Malicious Behavior Of Nodes Research Articles

Blockchain Based Decentralized and Proactive Caching Strategy in Mobile Edge Computing Environment.

In the mobile edge computing (MEC) environment, the edge caching can provide the timely data response service for the intelligent scenarios. However, due to the limited storage capacity of edge nodes and the malicious node behavior, the question of how to select the cached contents and realize the decentralized security data caching faces challenges. In this paper, a blockchain-based decentralized and proactive caching strategy is proposed in an MEC environment to address this problem. The novelty is that the blockchain was adopted in an MEC environment with a proactive caching strategy based on node utility, and the corresponding optimization problem was built. The blockchain was adopted to build a secure and reliable service environment. The employed methodology is that the optimal caching strategy was achieved based on the linear relaxation technology and the interior point method. Additionally, in a content caching system, there is a trade-off between cache space and node utility, and the caching strategy was proposed to solve this problem. There was also a trade-off between the consensus process delay of blockchain and the caching latency of content. An offline consensus authentication method was adopted to reduce the influence of the consensus process delay on the content caching. The key finding was that the proposed algorithm can reduce latency and can ensure the security data caching in an IoT environment. Finally, the simulation experiment showed that the proposed algorithm can achieve up to 49.32%, 43.11%, and 34.85% improvements on the cache hit rate, the average content response latency, and the average system utility, respectively, compared to the random content caching algorithm, and it achieved up to 9.67%, 8.11%, and 5.95% increases, successively, compared to the greedy content caching algorithm.

A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs.

Vehicle ad hoc networks (VANETs) are a vital part of intelligent transportation systems (ITS), offering a variety of advantages from reduced traffic to increased road safety. Despite their benefits, VANETs remain vulnerable to various security threats, including severe blackhole attacks. In this paper, we propose a deep-learning-based secure routing (DLSR) protocol using a deep-learning-based clustering (DLC) protocol to establish a secure route against blackhole attacks. The main features and contributions of this paper are as follows. First, the DLSR protocol utilizes deep learning (DL) at each node to choose secure routing or normal routing while establishing secure routes. Additionally, we can identify the behavior of malicious nodes to determine the best possible next hop based on its fitness function value. Second, the DLC protocol is considered an underlying structure to enhance connectivity between nodes and reduce control overhead. Third, we design a deep neural network (DNN) model to optimize the fitness function in both DLSR and DLC protocols. The DLSR protocol considers parameters such as remaining energy, distance, and hop count, while the DLC protocol considers cosine similarity, cosine distance, and the node's remaining energy. Finally, from the performance results, we evaluate the performance of the proposed routing and clustering protocol in the viewpoints of packet delivery ratio, routing delay, control overhead, packet loss ratio, and number of packet losses. Additionally, we also exploit the impact of the mobility model such as reference point group mobility (RPGM) and random waypoint (RWP) on the network metrics.

ChainKeeper: A cross‐chain scheme for governing the chain by chain

AbstractWith the rapid application of consortium chains, supervising these systems has become a challenge for governments. The centralized model fails to deliver supervision services that are both open and transparent. Given the benefits of decentralization, non‐tampering, and traceability offered by blockchains, researchers propose the concept of ‘governing the chain by chain’, which involves supervising multiple consortium chains by constructing a blockchain. Under this idea, the cross‐chain scheme becomes the key to achieving excellent supervision. Existing studies have shortcomings and cannot meet the requirements of universality, security, and efficiency in cross‐chain supervision scenarios. Aiming at the challenges, we propose ChainKeeper, a cross‐chain scheme for governing the chain by chain. The innovation of our work lies in three points. First, a modular node proxy program is designed to adapt to various implementations of consortium chains. Second, a verifiable node random selection method is put forward to improve the throughput of cross‐chain data transmission. Finally, a verifiable identity threshold signature method is proposed to prevent the cheating behavior of malicious nodes. To verify the universality of ChainKeeper, we built a prototype system on three types of consortium chains. The experimental results show that ChainKeeper can achieve high throughput, outperforming two state‐of‐the‐art cross‐chain schemes.

