Genuine Nodes Research Articles

The Guardian Node Slow DoS Detection Model for Real-Time Application in IoT Networks.

The pernicious impact of malicious Slow DoS (Denial of Service) attacks on the application layer and web-based Open Systems Interconnection model services like Hypertext Transfer Protocol (HTTP) has given impetus to a range of novel detection strategies, many of which use machine learning (ML) for computationally intensive full packet capture and post-event processing. In contrast, existing detection mechanisms, such as those found in various approaches including ML, artificial intelligence, and neural networks neither facilitate real-time detection nor consider the computational overhead within resource-constrained Internet of Things (IoT) networks. Slow DoS attacks are notoriously difficult to reliably identify, as they masquerade as legitimate application layer traffic, often resembling nodes with slow or intermittent connectivity. This means they often evade detection mechanisms because they appear as genuine node activity, which increases the likelihood of mistakenly being granted access by intrusion-detection systems. The original contribution of this paper is an innovative Guardian Node (GN) Slow DoS detection model, which analyses the two key network attributes of packet length and packet delta time in real time within a live IoT network. By designing the GN to operate within a narrow window of packet length and delta time values, accurate detection of all three main Slow DoS variants is achieved, even under the stealthiest malicious attack conditions. A unique feature of the GN model is its ability to reliably discriminate Slow DoS attack traffic from both genuine and slow nodes experiencing high latency or poor connectivity. A rigorous critical evaluation has consistently validated high, real-time detection accuracies of more than 98% for the GN model across a range of demanding traffic profiles. This performance is analogous to existing ML approaches, whilst being significantly more resource efficient, with computational and storage overheads being over 96% lower than full packet capture techniques, so it represents a very attractive alternative for deployment in resource-scarce IoT environments.

IoT-based external attacks aware secure healthcare framework using blockchain and SB-RNN-NVS-FU techniques.

In recent times, there has been widespread deployment of Internet of Things (IoT) applications, particularly in the healthcare sector, where computations involving user-specific data are carried out on cloud servers. However, the network nodes in IoT healthcare are vulnerable to an increased level of security threats. This paper introduces a secure Electronic Health Record (EHR) framework with a focus on IoT. Initially, the IoT sensor nodes are designated as registered patients and undergo initialization. Subsequently, a trust evaluation is conducted, and the clustering of trusted nodes is achieved through the application of Tasmanian Devil Optimization (STD-TDO) utilizing the Student's T-Distribution. Utilizing the Transposition Cipher-Squared random number generator-based-Elliptic Curve Cryptography (TCS-ECC), the clustered nodes encrypt four types of sensed patient data. The resulting encrypted data undergoes hashing and is subsequently added to the blockchain. This configuration functions as a network, actively monitored to detect any external attacks. To accomplish this, a feature reputation score is calculated for the network's features. This score is then input into the Swish Beta activated-Recurrent Neural Network (SB-RNN) model to classify potential attacks. The latest transactions on the blockchain are scrutinized using the Neutrosophic Vague Set Fuzzy (NVS-Fu) algorithm to identify any double-spending attacks on non-compromised nodes. Finally, genuine nodes are granted permission to decrypt medical records. In the experimental analysis, the performance of the proposed methods was compared to existing models. The results demonstrated that the suggested approach significantly increased the security level to 98%, reduced attack detection time to 1300 ms, and maximized accuracy to 98%. Furthermore, a comprehensive comparative analysis affirmed the reliability of the proposed model across all metrics. The proposed healthcare framework's efficiency is proved by the experimental evaluation.

Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN

Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection.

Reputation-Based Opportunistic Routing Protocol Using Q-Learning For Manet Attacked By Malicious Nodes: A Survey

Mobile Ad-hoc Networks (MANETs) have emerged as a viable paradigm for creating dynamic communication between mobile nodes in the absence of a permanent infrastructure. However, since MANETs are inherently decentralized and self-organizing, they are vulnerable to a variety of security concerns, especially when hostile nodes seek to interrupt communication and undermine network operations. As a consequence, developing efficient and robust routing protocols is critical to ensuring dependable and secure data transmission in MANETs. This review study gives an in-depth look into Reputation-Based Opportunistic Routing Protocols (RORPs), which use Q-Learning methods to improve MANET resistance against malicious node assaults. We provide a thorough study of current RORPs, delving into their core design ideas, underlying processes, and comparative performance evaluations. We begin with a comprehensive introduction to the fundamental concepts, issues, and security risks associated with MANETs. Following that, we explore the concepts of reputation-based routing, emphasizing the need of reputation management in discriminating between malicious and genuine nodes. The review then goes into further detail on the incorporation of Q-Learning methods into RORPs, emphasizing on how reinforcement learning mechanisms may adaptively change routing choices depending on the developing network state and the dynamic reputations of participating nodes. This article reviews 38 research papers of opportunistic routing protocol and explores the potential of computer-assisted methods for MANET. We next divide the studied RORPs into categories depending on their approach to reputation computation, learning algorithms, and cooperative enforcement methods. Furthermore, we analyze their strengths and weaknesses in terms of resilience to different sorts of attacks, as well as their influence on network performance measures including packet delivery ratio, end-to-end latency, and throughput.

