delay of 67 %.

Multipath Routing in Cloud Computing using Fuzzy based Multi-Objective Optimization System in Autonomous Networks

Intelligent houses and buildings, autonomous automobiles, drones, robots, and other items that are successfully incorporated into daily life are examples of autonomous systems and the Internet of Things (IoT) that have advanced as research areas. Secured data transfer in untrusted cloud applications has been one of the most significant requirements in the cloud in recent times. In order to safeguard user data from unauthorised users, encrypted data is stored on cloud servers. Existing techniques offer either security or efficiency for data transformation. They fail to retain complete security while undergoing significant changes. This research proposes novel technique in multipath routing based energy optimization of autonomous networks. The main goal of this research is to enhance the secure data transmission in cloud computing with network energy optimization. The secure data transmission is carried out using multi-authentication attribute based encryption with multipath routing protocol. Then the network energy has been optimized using multi-objective fuzzy based reinforcement learning. The experimental analysis has been carried out based on secure data transmission and energy optimization of the network. The parameters analysed in terms of scalability of 79%, QoS of 75%, encryption time of 42%, latency of 96%, energy efficiency of 98%, end-end delay of 45%.

Cyber-Attack Detection in Autonomous Vehicle Networks by Energy Aware Optimal Data Transmission with Game Fuzzy Q-Learning based Heuristic Routing Protocol

The automotive sector has seen a dramatic transition due to rapid technological advancement. Network connection has improved, enabling the transfer of the cars' technologies from being fully machine- to software-controlled. Controller area network (CAN) bus protocol manages network for autonomous vehicles. However, due to the intricacy of data and traffic patterns that facilitate unauthorised access to a can bus and many sorts of assaults, the autonomous vehicle network still has security flaws as well as vulnerabilities. This research proposes novel technique in cyber attack detection in autonomous vehicle networks enhanced data transmission based optimization and routing technique. Here the autonomous vehicle network optimal data transmission has been carried out using energy aware lagrangian multipliers based optimal data transmission. The cyber attack detection has been carried out using fuzzy q-learning based heuristic routing protocol. The experimental results has been carried out based on optimal data transmission and attack detection in terms of throughput of 95%, PDR of 94%, End-end delay of 46%, energy efficiency of 96%, network lifetime of 95%, attack detection rate of 88%.

Energy Harvesting based Mobile Cloud Network in Latency and QoS Improvement using 5G Systems by Energy Routing Optimization

D2D communication technology enables the User Equipment (UE) in 5G networks to instantly connect with other UEs, with or without partial infrastructure involvement. In a Cloud Assisted energy harvesting system, it has improved user numbers and data transmission between mobile nodes. This research propose energy harvesting for mobile cloud computing in enhancing the QoS and latency of the network. The main aim of this research is to enhance energy optimization using discrete energy efficient offloading algorithm. The routing has been optimized using fuzzy logic cognitive Bellman-Ford routing algorithm. To identify the failing node and find an alternative node to deliver the seamless services, an unique weight-based approach has been presented. The method relies on two working node parameters: execution time and failure rate. Threshold values are specified for the parameters of the chosen master node. By contrasting the values with the threshold values, the alternative node is chosen. The experimental results shows comparative analysis in terms of throughput of 96%, QoS of 96%, latency of 28%, energy consumption of 51%, end-end delay of 41%, average power consumption of 41% and PDR of 85%

A Comparision of Node Detection Algorithms Over Wireless Sensor Network

MANET is standing for Network as Mobile Ad-hoc which is a self-directed mobile handlers group which communicates over relatively bandwidth constrained wireless channels. Many services with different classes of Quality of Services (QoS) could be provided through the MANET such as data, voice, and video streaming. Thus, efficient packets routing is an essential issue especially over this kind of burst channel. To settle this issue, many scheduling techniques are proposed to reduce the packets dropping and channel collision when a huge demand of data is transferred from a sender to a receiver. In this paper, four MANET scheduling algorithms are selected and investigated in mobile ad hoc network which are Strict Preference (SP), Round Robin (RR), Weighted Round Robin (WRR), and Weighted Fair (WF). The network simulator EXata 2.0.1 is used to build the scenario which is consist of 50 nodes and performed the simulation. The results showed the performance metrics difference of the network such as the throughput and the end-end delay as well as queuing metrics like peak queue size, average queue length, in queue average time, and droppe of whole packets. Regrading throughput, the SP algorithm has a greater throughput than WF, RR, and WRR by 4.5%, 2.4%, and 1.42%, but WRR has outperformed others regarding the end-end delay. Moreover, WRR represents the best scheduling algorithm regarding both peak queue size since its greater than RP, WF, and WRR by 10.13%, 9.6%, and 5.32%, in order, and average output queue length in contrast, WRR worsts more time in queuing but it is the best in preventing the packets from dropping.

