Node Attacks Research Articles

A Novel Statistical Adhoc On-Demand Distance Vector Routing Protocol Technique is using for Preventing the Mobile Adhoc Network from Flooding Attack

The Mobile Adhoc Networks are more vulnerable because in the (MANET) Mobile Adhoc Network all node works as data sink, transmitter, router. There is no centralized system in the Mobile Adhoc network, so the chances of the vulnerabilities are very high in the network. There are various security issues in the Mobile Adhoc Network. From the various attacks the flooding attacks are most difficult attacks that extremely affect in Mobile Adhoc Network. In this paper, a new statistical based technique is planned, which is used to discover the flooding attack in an positive approach than other approaches. In the planned of Statistical Ad-Hoc on Demand Distance Vector (SAODV) approach is used to detect malicious nodes in the Mobile Adhoc Network. In this technique, statistical threshold value is obtained from mean and variance. In this approach the value is utilize to locate the (RREQ) Route Request flooding attacker nodes in the Mobile Adhoc Network. The proposed method is capable because threshold values are calculated on the source of RREQs prepared by every node in the Mobile Addhoc Network. The simulation results clearly depict that the proposed approach has significant performance in the terms of throughput, delay, packet delivery ratio, and overhead.

Detection for Nodes Intrusion of Compressed Transmission Data Based on Fuzzy Analytic Hierarchy Process

In order to overcome the problems of low clustering accuracy, low detection efficiency and low detection accuracy in the traditional intrusion detection method for compressed transmission data nodes. This paper proposes an intrusion detection method based on fuzzy analytic hierarchy process. Based on the first-order RF energy consumption model, this method calculates the optimal number of cluster heads, and combines GWO algorithm and FCM algorithm to divide the network into clusters to achieve the clustering of compressed transmission data nodes. On the basis of the characteristics of leaf node attack behavior, the fuzzy consistent judgment matrix is constructed to determine the weight of each security attribute of leaf node, calculate the probability of attack leaf node, and complete the intrusion detection of compressed transmission data node according to the calculation results. The experimental results show that the proposed method has high clustering accuracy, high detection efficiency and high detection accuracy. The shortest detection time is only 2.7 s, and the highest detection accuracy is 98.9%.

Jamming in Eavesdropping on Throughput Maximization in Green Cognitive Radio Networks

This work considers a cognitive radio (CR) network consisting of a set of CR transmit-receive node pairs, one fusion center (FC), multiple primary user emulation attack (PUEA) nodes, an eavesdropper ( <inline-formula><tex-math notation="LaTeX">$E_{av}$</tex-math></inline-formula> ) node and a set of friendly jammers used for protection of CR data transmission from eavesdropping. At the initial time slot of the frame, simultaneous energy harvesting (EH) and spectrum sensing are done through power splitting (PS) mode. CR transmit nodes then amplify and forward the sensed samples of both the primary user (PU) and the PUEA to the FC for cooperative spectrum sensing (CSS). Based on the CSS decision, CR transmit nodes either perform EH over the entire duration or make an opportunistic data transmission in time division mode. The closed form expressions of the optimal sensing duration, power allocation factor and transmit power for each secondary user (SU) are found. The sum secondary throughput of the network is maximized under the constraints of meeting the sensing reliability of the PU, individual energy causality for each SU and the best selected jammer, interference at the PU receiver, individual secondary and secrecy outage probability. Simulation results show a performance gain on the maximum value of the sum secondary throughput by <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 17.56 and <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 49.69 percent over the existing works.

Trust Model for IoT Using Cluster Analysis: A Centralized Approach

The emergence of Internet-of-Things (IoT) resulted in building an environment of intelligent applications and services activated by a multitude of sensors. These sensor nodes are installed in a transparent and remote environment which makes devices vulnerable to security threats, especially when there are malicious node attacks in those networks. Malicious nodes can initiate multiple attacks, based on methods of coercion, retransmission, and discard. This paper suggests a centralized model of trust based on clusteringto address the security risks and challenges associated withIoT. Trust evaluation is a significant factor in planning and assessing defence solutions. Many theoretical works have been carried out to suggest trust evaluation and prediction methods. The main objective of this paper is to develop a centralized trust model for IoT. Initially, a cluster is formed based on the node location. Then for each group, the master node is selected based on their direct and indirect trust value, which includes Quality of Service (QoS) and Social trust properties. The master node is periodically updated using node data trust value and each node cluster sensed information using a regression model-based clustering. The intra-cluster and inter-cluster analysis is utilized to find the data trust value of the node, as well as outlier data. An extensive simulation is performed to analyze the performance of the proposed work. The result indicates that the projected model achievesimproved in terms of various operational metrics.