SWEEPER: Secure Waterfall Energy-Efficient Protocol-Enabled Routing in FANET

The recent advancements in ad-hoc networks have resulted in innovations like FANETs. FANETs (Flying Ad hoc Networks) have become diversified in its applications ranging from agriculture, military, emergency services etc. The FANET environment needs to be dynamic and is populated by UAVs. The highly mobile UAVs are responsible for data transmission between nodes. If unchecked, this results in packet loss. Reliable data transmission in FANETs is possible, if effective routing protocols are in place. In this research, a Secure Waterfall Energy-Efficient Protocol-Enabled Routing (SWEEPER) has been proposed which helps conserve the node energy throughout the transmission process. The framework uses a waterfall model approach which includes group key management as an underlying process governing the security aspect of the protocol. Asymmetric key cryptography is applied to our technique, which involves two unique nodes, labeled the Computed Key (CKey) and the Dissemination Key (DKey). The two nodes will generate, verify, and distribute the secret keys. This will help other nodes concentrate on transmission alone and needn’t waste time in computational activities or key handling. Security breaches and malignant nodes are also handled efficiently. The nodes along the route are selected based on a trust factor, which allows our protocol to select only genuine nodes to forward packets along the discovered path. Our work is analyzed with existing protocols of FANETS like SecRIP and MDRMA. The analysis reveals that our protocol outperforms the existing protocols in terms of minimal delay, maximum energy conservation and PDR, which contributes towards a maximized throughput.

Consensus-based secure and efficient compressive sensing in a wireless network sensors environment

Compressive sensing provides the optimal paradigm for data aggregation approach in wireless sensor networks as it holds the characteristics of low traffic cost and avoids the large communication overhead through a traditional approach. However, security is still considered one of the important, in recent years consensus-based approach has been one of the security highlights in compressive sensing. Moreover, a consensus is developed only with the help of genuine nodes. In this research work, a novel integrated-consensus-based secure compressive sensing (CSCS) is proposed which aims to develop a secure consensus environment to secure the network. Integrated-CSCS comprises several modules; at first certain variables are assigned to each node participating in the network. Later an algorithm is developed where each node evaluates the assigned neighbor variable and updates its states. Moreover, this phenomenon leads to the identification of genuine and compromised nodes. The proposed model is evaluated by considering the efficiency and security parameters; efficiency evaluation is carried out through energy consumption, and several node failures where as security is evaluated by considering the ability to identify the genuine node and malicious or corrupt node.

Bipolar fuzzy information‐based PROMETHEE‐based outranking scheme for mitigating vampire attack in wireless sensor networks

AbstractThe sensor nodes comprising sensing devices, small processing units, and battery, cooperate in wireless sensor networks (WSNs) for gathering potential factors that aids in achieving significant decision‐making processes in deployed applications. WSNs are vulnerable to security threats due to their open nature, availability characteristics, and lack of energy, resulting in a diversified number of security attacks. Among the security attacks, vampire attack is the serious threat as they drain the energy of nodes, which in turn crumbles the network lifetime and energy stability in WSNs. In this article, Bi‐Polar Information‐Based Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE)‐based outranking scheme is proposed for detecting and isolating vampire attacks to improve throughput with minimized energy consumptions and delay for prolonging lifetime. This proposed BPFI‐POR scheme used bipolar information that facilitates an asymmetrical association between two decision making factors that influences the discrimination and selection of genuine nodes from vampire nodes during the routing process. It aids in characterizing precise and vague information through bipolar fuzzy linguistic terms that has the maximized probability of being represented as trapezoidal bipolar fuzzy numbers. It estimated the cooperation of intermediate sensor nodes' based on trapezoidal bipolar fuzzy numbers for determining their preference or selection using criteria that could be influenced during the routing process. It also used the ranking function for accessing the alternatives through crisp real preferences with entropy weighting information responsible for attributes weight computation. The simulation results of the BPFI‐POR scheme confirmed better throughput, detection time with minimized energy consumptions and latency on par with the competitive algorithms.

