2 edition of Detection of Replica Node Attack Based on Hybrid Artificial Immune System Technique found in the catalog.
Detection of Replica Node Attack Based on Hybrid Artificial Immune System Technique
by Association of Scientists, Developers and Faculties in Bangkok, Thailand
Written in English
In the recent years, Wireless Sensor Networks (WSNs) provide an economically feasible solution to a diversity of applications. The applications include object tracking and environmental monitoring. However, security of sensor nodes is critical because of the unattended nature of the network and thus they are prone to many attacks. One such attack is the node replication attack which corrupts the entire network by compromising few sensor nodes. Few of the techniques are proposed to detect the node replication attack using witness finding strategy and centralized detection methods are used for static networks. These methods incur high communication and memory overheads and induce problems related to security and efficiency. This paper proposes to solve these issues using Enhanced eXtremely Efficient Detection (Enhanced XED) and integrated Artificial Immune Systems (iAIS) model to detect the clones which are not resilient against collusive replicas. The advantages of the proposed method include (i) increase in the detection rate, (ii) decrease in the false rates, (iii) effectiveness and (iv) low energy consumption. The performance of the proposed work is measured using Bandwidth, Message drop, Energy, Overhead, Average Delay and Packet Delivery Ratio. The implementation is done using ns2 to exhibit the actuality of the proposed method.
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The goal of tissue engineering and regenerative medicine is to develop synthetic versions of human organs for transplantation, in vitro toxicology testing and to understand basic mechanisms of organ function. A variety of different approaches have been utilized to replicate the microenvironments found in lymph nodes including the use of a variety of different bio-materials, culture systems. References Primary Sources. The Artificial Immune Recognition System was proposed in the Masters work by Watkins [Watkins], and later published [Watkinsa].Early works included the application of the AIRS by Watkins and Boggess to a suite of benchmark classification problems [Watkins], and a similar study by Goodman and Boggess comparing to a conceptually similar approach called.
network partitioning and packet losses. The various attacks that can be carried out on MANETs challenge the security capabilities of the mobile wireless network in which nodes can join, leave and move dynamically. The Human Immune System (HIS) provides a foundation upon which Artificial Immune algorithms are based. The Artificial Immune System(AIS),however exhibits a very high level of ent algorithms used in AIS actually imitate the behavior of the different types of cells of the immune the first generation AIS,emphasis has been on Clonal Selection[1,2,3],Negative Selection[1,2,3] and Idiotypic.
problems such as intrusion detection, data clustering, and classification and search problems. A new hybrid paradigm of artificial immune recognition system algorithm along with FCM (AIRS_FCM_LD)is proposed for classification of learning disabled datasets and yields a classification accuracy of %. Keywords: learning disability, FCM, AIRS. An artificial immune system (ARTIS) is described which incorporates many properties of natural immune systems, including diversity, In this paper, ARTIS is applied to computer security in the form of a network intrusion detection system called LISYS. LISYS is described and shown to be effective at detecting intrusions, while maintaining low.
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Detection of Replica Node Attack Based on Hybrid Artificial Immune System Technique 1Ms. Sindhuja, 2Dr. Padmavathi 1Research Scholar, 2Professor and Head, Department of Computer Science, Avinashilingam University, Coimbatore Abstract-In the recent years, Wireless Sensor Networks (WSNs) provide an economically feasible solution.
In this paper, a novel protocol, called extremely Efficient Detection (XED), is proposed to resist against node replication attacks in mobile sensor networks.
The advantages of XED include (1) only. (PDF) Detection of Replica Node Attack Based on Hybrid Artificial Immune System Technique | Admin ASDF - In the recent years, Wireless Sensor Networks (WSNs) provide an economically feasible solution to a diversity of applications.
The applications include object tracking and environmental monitoring. An artificial immune system uses ideas from the operation of the human immune system and applies them to computational problems. Of particular relevance to the problem of intrusion detection is the fact that that the immune system can be viewed as performing anomaly detection, since it distinguishes between normal self and harmful non-self, e.g Cited by: Artificial Immune System A number of works related to intrusion detection have been inspired by the human immune system.
 presents one of the first lightweight intrusion detection systems based on AIS (Artificial Immune System) .
 presents an intrusion detection system based on the emerging ‘Danger Theory’ . An Intrusion Detection System is also expected to evolve over time to combat new attacks that emerge over time.