Securing 6LoWPAN Neighbor Discovery

The IEEE 802.15.4-based 6LoWPAN is a de facto standard platform for a variety of Internet-of-Things applications. To bootstrap the low-power wireless personal area Network (LoWPAN), each device must perform the 6LoWPAN neighbor discovery (6LoWPAN-ND) address registration so that a unique IPv6 address can be allocated to each device. Without a suitable security mechanism, the 6LoWPAN-ND is vulnerable to various security attacks including the compromised node attack. Although a few security mechanisms have been proposed, they are not satisfactory as they depend on the IEEE 802.15.4 hop-by-hop security without specifying how to share the hop-by-hop keys and without considering the compromised node attack. As previous studies for securing 6LoWPAN-ND address registration have security weaknesses, a new security mechanism resilient to compromised node attack, and with a key management system for the IEEE 802.15.4 hop-by-hop security embedded in its design, is proposed and analyzed in this article. We also show that the proposed security mechanism is compatible with the Internet engineering task force (IETF) standard and is more efficient than the mechanism proposed in IETF 6lo WG.

Determination of the risk propagation path of cascading faults in chemical material networks based on complex networks

AbstractIn a chemical material network, a small fault is easy to spread and cause serious consequences, which is the embodiment of a complex network cascading fault. In order to ensure the safety and stability of chemical production, it is necessary to study the cascading fault propagation of a chemical material network. Firstly, the chemical material network model is constructed, and the SR algorithm is used to identify the important nodes in the network, which is the basis for selecting attack nodes. Secondly, the fault propagation strength is defined by the fault propagation probability and material hazard degree. The fault propagation probability is calculated by constructing the load cascading fault model of nodes. Based on the rough set theory and analytic hierarchy process (AHP), the index weights of flammability and explosiveness, toxicity, and corrosiveness are calculated to get the material hazard degree. Finally, based on the fault propagation strength, the risk propagation path of cascading fault is obtained by the ant colony algorithm. The case analysis shows that the proposed method can find the risk propagation path of cascading faults in material networks in the chemical production process, which provides certain basis for cascading fault prevention, selection of key monitoring variables, and risk assessment.

An efficient layer node attack strategy to dismantle large multiplex networks

Network dismantling aims to identify the minimum set of nodes whose removal breaks the network into components of sub-extensive size. The solution to this problem is significant for designing optimal strategies for immunization policies, information spreading, and network attack. Modern systems, such as social networks and critical infrastructure networks, which consist of nodes connected by links of multiple types can be encapsulated into the framework of multiplex networks. Here we focus on the dismantling problem in multiplex networks under layer node-based attack, and propose an efficient dismantling algorithm based on network decycling. Experiments on synthetic and real-world networks show that the proposed algorithm outperforms existing algorithms by a considerable margin. We also show how the robustness of a multiplex network is affected by the interlayer degree correlation. Our results shed light on the design of more resilient network systems and the effective destruction of harmful networks.

Qualitative Simulation Algorithm for Resource Scheduling in Enterprise Management Cloud Mode

Aiming at the problem of resource scheduling optimization in enterprise management cloud mode, a customizable fuzzy clustering cloud resource scheduling algorithm based on trust sensitivity is proposed. Firstly, on the one hand, a fuzzy clustering method is used to divide cloud resource scheduling into two aspects: cloud user resource scheduling and cloud task resource scheduling. On the other hand, a trust-sensitive mechanism is introduced into cloud task scheduling to prevent malicious node attacks or dishonest recommendation from node providers. At the same time, in the cloud task scheduling, cloud resources are divided according to the comprehensive performance of resources, and the trust sensitivity coefficient of each type of task resources is calculated. Then, according to the trust sensitivity coefficient, the matching cloud tasks are selected for users. Through the comparison of simulation experiments, the customized fuzzy clustering cloud resource scheduling algorithm proposed in this paper reduces the user’s cost of selecting cloud service provider in the cloud resource scheduling. It not only embodies the principle of cloud resource allocation on demand but also can give full play to the advantages of cloud resources and improve the throughput of the whole cloud system and the satisfaction of cloud users.