Novel Link Establishment Communication Scheme against Selfish Attack Using Node Reward with Trust Level Evaluation Algorithm in MANET

Mobile network nodes are trustworthy nodes; the data access in transmitting and receiving the data packet is not efficient in different network procedures. The malicious nodes are available in routing path making the packet loss, in packet transmission for time instance. Since the regular mobile network must not verify every mobile node approved else illegal, subsequently the reserved level of nodes also was not considered, to injure the packet transmission procedure by link failure called connection loss. It minimizes the transmission rate and network lifetime and improves packet latency and energy usage. The proposed novel link establishment communication (NLEC) technique is used to find the dependable routing path against intruder node available in the network. This scheme selects genuine node for routing path production, by using the node reward with dependence level estimating algorithm to compute every node trust level and resource range, to disconnect higher trust level node and lower trust level node; higher trust level node is a genuine node which performs secure communication. Lower trust level node is selfish node and they are detected and ignored from path. It increases the lifespan of network and throughput and minimizes the end to end delay and energy consumption.

Detection and Avoidance of Clone Attack in IoT Based Smart Health Application

The deployment of wireless sensors in the hostile environment makes them susceptible to malicious attacks. One of the most harmful attacks is the clone attack in which a malicious node illegitimately claims the identity of a genuine node in the network and eventually tries to capture the entire network. This attack is also termed as node replication attack. The mobile nature of wireless sensor network (WSN) in smart health environment increases the vulnerability of node replication attack. Since the data involved in smart health system are highly sensitive data, preserving the system from the attack by malicious nodes is a crucial task. In this research, we proposed a new scheme called Routing Protocol for Energy Efficient Networks (RPEEN) for the detection of clone attack in IoT-based smart health application. The main advantage of this scheme is the increase in the energy efficiency as the energy efficiency is the most important constraint in WSN systems. The performance of the proposed scheme is highlighted using parameters like time delay, residual energy, throughput, energy efficiency and error rate. Further, to portray the efficacy of the proposed algorithm, this algorithm is compared with the existing Hybrid Multi-Level Clustering (HMLC) algorithm. It has been found that the proposed RPEEN scheme achieves a time delay of 0.63 and 0.6 ms with 0 dead nodes and by avoiding clone attack respectively. Furthermore, the proposed scheme attains the highest residual energy of 49.5 J for the 2500 rounds. In addition, the proposed algorithm attains the highest throughput of 99.2% for the 50 nodes.

FSM based Intrusion Detection of Packet Dropping Attack using Trustworthy Watchdog Nodes