Gray-Hole Attack Minimization in IoMT with 5G Based D2D Networks

Reliable transmission is vital to the success of the next generation of communications technologies and Fifth Generation (5G) networks. Many sensitive applications, such as eHealth and medical services, can benefit from a 5G network. The Internet of Medical Things (IoMT) is a new field that fosters the maintenance of trust among various IoMT Device to Device (D2D) modern technologies. In IoMT the medical devices have to be connected through a wireless network and constantly needs to be self-configured to provide consistent and efficient data transmission. The medical devices need to be connected with sophisticated protocols and architecture to handle the synergy of the monitoring devices. Today, one of the commonly used algorithms in D2D communication is the Optimized Link State Routing protocol (OLSR). The OLSR is considerably good at effectively utilizing the bandwidth and reserving the paths. One of the major attack against the OLSR is the Node isolation attack, also known as the Gray hole denial of service attack. The Gray hole attack exploits the vulnerabilities present with sharing the topological information of the network. The attackers may use this topological information to maliciously disconnect the target nodes from the existing network and stops rendering the communication services to the victim node. Hence, considering the sensitivity and security concerns of the data used in e-Health applications, these types of attacks must be detected and disabled proactively. In this work, a novel Node Authentication (NA) with OLSR is proposed. The simulation experiments illustrated that the proposed protocol has an excellent Packet Delivery Ratio, minimal End-End delay, and minimal Packet loss when compared to the Ad-hoc On-Demand Distance Victor (AODV) protocol and the proposed authentication scheme was able to protect the OLSR protocol from a node isolation attack.

Grid-Based Routing Model for Energy Efficient and Secure Data Transmission in WSN for Smart Building Applications

Presently, due to the establishment of a sensor network, residual buildings in urban areas are being converted into smart buildings. Many sensors are deployed in various buildings to perform different functions, such as water quality monitoring and temperature monitoring. However, the major concern faced in smart building Wireless Sensor Networks (WSNs) is energy depletion and security threats. Many researchers have attempted to solve these issues by various authors in different applications of WSNs. However, limited research has been conducted on smart buildings. Thus, the present research is focused on designing an energy-efficient and secure routing protocol for smart building WSNs. The process in the proposed framework is carried out in two stages. The first stage is the design of the optimal routing protocol based on the grid-clustering approach. In the grid-based model, a grid organizer was selected based on the sailfish optimization algorithm. Subsequently, a fuzzy expert system is used to select the relay node to reach the shortest path for data transmission. The second stage involves designing a trust model for secure data transmission using the two-fish algorithm. A simulation study of the proposed framework was conducted to evaluate its performance. Some metrics, such as the packet delivery ratio, end-end delay, and average residual energy, were calculated for the proposed model. The average residual energy for the proposed framework was 96%, which demonstrates the effectiveness of the proposed routing design.

Trust-Based Secure Network For Detection Of Attacks (Wormhole And Black Hole) Due To Malicious Nodes In Ad Hoc Wireless Sensor Network

Recent researches about in alternate points of view and industrialized situated applications have comprehensively used Wireless Sensor Network (WSN). At present, WSN have achieved the authority consideration for giving the protected systems, which is free from the assailants and vindictive hubs. The remote sensor systems security can be liable to dangers by assaulters. It requires the sensor to sporadically detect and transmit touchy information to the base station for information transmitting generally through multi bounce way. As, it is exceptionally fundamental to plan the safe Ad Hoc system to identify the savvy assailants productively. Here in this paper we configuration trust based secure system for discovery of assaults (wormhole and dark opening) because of the nearness of noxious hubs (TSDAMN) plan of Ad Hoc remote sensor organize. For the upgrade of system security Heterogeneous Cluster Based Secure (HCBS) directing convention has been utilized. The test results indicates security enhanced system with the parameters like throughput, limited end-end delay, parcel transmission rate, vitality preservation rate, assault discovery rate.

Energy Efficient MAC with VariableDuty Cycle for Wireless Sensor Networks

ABSTRACT WSN consists of many sensors, they need high throughput and network lifetime to get involved in communication. Duty Cycle is a key factor in sensor nodes to maintain sleep, active condition in the presence of overhearing, collisions of packets while data transmission between sensor nodes. The key challenge of sensor nodes is to attain high energy efficiency, throughput and low End-End Delay. This paper presents several of existing approaches for MAC protocol. They are (1) Synchronization between nodes (2) Mathematical Analysis (3) TDMA based MAC and (4) Cross-Layer Design. We proposed a new method for MAC protocol with variable duty cycle. Neighboring nodes follow same duty cycle and same active time. Far away nodes doesn’t maintain same duty cycle, hence their active time is not same. The duty cycle is maintained in synchronous way and adaptive manner among the nodes within the network. In this paper, a conventional MAC and proposed MAC are simulated using NS-2.35. Variable duty cycle approach in the proposed MAC with sleep and idle states for the nodes yields better throughput and energy efficiency with respect to other conventional technique. These results validate that the proposed variable DC-MAC is feasible for Wireless Sensor Network applications.

