scholarly journals HARDWARE IMPLEMENTATION OF NEURAL NETWORK FOR VEHICLE CLASSIFICATION USING FPGA

2012 ◽  
pp. 131-135
Author(s):  
ANIKET U TRIVEDI ◽  
VM UMALE
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Intrusion Detection ◽  
Acoustic Signal ◽  
Hardware Implementation ◽  
Sensor Nodes ◽  
Vehicle Classification ◽  
Fixed Size ◽  
Active Sensors ◽  
Border Surveillance

Vehicle detection and classification is an important and demanding application of WSN. The idea of utilizing small, almost invisible sensor nodes, equipped with a variety of passive and active sensors, for intrusion detection, force protection and border surveillance, though not new, remains fascinating. This project presents vehicle classification using acoustic signal and its FPGA implementation. For feature extraction TESPAR coding used to produce simple and fixed size matrices which are used for classification purpose. Classification is accomplished by using ANN. It is implemented on FPGA.

scholarly journals A New Neural Network-Based Intrusion Detection System for Detecting Malicious Nodes in WSNs

2020 ◽  
Vol 1 (3) ◽  
pp. 1-8
Author(s):  
Narmatha C ◽  
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Key Management ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Fuzzy Rule ◽  
Sensor Nodes ◽  
Abnormal Behavior ◽  
Malicious Nodes

The Wireless Sensor Networks (WSNs) are vulnerable to numerous security hazards that could affect the entire network performance, which could lead to catastrophic problems such as a denial of service attacks (DoS). The WSNs cannot protect these types of attacks by key management protocols, authentication protocols, and protected routing. A solution to this issue is the intrusion detection system (IDS). It evaluates the network with adequate data obtained and detects the sensor node(s) abnormal behavior. For this work, it is proposed to use the intrusion detection system (IDS), which recognizes automated attacks by WSNs. This IDS uses an improved LEACH protocol cluster-based architecture designed to reduce the energy consumption of the sensor nodes. In combination with the Multilayer Perceptron Neural Network, which includes the Feed Forward Neutral Network (FFNN) and the Backpropagation Neural Network (BPNN), IDS is based on fuzzy rule-set anomaly and abuse detection based learning methods based on the fugitive logic sensor to monitor hello, wormhole and SYBIL attacks.

scholarly journals CLASSIFICATION OF VEHICLE TYPES USING BACKPROPAGATION NEURAL NETWORKS WITH METRIC AND ECENTRICITY PARAMETERS

2021 ◽  
Vol 4 (1) ◽  
pp. 65-70
Author(s):  
Hendra Mayatopani ◽  
Rohmat Indra Borman ◽  
Wahyu Tisno Atmojo ◽  
Arisantoso Arisantoso
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Vehicle Classification ◽  
Test Results ◽  
Backpropagation Neural Network ◽  
Vehicle Type ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Wheeled Vehicles

One of the efforts to break down traffic jams is to establish special lanes that can be passed by two, four or more wheeled vehicles. By being able to recognize the type of vehicle can reduce congestion. Citran based vehicle classification helps in providing information about the vehicle type. This study aims to classify the type of vehicle using a backpropagation neural network algorithm. The vehicle image can be recognized based on its shape, then the backpropagation neural network algorithm will be supported by metric and eccentricity parameters to perform feature extraction. Then from the results of feature extraction with metric parameters and eccentricity, the object will be classified using a backpropagation neural network algorithm. The test results show an accuracy of 87.5%. This shows the algorithm can perform classification well.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

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