Introduction: * The proposed TWIST model aims to achieve a secure MANET by detecting and mitigating packet dropping attack using finite state machine based IDS model. * To determine the trust values of the nodes using context-aware trust calculation * To select the trustworthy nodes as watchdog nodes for performing intrusion detection on the network * To detect and isolate the packet dropping attackers from routing activities, the scheme uses FSM based IDS for differen-tiating the packet dropping attacks from genuine nodes in the MANET. Method: In this methodology, instead of launching an intrusion detection system (IDS) in all nodes, an FSM based IDS is placed in the trustworthy watchdog nodes for detecting packet dropping attacker nodes in the network. The proposed FSM based intrusion detection scheme has three steps. The three main steps in the proposed scheme are context- aware trust calculation, watchdog node selection, and FSM based intrusion detection. In the first process, the trust calculation for each node is based on specific parameters that are different for malicious nodes and normal nodes. The second step is the watchdog node selection based on context-aware trust value calculation for ensuring that the trust-worthy network monitors are used for detecting attacker nodes in the network. The final process is FSM based intrusion detection, where the nodes acquire each state based on their behavior during the data routing. Based on the node behavior, the state transition occurs, and the nodes that drop data packets exceeding the defined threshold are moved to the malicious state and restricted to involve in further routing and services in the network Result: The performance of the proposed (TWIST) mechanism is assessed using the Network Simulator 2 (NS2). The proposed TWIST model is implemented by modifying the Ad-Hoc On-Demand Distance Vector (AODV) protocol files in NS2. Moreover, the proposed scheme is compared with Detection and Defense against Packet Drop attack in the MANET (DDPD) scheme. A performance analysis is done for the proposed TWIST model using performance metrics such as detection accuracy, false-positive rate, and overhead and the performance result is compared with that of the DDPD scheme. After the compare result we have analyzed that the proposed TWIST model exhibits better performance in terms of detection accuracy, false positive rate, energy consumption, and overhead compared to the existing DDPD scheme. Conclusion: In the TWIST model, an efficient packet dropping detection scheme based on the FSM model is proposed that efficiently detects the packet dropping attackers in the MANET. The trust is evaluated for each node in the network, and the nodes with the highest trust value are selected as watchdog nodes. The trust calculation based on parameters such as residual energy, the interaction between nodes and the neighbor count is considered for determining watchdog node selec-tion. Thus, the malicious nodes that drop data packets during data forwarding cannot be selected as watchdog nodes. The FSM based intrusion detection is applied in the watchdog nodes for detecting attackers accurately by monitoring the neigh-bor nodes for malicious behavior. The performance analysis is performed between the proposed TWIST mechanism and existing DDPD scheme. The proposed TWIST model exhibits better performance in terms of detection accuracy, false positive rate, energy consumption, and overhead compared to the existing DDPD scheme Discussion: This work may extend the conventional trust measurement of MANET routing, which adopts only routing behavior observation to cope with malicious activity. In addition, performance evaluation of proposed work under packet dropping attack has not been performed for varying the mobility of nodes in terms of speed. Furthermore, various perfor-mance metric parameters like route discovery latency and malicious discovery ratio which can be added for evaluate the performance of protocol in presence of malicious nodes. This may be considered in future work for extension of protocol for better and efficient results. Furthermore, In the future, the scheme will focus on providing proactive detection of packet dropping attacker nodes in MANET using a suitable and efficient statistical method.

Enhanced MANET security using artificial immune system based danger theory to detect selfish nodes

The dynamic distributed topology of a Mobile Ad Hoc Network (MANET) provides many challenges including decentralized infrastructure wherein each node acts as both the host and router for transiting traffic. MANET nodes provide the transmission capability to receive, transmit and route traffic from a sender node to the destination node. In this paper, we design an artificial immune system-based security method for MANET by simulating the mechanism of the human immune system. This paper presents a new bio inspired algorithm namely Artificial Immune System Based Algorithm (AISBA) to select selfish nodes in MANET, which utilizes principles of Artificial Immune Systems (AIS) that in turn mimics the strategy of the Human Immune System. Unlike Combined Immune Theories Algorithm, AISBA does not utilize a learning stage. In AISBA two different Trust models are developed as well to distinguish between a genuine node as well as a selfish node. An average detection rate of 93.41% was achieved with this bio inspired approach. The Simulation results demonstrating the superiority of the proposed scheme compared to the well known secured AODV (SAODV) protocol in terms of packet delivery ratio (about 87% of packet delivery ratio is achieved compared to about 37% in the case of SAODV) and about 94% of detection rate for the proposed scheme compared to 86% for SAODV in the presence of misbehaved nodes. The statistical analysis of the false positive probability is also conducted to assess the efficiency of the obtained results, showing promising results with respect to the effect of weight and trust on threshold.

Seek-and-destroy algorithm for optimal resource allocation and security analysis in software-defined vehicular networks

PurposeSoftware-define vehicular networks (SDVN) assure the direct programmability for controlling the vehicles with improved accuracy and flexibility. In this research, the resource allocation strategy is focused on which the seek-and-destroy algorithm is implemented in the controller in such a way that an effective allocation of the resources is done based on the multi-objective function.Design/methodology/approachThe purpose of this study is focuses on the resource allocation algorithm for the SDVN with the security analysis to analyse the effect of the attacks in the network. The genuine nodes in the network are granted access to the communication in the network, for which the factors such as trust, throughput, delay and packet delivery ratio are used and the algorithm used is Seek-and-Destroy optimization. Moreover, the optimal resource allocation is done using the same optimization in such a way that the network lifetime is extended.FindingsThe security analysis is undergoing in the research using the simulation of the attackers such as selective forwarding attacks, replay attacks, Sybil attacks and wormhole attacks that reveal that the replay attacks and the Sybil attacks are dangerous attacks and in future, there is a requirement for the security model, which ensures the protection against these attacks such that the network lifetime is extended for a prolonged communication. The achievement of the proposed method in the absence of the attacks is 84.8513% for the remaining nodal energy, 95.0535% for packet delivery ratio (PDR), 279.258 ms for transmission delay and 28.9572 kbps for throughput.Originality/valueThe seek-and-destroy algorithm is one of the swarm intelligence-based optimization designed based on the characteristics of the scroungers and defenders, which is completely novel in the area of optimizations. The diversification and intensification of the algorithm are perfectly balanced, leading to good convergence rates.