Thus an Intrusion Detection System design, based on a Human Immune System can provide significant advantages. Like the Human Immune System, the Intrusion Detection System has to work with a large amount of data (network traffic traces).
Network intrusion detection is the problem of detecting unauthorised use of, or access to, computer systems over a network. Two broad approaches exist to tackle this problem: anomaly detection and misuse detection. An anomaly detection system is trained only on examples of normal connections, and thus has the potential to detect novel attacks.
However, many anomaly detection. Similarly, in Attack-2, Attack-3 and Attack-4, the detecting nodes – node 1 and node 0– that receive the forged bee agents from the attacker nodes – node 2, node 5 and node 7– achieve a detection rate of % in Ph B because they successfully detect all non-self Ags.
Distributed Detection of Node Replication Attacks in Sensor Networks. This research paper is proposed by. Parno, A. Perrig, and V. Gilmore proposes a effective detection method called Randomized, Authentic, Efficient, Distributed protocol to detect node replication attack.
Detecting misbehaving node and punishing them is the only way for network survival. This paper introduces a Misbehavior Detection System (MDS) for MANET based on Artificial Immune System (AIS). Negative Selection technique is used for generating the detectors for identifying deviation from normal behavior.
Artificial Immune System (AIS), a model based on the principles of the biological immune system  is implemented for the computer viruses detection. It is a significant growing area employing immunological bio-mechanisms to solve virus computer problems.
Negative Selection Algorithm (NSA) and Clonal Selection. In this paper, we investigate the use of an artificial immune system (AIS) to detect node misbehavior in a mobile ad hoc network using DSR.
The system is inspired by the natural immune system (IS) of vertebrates. Our goal is to build a system that, like its natural counterpart, automatically learns, and detects new misbehavior. This paper introduces a hybrid model for network intrusion detection that combines artificial immune system methods with conventional information security methods.
The Network Threat Recognition with Immune Inspired Anomaly Detection, or NetTRIIAD, model incorporates misuse-based intrusion detection and network monitoring applications into an. The use of artificial immune systems in intrusion detection is an appealing concept for two reasons.
Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in. There is a lot of literature about ID, but this study only surveys the approaches based on Artificial Immune System (AIS).
The use of AIS in ID is an appealing concept in current techniques. This paper summarizes AIS based ID methods from a new view point; moreover, a framework is proposed for the design of AIS based ID Systems (IDSs).
Notice of Violation of IEEE Publication Principles"An Intrusion Detection Architecture for Ad hoc Network Based on Artificial Immune System"by Hongxia Xie and Zhengyun Huiin the Proceedings of the. ResearchArticle Efficient Hybrid Detection of Node Replication Attacks in Mobile Sensor Networks ZeWang,ChangZhou,andYiranLiu SchoolofComputerScienceandSoftware.
Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system.
During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. Definition. The field of Artificial Immune Systems (AIS) is concerned with abstracting the structure and function of the immune system to computational systems, and investigating the application of these systems towards solving computational problems from mathematics, engineering, and information technology.
AIS is a sub-field of Biologically-inspired computing, and Natural computation, with. Artificial Immune Network.
Immune network theory has been proposed first by Jerne in  and it has been widely used in the development of Artificial Immune System (AIS) . This theory suggests that for each antibody molecule, there is a portion of their receptor that.
Abstract: Artificial immune system (AIS) is considered as an adaptive computational intelligence method that could be used for detecting and preventing current computer network threats. AIS generates Antibodies (self) competent in recognizing Antigen (non-self), which is considered as an anomaly technique.
This paper aims to develop artificial immune system (AIS) that consists of two .In artificial intelligence, artificial immune systems (AIS) are a class of computationally intelligent systems inspired by the principles and processes of the vertebrate immune system.
The algorithms are typically modeled after the immune system's characteristics of learning and memory for .In mobile ad-hoc networks, nodes act both as terminals and information relays, and participate in a common routing protocol, such as Dynamic Source Routing (DSR).
The network is vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability.
In this paper we investigate the use of an Artificial Immune System (AIS) to detect.