An optimal scheduling algorithm considering the transactions worst-case delay for multi-channel hyperledger fabric network

As the most popular consortium blockchain platform, Hyperledger Fabric (Fabric for short) has released multiple versions that support different consensus protocols to address the risks faced in current and future network transactions. For example, Fabric v1.4 and v2.0 use Kafka and Raft mechanisms to complete consensus and ensure that the system can withstand failures such as crashes, network partitions, or network shutdowns. In a multi-channel Fabric network architecture, the system structure cannot guarantee the behavior of malicious nodes. Complex cooperation between peer groups on different channels can greatly affect the security and efficiency of the entire network architecture, which is challenging to estimate and optimize.To address this challenge, we designed a Drift Plus Penalty Algorithm (DPPA) and a Transaction Worst-case Delay Algorithm (TWDA) based on peer node random scheduling using the Lyapunov optimization framework. The DPPA ensures the stability of the system and provides the maximum transaction processing rate under the minimum safety probability. The numerical results show that this algorithm can achieve a good balance between system security probability and queue accumulation. The TWDA considers discarding transactions with excessively long delay time by setting a worst-case transaction delay threshold. When considering both the security probability and queue accumulation of the Fabric system, the optimal scheduling of peer nodes is given. Numerical simulations were conducted on two types of algorithms, and the results showed that the security of the TWDA was slightly worse than that of the DPPA, but the system queue accumulation was significantly smaller. Therefore, the simulation results not only validate the effectiveness of the two types of algorithms but also provide operators with operational strategies that consider different factors.

Efficient consensus algorithm based on improved DPoS in UAV-assisted mobile edge computing

Aiming at the problems of low decentralization, low motivation for node voting, and malicious behavior of nodes for the traditional DPoS consensus mechanism in the blockchain-based UAV-assisted mobile edge computing environment, this paper proposes an improved DPoS-based consensus mechanism approach. First, the framework of a blockchain-based UAV-assisted mobile edge computing system is given, and the consensus mechanism design problem in this framework is analyzed. Second, a proxy node selection model is established based on the rights and votes obtained by the blockchain nodes, and the proxy nodes are selected to participate in the consensus process of the current cycle. Then, the voting behavior, block generation behavior, and block verification behavior of nodes are classified into positive and malicious behaviors to reward and punish the reputation value of nodes. Finally, a blockchain-based UAV-assisted mobile edge computing experimental environment is built, and the TDPoS algorithm, ADRP algorithm, and RDPoS algorithm are used as benchmark algorithms to experimentally compare with the proposed improved DPoS consensus-based algorithm. The experimental results show that the algorithms in this paper can improve network throughput, reduce block-out delay, and increase the proportion of secure proxy nodes.

Artificialintelligence‐basedremote monitoring system for automated anomaly detection in wireless sensor networks

SummaryIn recent decades, wireless sensor networks (WSNs) are employed in the remote monitoring system which experiences a huge delay due to the excessive time consumption by the image processing techniques. The existing video surveillance system is unable to detect the anomaly accurately and falls short in classifying the routine activity and the anomaly activities. Therefore, an efficient anomaly detection model is necessitated for identifying the anomaly behavior of malicious nodes in the WSN environment. This research work introduces a novel artificial intelligence (AI)‐based remote monitoring system for WSN. Primarily, the online video frames are retrieved from the wireless video sensors where the frames are generated by segmenting the entire stream of video as a constant‐sized structural frame. The sequence/stream of video segments is fed into the proposed AI framework to detect anomaly activities. It has multiple levels of hidden layers to compare the features of the input captured image with the preloaded reference image. Furthermore, a 5‐fold cross‐validation approach has been inspected to validate the efficacy of the suggested AI model. The performance results evident that the proposed AI system acquires a better prediction accuracy of 98.2% and precision of 97.3% than the existing monitoring systems.

Predicting Malicious Node Behavior in Wireless Network Using DSR Protocol and Network Metrics

This paper describes a set of network metrics are helpful to predict behavior of malicious node in wireless network. The Network and internet is the device in which multiple people can communicate with each other through the wired or wireless media. Nowadays, Internet of Things, Mobile, vehicular, and wireless ad hoc networks all merge into one shared network. These networks are often used to send receive confidential data and information. The unauthorized or malicious node misuse these secrete information. With a rise in rogue nodes, network performance will suffer. A rogue node in the network can cause variations in network metrics including the packet dropping percentage, throughput, latency, energy consumption, and average queue duration. This behavior used to identify the malicious node.