DETERMINATION OF THE ELECTRONIC ENERGY LEVELS OF A QUANTUM WELL HETEROSTRUTURE Zn(1-x) MgxO/ZnO/Zn1-xMgxO.

Nowadays, Cloud Computing is the preferred choice of IT organizations as it provides dynamic and pay-per-use based services to its clients. The main problem of the cloud computing is security and privacy of data which occurs because of its open and distributed structural design. Thus, it becomes essential to design an intrusion detection system (IDS) to provide security to the system. IDS help to protect cloud ecosystem from the malicious activities of the attacker. In this research work, IDS is designed to detect malicious node by using an optimization technique along with the concept of AI (Artificial Intelligence).The Cuckoo Search (CS) algorithm is used as an optimization technique and it is a meta-heuristic approach which is inspired by the behavior of birds and CS algorithm operates on the healthiness function. After the feature optimization, different types of feature are categorized based on the node’s nature into two types namely normal and attackers. On the basis of extracted features, train the proposed system using ANN (artificial neural network) as classifier to classify the attackers which affect the networks in cloud environment. ANN is a multiclass classifier which is used to solve multi-class problems and due to this reason ANN is used in proposed work with CS optimization algorithm. In this research work, ANN is used to distinguish between attacker nodes and genuine nodes based on their optimized feature sets. Thus, instead of passing data to the attacker node, the node passes the data to the genuine node and hence, the system is protected. By using the above motioned concept in cloud computing the possibility of results improvement become high because only genuine node involving in the data transmission process. To know the performance of the system, the QoS (Quality of service) parameters such as PDR (Packet delivery ratio), energy consumption rate and total delay with and without prevention algorithm are measured. The development of proposed ARS is done in the MATLAB 2016a software with the help of various toolboxes like data acquisition, artificial intelligence, optimization and curve fitting.

STRATEGIC ALLIANCE FOR BLOCKCHAIN GOVERNANCE GAME

This paper deals with design of an alternative secure Blockchain network framework to prevent damages from an attacker. The alliance concept from the strategic management perspectives is applied on the top of a general stochastic game framework. This new enhanced hybrid theoretical model is designed to find the best strategies toward preparation for preventing a network malfunction from an attacker through strategic alliances with other genuine nodes and it is developed based on the combination of a strategic management framework and a conventional stochastic model based on the Blockchain Governance Game. Analytically, tractable results for decision-making parameters are fully obtained to predict of the moment for operations and also to provide the optimal number of allegiance nodes to protect a Blockchain network. This research helps those whom are considering initial coin offering or launching new Blockchain-based services by enhancing security features through strategic alliances in a decentralized network.

A Blackhole Attack Mitigation Algorithm in MANET based on Standard Deviation Outlier Detection

A mobile ad hoc network (MANET) performs a routing and data forwarding obligations in a trustless environment where mobile nodes are not controlled and administered by third parties, it usually suffers from multiple attacks such as the blackhole attacks. This blackhole attacks cause two extreme effects on the network if the malicious nodes are not eliminated. Firstly, it deliberately alters the original data route sequence with an injection of false routing information comprises of the counterfeit destination sequence number and smallest hop count. Secondly, it becomes too greedy to send data to other nodes after receiving from a genuine node. Therefore, this may delay the data transmission process in the act of data dropping. To protect the routing discovery process of an ad hoc on-demand distance vector (AODV) protocol from the blackhole attack, we present a mitigation algorithm based on standard deviation outlier detection to determine a threshold value for validating the route reply (RREP) destination sequence number before route establishment. The simulation shows that the algorithm can detect the malicious nodes and performs better than AODV in a blackhole attack environment.

