scholarly journals Feature Reduction in MANET using Machine Learning Language

2019 ◽  
Vol 9 (2S4) ◽  
pp. 639-643
Keyword(s):  
Machine Learning ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Feature Reduction ◽  
Learning System ◽  
Learning Program ◽  
Misbehaving Nodes ◽  
Learning Language ◽  
Exact Security ◽  
Selection Of

Mobile ad-hoc network (MANET) is an infrastructure-less network. Therefore, MANET involves a selection of exact security schemes to notice the false entrance of the mischievous nodes. Along these lines, we require solid instrument to identify these pernicious nodes and to arrange ordinary and irregular nodes based on the conduct or performance of nodes. Machine learning system nowadays used to built a best IDS for recognizing exception or misbehaving nodes rapidly and precisely give grouping by watching conduct of those nodes in the system. In MANET system, numbers of parameters are taken for analysation. It makes the IDS system complex. To avoid this complexity many techniques are derived for feature reduction. In this proposed work, we are testing how feature reduction can be done using Python machine learning program.

An Optimal Fuzzy Load Balanced Adaptive Gateway Discovery for Ubiquitous Internet Access in MANET

Fuzzy Systems ◽  
2017 ◽  
pp. 663-681 ◽  
Author(s):  
Prakash Srivastava ◽  
Rakesh Kumar
Keyword(s):  
Fuzzy Logic ◽  
Natural Disaster ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Internet Access ◽  
The Internet ◽  
Optimal Selection ◽  
Multiple Parameters ◽  
Gateway Discovery ◽  
Selection Of

A mobile ad hoc network (MANET) is an autonomous collection of independent nodes cooperating together to form an infrastructure less network spontaneously. For increasing usability of MANET domain which finds application in natural disaster such as earthquake, floods etc. it is also desired to be connected with Internet through Internet gateways. Therefore, an efficient gateway discovery mechanism is required for MANET-Internet integration. Existing schemes use one or multiple parameters for optimal selection of gateway which causes a particular gateway to be selected many times which results in higher delay latency and packet drops due to prevailing congestion at a particular gateway. To avoid this situation, the authors have utilized the potential of fuzzy logic to ascertain the decision of load balancing at the Internet gateway. Besides this, their scheme also incorporates an effective adaptive gateway discovery mechanism. Consequently, enhanced performance is achieved as compared to existing state-of-the-art related schemes. The proposed approach is evaluated by simulation and analytical validation.

Adaptive Learning System Designed and Learning Program Optimization Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 3114-3118
Author(s):  
Chang Ming Liu ◽  
Yan Jun Sun ◽  
Hai Yu Li
Keyword(s):  
Adaptive Learning ◽  
Personality Characteristic ◽  
Learning System ◽  
Program Optimization ◽  
Learning Program ◽  
Teaching Mode ◽  
Learning Scheme ◽  
Adaptive Learning System ◽  
Positive Effect ◽  
Selection Of

Adaptive learning system has been developed maturing. But the formulation and selection of learning scheme in the learners' personality characteristic mining is still not satisfactory. The adaptive learning scheme with specific learner characteristics personalized will have positive effect for the learner on learning effect. The emergence of adaptive learning system based on network, making the learning is no longer affected by time and space restrictions, and the teachers teaching mode is changing to students' learning mode. The prior paper had given a brief introduction for adaptive learning system. Then in this paper we will focus on the designed theoretical knowledge of the adaptive learning system and give the design scheme.

scholarly journals Research Trend of Causal Machine Learning Method: A Literature Review

2020 ◽  
Vol 9 (2) ◽  
pp. 111-118
Author(s):  
Shindy Arti ◽  
Indriana Hidayah ◽  
Sri Suning Kusumawardhani
Keyword(s):  
Machine Learning ◽  
Learning System ◽  
Research Trend ◽  
Learning Approaches ◽  
Algorithm Performance ◽  
Combination Technique ◽  
Causal Impact ◽  
Learning Research ◽  
The Relationship ◽  
Selection Of

Machine learning is commonly used to predict and implement  pattern recognition and the relationship between variables. Causal machine learning combines approaches for analyzing the causal impact of intervention on the result, asumming a considerably ambigous variables. The combination technique of causality and machine learning is adequate for predicting and understanding the cause and effect of the results. The aim of this study is a systematic review to identify which causal machine learning approaches are generally used. This paper focuses on what data characteristics are applied to causal machine learning research and how to assess the output of algorithms used in the context of causal machine learning research. The review paper analyzes 20 papers with various approaches. This study categorizes data characteristics based on the type of data, attribute value, and the data dimension. The Bayesian Network (BN) commonly used in the context of causality. Meanwhile, the propensity score is the most extensively used in causality research. The variable value will affect algorithm performance. This review can be as a guide in the selection of a causal machine learning system.

scholarly journals Improving the selection of MPRs in OLSR protocol: a survey of methods and techniques

2020 ◽  
Vol 10 (1) ◽  
pp. 288
Author(s):  
Omar Barki ◽  
Zouhair Guennoun ◽  
Adnane Addaim
Keyword(s):  
Routing Protocol ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Link State ◽  
Execution Environment ◽  
Mobile Ad Hoc ◽  
Optimized Link State Routing ◽  
Selection Of

Multi Point Relays (MPRs) are those nodes that are calculated and determined by the Optimized Link State Routing protocol (OLSR) in order to minimize and avoid overload inside the Mobile Ad hoc Network (MANET). In this paper, we will present a synthetic study of many techniques and methods for calculating and selecting the MPR nodes using a set of criteria namely energy, mobility, bandwidth, the quality of links, etc. The result of this study shows that most techniques consider a limited number of metrics for selecting the MPR nodes and therefore they are insufficient to allow the OLSR protocol to be quite complete and efficient because several metrics can occur at the same time in the real execution environment.

