IoT based Border Security System using Machine Learning

2021 ◽  
Author(s):  
Neda Fatima ◽  
Salman Ahmad Siddiqui ◽  
Anwar Ahmad
Keyword(s):  
Machine Learning ◽  
Security System ◽  
Border Security

SMART X-RAY BAGGAGE SECURITY SYSTEM USING MACHINE LEARNING ALGORITHM

2020 ◽  
Vol 8 (2) ◽  
pp. 8
Author(s):  
M. SUDHA ◽  
R. HARINI ◽  
D. JAYASHREE ◽  
K. KEERTHI NISHA ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Security System ◽  
Machine Learning Algorithm ◽  
X Ray

scholarly journals Repositioning security spaces of exclusion, exception, and integration in China–Southeast Asia borderlands

2021 ◽  
Vol 11 (2) ◽  
pp. 1-25
Author(s):  
Zhiding Hu ◽  
Victor Konrad
Keyword(s):  
Southeast Asia ◽  
Mobile Security ◽  
Security System ◽  
Border Security ◽  
English Abstract ◽  
Effective Management ◽  
Cross Border ◽  
Mise En Œuvre ◽  
Security Infrastructure ◽  
Security Checkpoints

English Abstract: Formerly localized, restricted border interaction between China and Southeast Asia has shifted to extensive cross-border engagement along regulated borders with a hierarchy of crossings and expansive borderlands. This expanded security system reveals rescaled and repositioned border security infrastructure and practice into a point and corridor system with vanguard crossings at Hekou, Mohan and Ruili. Fundamental shifts are concurrent focus on primary crossings and spatially extensive borderlands that encompass diminished attention to lesser crossings, beyond the border implementation of security checkpoints, mobile security, and compromise, to enable effective management of expansive borderlands. These borderlands mediate space and enable spatial reapportionment of security to accommodate greatly enhanced cross-border flows of people, goods, and information, thus shaping extensive spaces of exclusion and integration and focused places of exception.Spanish Abstract: La anteriormente restringida interacción fronteriza China–Sudeste Asiático, cambió a un extenso compromiso de fronteras reguladas con una jerarquía de cruces y zonas transfronterizas expansivas. Este sistema ampliado de seguridad, revela la infraestructura y prácticas transfronterizas reescaladas y reubicadas como puntos y sistemas de corredores con cruces de vanguardia en Hekou, Mohan y Ruili. Los cambios se enfocan en los cruces primarios y extensión de fronteras, disminuyendo la atención a los cruces menores —después de la implementación de puntos de control de seguridad—, la seguridad móvil y el compromiso a una gestión fronteriza eficaz. Estas zonas permiten la redistribución espacial de la seguridad acomodando los intensificados flujos transfronterizos de personas, bienes e información, conformando espacios de exclusión e integración, así como lugares de excepción focalizados. French Abstract: L’interaction frontalière entre la Chine et l’Asie du Sud-Est, autrefois localisée et limitée, s’est transformée en un engagement transfrontalier réglementé avec une hiérarchie de passages et des zones frontalières étendues. Ce système de sécurité élargi révèle une infrastructure et une pratique de sécurité frontalière redimensionnées et repositionnées dans un système de points et de corridors avec des passages d’avant-garde à Hekou, Mohan et Ruili. Les changements fondamentaux se concentrent sur les principaux points de passage, les zones frontalières étendues, la mise en œuvre de points de contrôle de sécurité, la sécurité mobile et le compromis, pour permettre une gestion effi cace des zones frontalières étendues. Ces dernières permettre ent la médiation de l’espace et la réaffectation spatiale de la sécurité afin d’accueillir des fl ux transfrontaliers de personnes, de biens et d’informations considérablement accrus, façonnant ainsi de vastes espaces d’exclusion et d’intégration et des lieux d’exception ciblés.

Machine-Learning-Based White-Hat Worm Launcher in Botnet Defense System

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Petri Net ◽  
Cyber Security ◽  
Large Scale ◽  
Security System ◽  
Divide And Conquer ◽  
Defense System ◽  
Divide And Conquer Algorithm

This article proposes a white-hat worm launcher based on machine learning (ML) adaptable to large-scale IoT network for Botnet Defense System (BDS). BDS is a cyber-security system that uses white-hat worms to exterminate malicious botnets. White-hat worms defend an IoT system against malicious bots, the BDS decides the number of white-hat worms, but there is no discussion on the white-hat worms' deployment in IoT network. Therefore, the authors propose a machine-learning-based launcher to launch the white-hat worms effectively along with a divide and conquer algorithm to deploy the launcher to large-scale IoT networks. Then the authors modeled BDS and the launcher with agent-oriented Petri net and confirmed the effect through the simulation of the PN2 model. The result showed that the proposed launcher can reduce the number of infected devices by about 30-40%.

scholarly journals Visitor/Intruder Monitoring System using Haar-cascade Classifier Algorithm

2020 ◽  
pp. 229-231
Author(s):  
Jenifa G ◽  
Yuvaraj N ◽  
SriPreethaa K R
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Monitoring System ◽  
Face Detection ◽  
Security System ◽  
Machine Learning Algorithms ◽  
Advanced Technology ◽  
Cascade Classifier ◽  
Home Security ◽  
Haar Cascade

Home security system plays a predominant role in the modern era. The purpose of the security systems is to protect the members of the family from intruders. The main idea behind this system is to provide security for residential areas. In today’s world securing our home takes a major role in the society. Surveillance from home to huge industries, plays a significant role in the fulfilment of our security. There are many machine learning algorithms for home security system but Haar-cascade classifier algorithm gives a better result when compared with other machine learning algorithm This system implements a face recognition and face detection using Haar-cascade classifier algorithm, OpenCV libraries are used for training and testing of the face detection process. In future, face recognition will be everywhere in the world. Face recognition is creating a magic in every field with its advanced technology. Visitor/Intruder monitoring system using Machine Learning is used to monitor the person and find whether the person is a known or unknown person from the captured picture. Here LBPH (Local Binary Pattern Histogram) Face Recognizer is used. After capturing the image, it is compared with the available dataset then their respective name and picture is sent to the specified email to alert the owner.

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