Spoofing – An Attack On Node Identity And Its Remedial Research

Network is the prime demand of today’s outreaching development in every sector. Starting from a small institute to big sectors like Industry, Organization, Defense, Ministry and Medicine all are dependent on network to maintain their data transmission efficiently. It provides the great ease of operation and information sharing. As we know with greater ease, much greater threat is handy. With the network also a big threat is associated i.e. network attacks. There are some major attacks which can violate and damage the whole network system and its integrity. Wireless attacks are very common and an intruder with vast knowledge of network administration and modification can easily find the loopholes in the security systems. There are different attacks like Denial of Services (DoS), Sniffer, Password Based, Spoofing, Eavesdropping etc. but the most dangerous of all is spoofing. Spoofing is an attack type in that the intruder impersonates as original identity and use their identity to achieve the intrusion goal. The attack is so severe because the user, even after seeing the intruder cannot identify it as the external body and take immediate action. The intruder enters the network and steals all the information as a legitimate user and damages the integrity of network. We propose here to find the intruder by using the energy consumption by nodes using the dead, asleep and active nodes. We can localize the location of intruders in the network using this technique. We use Received Signal Strength (RSS) to see the energy consumption of each node as the fluctuation of intruder node will be different from genuine nodes.

Secure Trust-Based Blockchain Architecture to Prevent Attacks in VANET.

Vehicular ad hoc networks (VANET) are also known as intelligent transportation systems. VANET ensures timely and accurate communications between vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) to improve road safety and enhance the efficiency of traffic flow. Due to its open wireless boundary and high mobility, VANET is vulnerable to malicious nodes that could gain access into the network and carry out serious medium access control (MAC) layer threats, such as denial of service (DoS) attacks, data modification attacks, impersonation attacks, Sybil attacks, and replay attacks. This could affect the network security and privacy, causing harm to the information exchange within the network by genuine nodes and increase fatal impacts on the road. Therefore, a novel secure trust-based architecture that utilizes blockchain technology has been proposed to increase security and privacy to mitigate the aforementioned MAC layer attacks. A series of experiment has been conducted using the Veins simulation tool to assess the performance of the proposed solution in the terms of packet delivery ratio (PDR), end-to-end delay, packet loss, transmission overhead, and computational cost.

Detection of Coverage Hole Nodes in Wireless Sensor Network using Artificial Intelligence

Adequate coverage of the sensing field in Wireless sensor networks (WSNs) is critical to many applications. However, when one or more sensor nodes stop working due to energy exhaustion or physical damage, the network may experience overlay vulnerability. This can disrupt network connectivity and hinder performance. Therefore, it must be fixed automatically. To resolve this problem, swarm inspired Artificial Bee Colony (ABC) scheme in addition to the Artificial Neural Network (ANN) approach is used. The aim of ABC is to optimize the shortest path by selecting an appropriate fitness function and then identify holes using ANN. Before the detection of holes, ANN is trained as per the optimized properties of nodes that are as per the genuine nodes and coverage hole repair properties. Therefore during the testing process, ANN compares these properties with the stored properties and then identify the hole repair node. From the experiment, it has been analyzed that the energy consumption up to 23.88% is saved.

Design, implementation and analysis of routing based attack model for delay tolerant networks for prophet routing protocol

Delay tolerant network (DTN) is an evolution of mobile adhoc network (MANET). In computing, network security is the main issue as the attacks by malicious and selfish nodes are increasing on the network day by day. This paper defines an attack model in which malicious nodes (in DTN with Prophet Routing Protocol) receive the data from genuine nodes. It is done by falsifying their routing information (Delivery Predictability). Later malicious nodes spoof their identity and receive messages as destination nodes. The Legitimate messages are deleted by malicious nodes and also from the source node buffer as the source node treats the message being delivered to the final destination. Delivery Probability, Overhead Ratio, Average Latency and Average Buffer time metrics are evaluated using ONE simulator. In simulated network, 126 nodes are considered and by making only 3% nodes malicious, performance of the network is decreased by approximately 40-50%.

A Framework to Enhance Security in Nemo Environment Using AAA Mechanisms

One of emerging trends in the mobile network era is Network Mobility (NEMO). It was standardized by the Internet Engineering Task Force (IETF) and gained attention of the researchers because of research opportunities that it provides. Though it was developed based on MIPv6, there are few spots that must be analyzed and rectified, especially in the security aspects. According to the literatures, NEMO lacks in providing a robust Authentication, Authorization and Accounting (AAA) services to its users. AAA operations must be performed for all the players of the mobile network, because a hacker may reside at any place and try to access the mobile network by hiding behind valid or genuine nodes’ addresses. This research work aims to provide an AAA framework for NEMO by comprising three different mechanisms which are developed for Local Mobile Node (LMN), Visiting Mobile Node (VMN) and Mobile Router (MR). Simulation and performance analysis are done.