Trusted data collection for Internet of Things

SummaryTrusted date collection is the precondition of the security of Internet of Things (IOT). Recently, compressed sensing technology, which can enable the reduction of the energy consumption and time delay of data collection, has been extensively studied in IOT; However, the security of compressed sensing technology in IOT has not been sufficiently considered. Since compressed sensing depends the cooperation between IOT nodes and then sensing data is transparent for all IOT nodes, which is subjected to malicious nodes attack. Therefore, we propose a trusted data collection scheme based on compressed sensing oriented to the IOT. When data collection is tampered with, the scheme can detect malicious nodes. Experiments demonstrate that the proposed scheme can ensure data security with a low energy consumption.

A Secure Storage and Sharing Scheme of Stroke Electronic Medical Records Based on Consortium Blockchain.

The maintenance and sharing of electronic medical records are one of the essential tasks in the medical treatment combination. Traditional cloud-based electronic medical record storage system is difficult to realize data security sharing. The tamper resistance and traceability of blockchain technology provide the possibility for the sharing of highly sensitive medical data. This paper proposes a safe sharing scheme of stroke electronic medical records based on the consortium blockchain. The scheme adopts the storage method of ciphertext of medical records stored in the cloud and index of medical records stored on the blockchain. The privacy protection mechanism proposed in this paper innovatively combines proxy reencryption and searchable encryption which supports patient pseudoidentity search. The mechanism could achieve controllable sharing of medical records and precise search. According to the organizational characteristics of the stroke medical treatment combination, this paper proposes an improved Practical Byzantine Fault Tolerance mechanism to reach a consensus between consensus nodes. Then, the proposed scheme is analyzed and evaluated from three aspects of medical record integrity, user privacy, and data security. The results show that the scheme can not only ensure the privacy of patient identity information and private key data but also resist the tampering and deletion attacks of internal and external malicious nodes on the medical record data. Therefore, the proposed scheme is conducive to the improvement of the timeliness of stroke treatment and the safe sharing of electronic medical records in stroke medical treatment combination.

Intrusion detection in mobile ad-hoc network using Hybrid Reactive Search and Bat algorithm

PurposeThe mischievous nodes that defy the standard corrupt the exhibition of good nodes considerably. Therefore, an intrusion discovery mechanism should be included to the mobile ad-hoc network (MANET). In this paper, worm-hole and other destructive malignant attacks are propelled in MANET.Design/methodology/approachA wireless ad-hoc network also called as mobile ad-hoc network (MANET) is a gathering of hubs that utilizes a wireless channel to exchange information and coordinate together to establish information exchange among any pair of hubs, without any centralized structure. The security issue is a major difficulty while employing MANETs.FindingsConsequently, the attacks due to the malicious node activity are detected using Hybrid Reactive Search and Bat (HRSB) mechanism to prevent the mischievous nodes from entering the network beneath the untruthful information. Moreover, the attack detection rate and node energy are predicted for determining the lifetime of the node.Originality/valueThe simulation outcomes of the proposed HRSB technique are evaluated with the prevailing methods. The comparison studies have proven the efficacy of the current research model by attaining high attack detection rate and achieving more network lifetime.

WITHDRAWN: Detection and rectification of cloned node intruder in wireless sensor network

Abstract Clone node attack is one of the major security issues in wireless sensor networks. Basically wireless sensor network is the combination of several sensor nodes which is deployed in some hostile environment to monitor that environment. There are various specific use of this network like system monitoring and analysing different properties of environment, household monitoring and it ranges up to military application also. The researchers are also facing lot of difficulties in the detection of clone node. Detection of a cloned node intruder in Homogenous Wireless Sensor Networks has many useful application in terms that they can be deployed in war zones for detecting intruder (enemies) and uniquely identify them which helps in preventing any harmful situation they can create when they continue to exist inside the system. This intrusion detection system is used to identify the mismatched, malicious, non-orient able nodes and false committing nodes. For demonstrating our work we have taken into account detection intrusion in a Heterogeneous network. For this we have taken into account Single –capability sensor detection and Multiple – capabilities sensor detection in heterogeneous connected wireless network.