Dual-Chain Blockchain in Agricultural E-Commerce Information Traceability Considering the Viniar Algorithm

In this paper, we consider the Vennia algorithm to conduct in-depth research and analysis on the traceability of dual-chain blockchain agricultural products' E-commerce information. This paper adds a collaborative verification module to the traceability system and carries out a detailed design of information storage, traceability consensus algorithm, and smart contract for agricultural products according to the characteristics of the agricultural products supply chain, among which the collaborative verification module adopts dynamic data storage technology; the ConsiderVinia consensus algorithm is improved by introducing the way of integral penalty mechanism to ensure the block data validity. After a comparative study of the features and differences of the three major blockchain technology platforms, this paper selects the super ledger to implement the agricultural traceability system based on blockchain technology, introduces the partitioning and credit mechanism into the ConsiderVinia algorithm, and elaborates the improvement process of the algorithm. The improved algorithm reduces the malicious behavior of nodes and maintains the system security through a credit mechanism while maintaining the consistency of blockchain. In the event of a transaction dispute, the third-party platform will determine the party at fault based on the transaction records and other evidence and make corresponding punishments and compensations. The experiment proves that the algorithm proposed in this paper can reduce the amount of network data transmission in the process of node consensus, which is better than the ConsiderVinia algorithm in terms of both throughput and latency, improves the consensus efficiency, and alleviates the communication bottleneck caused by the increase of users in blockchain applications, and the solution of applying the blockchain technology to the agricultural products traceability system is practical and feasible. The blockchain-based agricultural products information traceability system solves the problems of information asymmetry, difficult sharing, easy tampering, and storage centralization in the traditional IoT-based agricultural products traceability system and truly realizes the credible and reliable traceability of the whole chain of agricultural products information. The research content and results of this paper have certain theoretical and practical values.

A Novel Blockchain-Based Encryption Model to Protect Fog Nodes from Behaviors of Malicious Nodes

The world has experienced a huge advancement in computing technology. People prefer outsourcing their confidential data for storage and processing in cloud computing because of the auspicious services provided by cloud service providers. As promising as this paradigm is, it creates issues, including everything from data security to time latency with data computation and delivery to end-users. In response to these challenges, the fog computing paradigm was proposed as an extension of cloud computing to overcome the time latency and communication overhead and to bring computing and storage resources close to both the ground and the end-users. However, fog computing inherits the same security and privacy challenges encountered by traditional cloud computing. This paper proposed a fine-grained data access control approach by integrating the ciphertext policy attribute-based encryption (CP-ABE) algorithm and blockchain technology to secure end-users’ data security against rogue fog nodes in case a compromised fog node is ousted. In this approach, we proposed federations of fog nodes that share the same attributes, such as services and locations. The fog federation concept minimizes the time latency and communication overhead between fog nodes and cloud servers. Furthermore, the blockchain idea and the CP-ABE algorithm integration allow for fog nodes within the same fog federation to conduct a distributed authorization process. Besides that, to address time latency and communication overhead issues, we equip each fog node with an off-chain database to store the most frequently accessed data files for a particular time, as well as an on-chain access control policies table (on-chain files tracking table) that must be protected from tampering by rogue fog nodes. As a result, the blockchain plays a critical role here because it is tamper-proof by nature. We assess our approach’s efficiency and feasibility by conducting a simulation and analyzing its security and performance.

An Energy-Constrained Forwarding Scheme Based on Group Trustworthiness in Mobile Internet of Things

Residual energy of devices is a key factor to determine whether Internet of Things (IoT) applications can proceed smoothly. Partnership cheating behavior of malicious nodes easily distorts the trust evaluation results only based on the one-to-one relationship between nodes, which greatly reduces the security of data transmission. To balance the excessive energy loss of mobile devices and improve the reliability of data transmission, this article proposes an energy-constrained forwarding scheme based on group trustworthiness between communities (ECF-GT) for mobile IoT, which does everything possible to increase the reliability of data forwarding by improving the accuracy of relay selection. First, it leverages nodal residual energy ratio and two common social attributes between nodes to measure the unidirectional trustworthiness between devices, and constructs the community structure by using our published <i>Dynamic Discovery of Trustworthiness Overlapping Community</i> method. Then, based on the one-to-many, many-to-one, many-to-many trustworthiness between nodes and/or communities, a group dynamic trustworthiness assessment method is proposed to enhance the reliability of evaluating relays. Finally, ECF-GT combines nodal residual energy and group trustworthiness to design forwarding utilities and rules. The experimental results show the effectiveness of ECF-GT. For example, when the number of malicious nodes accounts for 25&#x0025; of the total number of nodes, ECF-GT can save about 17.3&#x0025; energy on average comparing to other three well-known forwarding models on <i>Reality</i> dataset.