Harvested Energy Scavenging and Transfer capabilities in Opportunistic Ring Routing

Energy conservation has always been a prominent design goal for hierarchical routing protocols supporting sink mobility. Advertising the current position of mobile sink introduces control packet overhead which ultimately results in an increase in energy consumption and shorter network lifetime. Energy harvesting through ambient sources have enabled the utilization of rechargeable devices for Wireless Sensor Networks to perpetually remain operational. The modifications in the hierarchical structure of wireless sensor networks along with energy scavenging approaches could possibly minimize the control packet overhead and also provide a significant improvement in energy conservation. In this paper, we propose a novel Harvested Energy Scavenging and Transfer capabilities in Opportunistic Ring Routing protocol which uses a distinguishing approach of hybrid (ring + cluster) topology in which the network architecture is initially supported by the formation of a virtual ring structure and then a two-tier routing topology is used in the virtual ring as an overlay by grouping nodes into clusters. The rate of energy gain from solar harvesting and radio frequency transfer is the criterion for selecting cluster heads. The role of cluster heads is exploited to advertise the mobile sink current position as well as forward the aggregated data towards mobile sink using energy transfer based opportunistic routing. The simulation results reveal that our scheme considerably outperforms the existing benchmarks in terms of control packet overhead, energy conservation, network lifetime, packet delivery ratio and average end-end delay.

WITHDRAWN: Hybrid cryptographic algorithm based key management scheme in MANET

Abstract In the recent networking era, Mobile Adhoc Network has been popular as it effectively solves the limitations that were pertaining with single path routers. Multi-path mobile adhoc networks (MANETs) have various advantages which include high throughput, lower time complexity and high energy efficiency. The major challenges here are the key management and secured way of routing. Secure routing ensures data integrity, confidentiality, authorization and authentication and non-repudiation. These requirements are satisfied only by cryptographic algorithms. Key management is the most essential one in cryptography. The management of keys which is integrated with the protocol start with trusted and network with more authentication. In order to ensure the secured broadcasting in MANET, here we proposed a secured multipath routing mechanism for reliable data transmission using cryptographic algorithms. In our proposed method, we are hybridizing the popular Diffie-Hellman method of key exchange and elliptic curve cryptography (HDHECC). First, the nodes are subjected to clustering using the LEACH protocol and the cluster head selection process will be carried out with the help of weight of each node. Here the trust mechanism will be done by using the AOMDV. For the management of keys, every head of the cluster is made to retain its public key of the corresponding member nodes and are made to act only as a router. The proposed technique solves the overhead of the centralized management of keys. Moreover, the requirement to store all the public keys. The propose method is tested for its performance by evaluating the end-end delay, Packet delivery ratio and packet losses. The implementation is carried out using the NS2 simulator.

DASH based video caching in MEC-assisted heterogeneous networks

Recently, the demand for video applications has become more and more pressing. To save backhaul bandwidth and reduce the network delay, Mobile Edge Computing (MEC) has arisen as a promising technique in future mobile networks. It can provide the storage, computing and networking resources in close-proximity to the mobile users through deploying multiple MEC servers at mobile edge. However, how to effectively utilize the limited storage capacity of these MEC servers is still the major challenge, especially when Dynamic Adaptive Streaming over HTTP (DASH) is used for video transmission. Recent studies have shown that layered encoding is very suitable for streaming service using DASH standard. Therefore, in our paper, combining with the feature of DASH-based scalable video transmission, we first design a novel multi-server cooperative scheme with software-defined networking (SDN) architecture for future MEC-assisted heterogeneous networks, to effectively manage and schedule the available resources, thus guaranteeing the requested video delivered to the clients with higher quality and lower delay. And then, based on the above scheme, the end-end delay is carefully analyzed for each real video streaming. With a full consideration of the averag end-to-end delay of video streaming and the provided video quality of MEC servers, we finally propose a Dynamic Programming based Adaptive Caching Algorithm (DPACA) to obtain an optimized caching strategy under the constraint of cache storage capacity at each MEC sever. Simulation results show that, our algorithm can improve the cache hit ratio while significantly reducing the end-to-end delay. Moreover, our algorithm has higher flexibility and adaptability.

Efficient Dual-Cooperative Bait Detection Scheme for Collaborative Attackers on Mobile Ad-Hoc Networks

Security and correspondence happening between network central point will be an instance for principal issues in Mobile Ad-hoc Networks (MANETs). Due to some ideas created by the organization leading to avoid attacks but may end in failure due to inappropriate way and thus attacks need recognized and cleared. The Dual-Cooperative Bait Detection Scheme (D-CBDS) is one of the ways that is in the stake for the discovery of MANET-dark/dim opening assailants. The current CBDS calculation consolidates the intensity of proactive and responsive security advancements to characterize lure mode assailants as proactive and receptive engineering. In CBDS, an adjacent source node is randomly selected as a bait target for searching. By reverse tracking as a reactive method, the attackers are identified. However, in some time, the chosen bait destination node may be an intruder that is not handled in the current CBDS approach. This paper therefore reinforces the CBDS with the dual mode of selecting two nearby nodes as two bait destinations. Dual reverse tracking enables effective collaborative assailants in MANET. Finally, when we analyze D-CBDS with respect to Routing overhead, End-End delay and throughput it gives much productivity than other methods like DSR, CBDS.