Vulnerability modeling and assessment in urban transit systems considering disaster chains: A weighted complex network approach

This research develops a hybrid approach that integrates disaster chains and complex networks to model and assess vulnerability in urban transit systems. A weighted complex network is established by combining various disaster chains and their frequencies in the operation of the urban transit system, where disaster events and their transmission paths in chains are considered as nodes and edges in the disaster network, respectively. Several measures are proposed to quantify the vulnerability magnitude of the disaster nodes, edges, and network from both micro-level and macro-level perspectives. A weighted complex network consisting of 31 disaster nodes and 73 edges is constructed to model the disaster transmission in the operation of the metro system. Results show that (1) The steward misoperation (Node 15), the unqualified maintenance (Node 17), and the edge E38 (from Node 17 to Node 23) are identified as the most vulnerable nodes and edge, which should be paid more attention for disaster risk reduction; (2) The network efficiency is calculated to be 0.1244, acting as an anchor for improving system resilience against disasters; (3) The proposed edge vulnerability measure proves to be more effective in discovering more vulnerable edges than the traditional edge measure. Both node and edge attack analysis are performed to discuss the robustness of the developed complex network. It is found that all the intentional attack strategies display better performance for reducing network efficiency than random attack strategies, and global attack strategies are more efficient in both node and edge attack analysis than local attack strategies. This indicates that the developed approach is necessarily effective to identify the vulnerability of disaster events and links in the urban transit system, enabling to discover the best strategy for improving the resilience of the transit system against disasters.

A secure routing protocol for low power and lossy networks based 6LoWPAN networks to mitigate DIS flooding attacks

Routing protocols for low-power and lossy networks (RPL) is considered as one of the significant information forwarding routing protocols for IPV6-based low-power wireless personal area networks (6LoWPAN). However, RPL suffers in lack of an appropriate load balancing mechanism and malicious node attack whenever it communicates the DODAG information solicitation (DIS) messages to join in the network. Moreover, in the existing system, the uneven load leads to packet losses, delay, consumes more power, and degrades the lifespan of the network. In order to avoid the drawbacks this research work proposes a novel adaptive load balancing and route discovery mechanism to eradicate the DIS flooding attacks and improve the network lifespan with the aid of enhanced secure RPL routing protocol. This proposed secure RPL routing protocol distributes the load between the several modes efficiently and mitigates the DIS flooding attack in the network. The proposed approach is implemented using Cooja platform simulator with static and dynamic network scenarios. To compare the performance of the proposed system, the standard RPL protocol and RPL without any security are used. The experimental results indicate that the proposed scheme outperforms the other routing protocol by eradicating the DIS flooding attacks and load distribution over the network. The simulation results show that the proposed scheme could decrease the average packet loss ratio, end-to-end delay, energy consumption, control overheads, and enhance packet delivery, throughput, and network lifespan.

Robustness Assessments of Urban Rail Transit Networks Based on Network Utilization

Urban rail transit has become an important traffic mode in large cities and has brought great conveniences to urban residents, however traffic accidents often occur in daily operations, therefore the safety and robustness must be paid more attention. In this paper, the network utilization of urban rail transit networks ( URTNs ) is expressed by the travel time difference between user equilibrium and optimal system, and the travel time difference is considered as the assessment indicator of robustness. Meanwhile, Shanghai metro network is taken as the example to analyze the robustness of URTNs under different disturbances, such as node attack, link-demand attack and overall-demand attack. The results show that URTNs have better robustness subjected to the random link-demand attack than node attack, and the robustness decreases with the increase of the attack intensity when the network suffers overall-demand attack. Moreover, we find that the critical stations and links can be identified, and URTNs can be paralyzed when the attack intensity exceeds the critical threshold.

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.