Mitigating service-oriented attacks using context-based trust for smart cities in IoT networks

Abstract Smart City technology is an attempt to improve the quality of life of its citizens by providing promising smart solutions for multiple applications. These applications include healthcare monitoring, resource utilization, city resource management, and various public services. Internet of Things (IoT) enables smart city applications to collect data from various sensors and process it for providing numerous smart services to the end-users with improved performance. The diverse nature of IoT network requires the use of multiple types of sensors which produce a huge amount of data. This data is highly vulnerable to multiple service-oriented attacks; therefore, it must be protected during the communication of IoT nodes. This research work has focused on the identification and detection of malicious nodes causing service-oriented attacks in smart city applications and networks. The direct experience of communicating nodes and recommendations from neighboring nodes are collected to formulate a total trust score. The adaptive weights assigned to direct observations and indirect recommendations ensure the effectiveness of the Context-Based Trust Evaluation System Model (CTES) in detecting On–Off attacks. Moreover, context similarity measure calculations filter out those bad nodes which are posing a Sybil Attack. The proposed CTES has also been simulated on Contiki Cooja. The results also validate the effectiveness of CTES in detecting the bad behavior of malicious nodes.

Network Intrusion Detection System Based on the Combination of Multiobjective Particle Swarm Algorithm‐Based Feature Selection and Fast‐Learning Network

Given the growth of wireless networks and the increase of the advantages and applications of communication networks, especially mobile ad hoc networks (MANETs), this type of network has attracted the attention of users and researchers more than before. The benefit of these types of networks in various kinds of networks and environments is that MANET does not require to hardware infrastructure to communicate and send and receive data packets within the network. It is one of the main reasons for using these MANET in various fields. On the other hand, the increased popularity of these networks has led to many challenges, one of the most important of which is network security. In this regard, a lack of regulatory and security infrastructure in MANETs has caused some problems in sending and receiving data, where intrusion in the network has been recognized as one of the most important issues. In MANETs, wireless notes act as a link between the source and destination nodes and play the role of relays and routers in the network. Therefore, malicious node penetration and the destruction of information packages become feasible. Today, intrusion detection systems (IDSs) are used as a solution to deal with the problem through remote monitoring of the performance and behaviors of nodes existing in wireless sensor networks. In addition to detecting malicious nodes in the network, IDSs can predict the behavior of malicious nodes in the future in most cases. Therefore, the present study introduced a network IDS (NIDS) entitled MOPSO‐FLN by using a combination of multiobjective particle swarm optimization algorithm‐ (MOPSO‐) based feature subset selection (FSS) and fast‐learning network (FLN). In this work, we used the KDD Cup99 and dataset to select features, train the network, and test the model. According to the simulation results, this method was able to improve the performance of the IDS in terms of evaluation criteria, compared to other previous methods, by creating a balance between the objectives of the number of representative features and training errors based on the evolutionary power of MOPSO.

Evaluation of Node-Metastasis in Sparse Underwater Acoustic Sensor Networks for Localization Under Acoustically Stratified Malicious Node Conditions

Malicious anchor node is a serious issue in underwater sensor networks. This problem gets compounded under sparse deployment scenario. To overcome this issue, the authors present a compressed sensing theorem (abbreviated as CS-MC Theorem) which would be applicable to underwater localization. To counter malicious node behavior, CS-MC Theorem is justified through three lemmas. For underwater acoustic propagation, a specific L1-norm minimization method is derived. It describes the condition for unique localization of target by discretizing the target search space. Malicious node is identified by inserting the anchor nodes into the discretized grid. An insertion vector concept is introduced to determine the extent of node metastasis. Derivations are performed to prove the mathematical basis of the proposed algorithm under acoustically stratified and unstratified scenarios. Numerical results establish the feasibility of CS-MC algorithm for sparse underwater acoustic sensor network.