Intrusion Finding Techniques for Collaboration of Node in Mobile ad hoc network (MANET)

2015 ◽  
Vol 9 (1and2) ◽  
Author(s):  
Hoshiyar Singh Kanyal ◽  
Prof. (Dr.) S. Rahamatkar ◽  
Dr. B. K. Sharma
Keyword(s):  
Routing Protocols ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Detection Techniques ◽  
Secure Protocol ◽  
Misbehaving Nodes ◽  
Manet Routing ◽  
Mobile Ad Hoc ◽  
Hoc Networks

Since there is no infrastructure in mobile ad hoc networks, each node must rely on other nodes for cooperation in routing and forwarding packets to the destination. Intermediate nodes might agree to forward the packets but actually drop or modify them because they are misbehaving. The simulations in show that only a few misbehaving nodes can degrade the performance of the entire system. There are several proposed techniques and protocols to detect such misbehavior in order to avoid those nodes, and some schemes also propose punishment as well. It is very difficult to design once-for-all intrusion detection techniques. Instead, an incremental enhancement strategy may be more feasible. A secure protocol should at least include mechanisms against known attack types. In addition, it should provide a scheme to easily add new security features in the future. Due to the importance of MANET routing protocols, we focus on the detection of attacks targeted at MANET routing protocols This include WatchDog and Pathrater approach. A watchdog identifies the misbehaving nodes by eavesdropping on the transmission of the next hop. A path rater then helps to find the routes that do not contain those nodes. In DSR, the routing information is defined at the source node. This routing information is passed together with the message through intermediate nodes until it reaches the destination.

A New Intrusion Detection Method Based on Machine Learning in Mobile Ad Hoc NETwork

2014 ◽  
Vol 548-549 ◽  
pp. 1304-1310
Author(s):  
Lai Cheng Cao ◽  
Wei Han ◽  
Sheng Dong
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
False Negative ◽  
False Negative Rate ◽  
Area Of Interest ◽  
Negative Rate ◽  
Mobile Ad Hoc

In a Mobile Ad hoc NETwork (MANET), intrusion detection is of significant importance in many applications in detecting malicious or unexpected intruder (s). The intruder can be an enemy in a battlefield, or a malicious moving object in the area of interest. Unfortunately, many anomaly intrusion detection systems (IDS) take on higher false alarm rate (FAR) and false negative rate (FNR). In this paper, we propose and implement a new intrusion-detection system using Adaboost, a prevailing machine learning algorithm, and its detecting model adopts a dynamic load-balancing algorithm, which can avoid packet loss and false negatives in high-performance severs with handling heavy traffic loads in real-time and can enhance the efficiency of detecting work. Compared to contemporary approaches, our system demonstrates an especially low false positive rate and false negative rate in certain circumstances while does not greatly affect the network performance.

scholarly journals A Multi-Hop Advertising Discovery and Delivering Protocol for Multi Administrative Domain MANET

2013 ◽  
Vol 9 (3) ◽  
pp. 261-280 ◽  
Author(s):  
Federico Mari ◽  
Igor Melatti ◽  
Enrico Tronci ◽  
Alberto Finzi
Keyword(s):  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Bloom Filters ◽  
Network Node ◽  
Central Authority ◽  
Network Nodes ◽  
Misbehaving Nodes ◽  
Mobile Ad Hoc ◽  
Service Advertising

A Mobile Ad-hoc NETwork (MANET) is Multi Administrative Domain (MAD) if each network node belongs to an independent authority, that is each node owns its resources and there is no central authority owning all network nodes. One of the main obstructions in designing Service Advertising, Discovery and Delivery (SADD) protocol for MAD MANETs is the fact that, in an attempt to increase their own visibility, network nodes tend to flood the network with their advertisements. In this paper, we present a SADD protocol for MAD MANET, based on Bloom filters, that effectively prevents advertising floods due to such misbehaving nodes. Our results with the ns-2 simulator show that our SADD protocol is effective in counteracting advertising floods, it keeps low the collision rate as well as the energy consumption while ensuring that each peer receives all messages broadcasted by other peers.

scholarly journals Deployment and Selection of Monitoring Nodes for Detection of Selfish Attacks in MANET

2020 ◽  
Vol 9 (4) ◽  
pp. 2217-2221
Keyword(s):  
Energy Consumption ◽  
Network Performance ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Detection System ◽  
Minimum Cost ◽  
Selfish Nodes ◽  
High Reward ◽  
Network Operation ◽  
Selection Of

Since Mobile Ad hoc Network (MANET) has distributed network structure using wireless links, designing efficient security applications has become a critical need. Selfish nodes are nodes that refuse to forward the data from other nodes. The existence of selfish nodes will disturb the normal process of the network, and reduce the network performance. Intrusion Detection System (IDS) is a scheme for detecting any misbehaviors in the network operation by monitoring the traffic flow. Each monitoring node need to execute the IDS module. The common problems encountered by the monitoring nodes are energy depletion, link disconnection, mobility and coverage. Hence the selection of monitoring nodes plays an important role in IDS. This paper develops a technique for deployment and selection of monitoring nodes for detection of selfish attacks. In this technique, the whole network is virtually divided in smaller grid like zones. In each grid, the nodes with higher stability and better coverage are assigned a reward value. A cost metric is derived in terms of energy consumption and computational delay. Then the nodes with minimum cost and high reward are selected as monitoring nodes. By simulation results, it is shown that the proposed technique has reduced detection delay, energy consumption and detection overhead.

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