Malicious node detection based on clustering techniques in network

Malicious node is an exciting area of research which has been impacting negatively over the performance parameters of the network. The attacker node can affect the throughput of the network causing the degradation of the network response time. To deal with it, anomaly based intrusion detection techniques have been used. Behavior based detection observe the behavior of the existing node by analysing its parameters and determine the type of the node. Intelligent mechanisms in the form of machine learning are developing newer techniques for localisation and removal of such malicious nodes from the network. The main aim of the study is to analyse the features of the various networks and identify the category of the node based on some particular characteristics by using unsupervised machine learning. For determining the malicious node, clustering techniques are tested on the reduced CIDDS-001 dataset and it was found that the density based clustering algorithm is performing best in identification of malicious nodes in the network.

A Network Security Risk Assessment Method Based on a B_NAG Model

Computer networks face a variety of cyberattacks. Most network attacks are contagious and destructive, and these types of attacks can be harmful to society and computer network security. Security evaluation is an effective method to solve network security problems. For accurate assessment of the vulnerabilities of computer networks, this paper proposes a network security risk assessment method based on a Bayesian network attack graph (B_NAG) model. First, a new resource attack graph (RAG) and the algorithm E-Loop, which is applied to eliminate loops in the B_NAG, are proposed. Second, to distinguish the confusing relationships between nodes of the attack graph in the conversion process, a related algorithm is proposed to generate the B_NAG model. Finally, to analyze the reachability of paths in B_NAG, the measuring indexs such as node attack complexity and node state transition are defined, and an iterative algorithm for obtaining the probability of reaching the target node is presented. On this basis, the posterior probability of related nodes can be calculated. A simulation environment is set up to evaluate the effectiveness of the B_NAG model. The experimental results indicate that the B_NAG model is realistic and effective in evaluating vulnerabilities of computer networks and can accurately highlight the degree of vulnerability in a chaotic relationship.

Game Theory Analysis and Modeling of Sophisticated Multi-Collusion Attack in MANETs

Mobile Adhoc Network (MANET) has been a core topic of research since the last decade. Currently, this form of networking paradigm is increasingly being construed as an integral part of upcoming urban applications of Internet-of-Things (IoT), consisting of massive connectivity of diverse types of nodes. There is a significant barrier to the applicability of existing routing approaches in conventional MANETs when integrated with IoT. This routing mismatch can lead to security risks for the MANET-based application tied with the IoT platform. This paper examines a pragmatic scenario as a test case wherein the mobile nodes must exchange multimedia signals for supporting real-time streaming applications. There exist two essential security requirements viz. i) securing the data packet and ii) understanding the unpredictable behavior of the attacker. The current study considers sophistication on the part of attacker nodes. They are aware of each other’s identity and thereby collude to conduct lethal attacks, which is rarely reflected in existing security modeling statistics. This research harnesses the potential modeling aspect of game theory to model the multiple-collusion attacker scenario. It contributes towards i) modeling strategies of regular/malicious nodes and ii) applying optimization principle using novel auxiliary information to formulate the optimal strategies. The model advances each regular node’s capability to carry out precise computation about the opponent player’s strategy prediction, i.e., malicious node. The simulation outcome of the proposed mathematical model in MATLAB ascertains that it outperforms the game theory’s baseline approach.

Detecting Sinkhole Attack in RPL-based Internet of Things Routing Protocol

The Internet of Things (IoT) is a novel paradigm in computer networks which is capable to connect things to the internet via a wide range of technologies. Due to the features of the sensors used in IoT networks and the unsecured nature of the internet, IoT is vulnerable to many internal routing attacks. Using traditional IDS in these networks has its own challenges due to the resource constraint of the nodes, and the characteristics of the IoT network. A sinkhole attacker node, in this network, attempts to attract traffic through incorrect information advertisement. In this research, a distributed IDS architecture is proposed to detect sinkhole routing attack in RPL-based IoT networks, which is aimed to improve true detection rate and reduce the false alarms. For the latter we used one type of post processing mechanism in which a threshold is defined for separating suspicious alarms for further verifications. Also, the implemented IDS modules distributed via client and router border nodes that makes it energy efficient. The required data for interpretation of network’s behavior gathered from scenarios implemented in Cooja environment with the aim of Rapidminer for mining the produces patterns. The produced dataset optimized using Genetic algorithm by selecting appropriate features. We investigate three different classification algorithms which in its best case Decision Tree could reaches to 99.35 rate of accuracy.