Malicious Node Detection on Routing Via Certificate Authority and Optimal Fuzzy for Wireless Ad-Hoc Network

Wireless Ad-Hoc network has a set of nodes which designed with wireless transceivers. These nodes communicate with each other by accessing the common channel. In the ad-hoc network, security is one of the main challenges. Due to the behavior of malicious nodes, the security of the network is compromised. So, the major objective of this paper is to improve the security of the network by combining both malicious node detection and cryptography techniques. In this paper, an optimized fuzzy system is presented for the detection of malicious nodes which are malfunctioning in the routing path between source and destination. To optimize the performance of the fuzzy system, Particle Swarm Optimization (PSO) algorithm is presented. After the detection of malicious nodes, each normal behavior node in the network is authenticated by the Certificate Authority (CA). Then the authenticated nodes transmit the data packets to the destination with the Elliptic Curve Cryptography (ECC) algorithm. Simulation results show that the performance of the optimized fuzzy system is improved than that of the conventional fuzzy system for malicious node detection in terms of delivery ratio, detection time and energy efficiency.

Towards a Light Weight Routing Security in IoT Using Non-cooperative Game Models and Dempster–Shaffer Theory

The internet of things (IoT) has become an emerging technology owing to the rapidly increasing number of devices and their connectivity to the internet. Routing protocol for low-power and lossy networks (RPL) is a predominant routing protocol for the IoT. The RPL routing embraces the distance to the gateway node as a rank for parent selection and builds the destination oriented directed acyclic graph (DODAG). The malicious nodes utilize the advantage of low rank based parent selection to involve in data forwarding. However, the malicious node behavior and the harsh channel conditions due to the collision are equally considered as malicious in the existing detection mechanisms. In addition, most of the routing solutions exploit the traditional schemes for IoT security, but not considering the adaptability to IoT routing specifications. Thus, this work attempts to integrate the DODAG-specific contextual trust model, and RPL-specific rank variance factor, named as Secure RPL using non-cooperative game MOdels and DEmpster Shaffer Theory in IoT (S-MODEST), such that it can detect the attackers accurately and significantly reduces the resource consumption. The non-cooperative game model consists of two interrelated formulations in the proposed solution. Firstly, non-zero sum game constructs the trust model and extracts the contextual information from DODAG structure to strengthen the trustworthiness. Secondly, the evolutionary game is utilized for trusted router selection. The non-zero sum game theory formulates the interaction of nodes and assists the trust measurement by applying direct and restricted Dempster–Shaffer theory. The DODAG-specific contextual trust evaluation differentiates the collision dropping from the undesirable scenarios and supports the nodes in building a highly trusted DODAG structure to the gateway using multiplicative trust factor. To enforce the routing cooperation of selfish nodes and differentiate malicious nodes, the evolutionary game model embraces the node trust, which is measured using non-zero sum game model. As a result, the non-cooperative game models enable the S-MODEST to precisely select a secure route as well as to maintain the routing performance effectively. The simulation results demonstrate that the detection accuracy and throughput of the proposed context-aware noncooperative game theory based trust model are substantially higher and outperform the existing SecTrust.

En-ABC: An ensemble artificial bee colony based anomaly detection scheme for cloud environment

With an exponential increase in the usage of different types of services and applications in cloud computing environment, the identification of malicious behavior of different nodes becomes challenging due to the diversity of traffic patterns generated from various services and applications. Most of the existing solutions reported in the literature are restricted with respect to the usage of a specific technique applicable to single class datasets. But in real life scenarios, applications and services especially in cloud environment may have multi-class datasets. Moreover, non-linear behavior among the dataset attributes generates additional challenges for identification of nodes behavior, and it has not been exploited to its full potential in the existing solutions. This can lead to performance bottlenecks with respect to the identification of malicious behavior of different nodes. Motivated from these facts, this paper proposes an Ensemble Artificial Bee Colony based Anomaly Detection Scheme (En-ABC) for multi-class datasets in cloud environment. En-ABC has following components for identification of malicious behavior of nodes-(i) feature selection and optimization, (ii) data clustering, and (iii) identification of anomalous behavior of nodes. The feature selection and optimization model in En-ABC has been built using Restricted Boltzmann Machine and Unscented Kalman Filter (to handle the non-linear behavior of dataset attributes) respectively. Moreover, Artificial Bee Colony-based Fuzzy C-means clustering technique is used to obtain an optimal clustering based on two objective functions, i.e., Mean Square Deviation and Dunn Index (to handle the participation of attributes in multiple clustered datasets). Then, a profile of normal/abnormal behavior has been built using clustering results for detection of the anomalies. Finally, the performance of the proposed scheme has been compared with the existing schemes (CM, SVM, ML-IDS and MSADA) using various parameters such as-detection, false alarm, and accuracy rates. Experimental results on benchmark (NSL-KDD, NAB and IBRL) and synthetic datasets validate the effectiveness of the proposed scheme.

Trust evaluation model with entropy-based weight assignment for malicious node\u2019s detection in wireless sensor networks

Trust management is considered as an effective complementary mechanism to ensure the security of sensor networks. Based on historical behavior, the trust value can be evaluated and applied to estimate the reliability of the node. For the analysis of the possible attack behavior of malicious nodes, we proposed a trust evaluation model with entropy-based weight assignment for malicious node’s detection in wireless sensor networks. To mitigate the malicious attacks such as packet dropping or packet modifications, multidimensional trust indicators are derived from communication between adjacent sensor nodes, and direct and indirect trust values will be estimated based on the corresponding behaviors of those sensor nodes. In order to improve the validity of trust quantification and ensure the objectivity of evaluation, the entropy weight method is applied to determine the proper value of the weight. Finally, the indirect trust value and direct trust value are synthesized to obtain the overall trust. Experimental results show that the proposed scheme performs well in terms of the identification of malicious node.

Trust-based security routing mechanism in mobile social networks

Malicious and selfish behaviors represent a serious security threat against routing in mobile social networks (MSN). Due to MSN’s unique network characteristics, such as sociability, mobility and diversity, it is a challenge to design a misbehavior detection scheme in MSN. To improve the security in MSN routing, a trust-based security routing mechanism is presented in this paper, i.e., a malicious behavior detection mechanism with identity verification scheme. The main idea is to introduce the behavior trust and the identity trust to guide the routing. Firstly, we judge a node’s behavior based on both comprehensive trust and social relationship strength. And then, we forward messages according to the different measurement within and outside the friend groups. We also propose a distributed key management scheme which can issue, verify or revoke certificates based on the evaluation of the social relationship strength. We further improve the efficiency of the proposed mechanism by verifying the identity trust of the node. The simulation results indicate that the proposed routing mechanism can lower the impact of malicious behavior of nodes effectively and it has greater security performance than some classical routing algorithms do.

An energy efficient flexible delay tolerant network with adaptive secured framework (ASF-DTN)

Delay tolerant networks are widely used in mobile communications because of network withstanding capability of delay. However, when the connectivity of nodes increases, data loss may occur while transmission. Due to the malicious behavior of nodes the data may get permanently lost, which is known as black hole attacks and there is a chance to partial data loss because of the lower energy level of a node, which is known as a gray hole attack. In existing work, the data capacity of a node is not limited, so most of the highly energy efficient node contains a huge amount of data, when compared to other nodes which may lead to random attack. To overcome this, we propose a subjective capability model (SCM) for each and every node to limit the capacity of each node. ASF-DTN prevents collision attack and injection attack by implementing Kalman filtering, which can statistically analyze the behavior of nodes while each and every transmission. Here, we propose an effective node optimization scheme using genetic algorithm with a fitness function to find out energy efficient nodes among the optimal path for effective communication and its performance.

IMPROVED PREDICTION STRATEGY USING PARTICLE SWARM OPTIMIZATION BASED ARTIFICIAL NEURAL NETWORK (IPS-ANN) CLASSIFIER FOR MALICIOUS NODE DETECTION FOR HYBRID P2P NETWORKS

Mining data from the hybrid P2P networks is an ever demanding task for detecting malicious behavior of nodes. This research work aims to propose an improved prediction strategy using particle swarm optimization based artificial neural network (IPS-ANN) classifier. From the extensive literature study it is identified that only very few literatures are there for detecting malicious node activities. The simulation are carried out using MATLAB by configuring 1000 nodes with three different attack scenarios namely collusion attack, Sybil attacks and file polluter attacks. Detection accuracy, false positive rate and false negative rate are taken as the performance metrics for evaluating the efficiency of the proposed classifier and also compared with the existing mechanisms. Results proved that the proposed IPS-ANN classifier better than that of PeerMate , SMART , Outlier mining mechanisms.