An Integrated Digital Authentication Mechanism for Intrusion Detection System

2019 ◽  
pp. 158-169 ◽  
Author(s):  
Ch Rupa
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Detection System ◽  
Network Connectivity ◽  
Advanced Technology ◽  
The Internet ◽  
Authentication Mechanism ◽  
The Internet Of Things ◽  
Day By Day ◽  
Exchange Data

The internet of things is the internetworking of physical devices, vehicles, buildings, and other items embedded with electronics, software, sensors, and network connectivity that enable these objects to collect and exchange data. Security has become an important issue everywhere. In current days, security is becoming necessary as the possibilities of attacks and threats are increasing day by day. In this situation, specific sensitive premises should monitor by a secure alert system with IoT-based advanced technology in order to prevent the threats and attacks on persons or system assets by intruders. The purpose of this system is to notify the use of the intruder's presence at premises and send alert messages to the authority people who help to take prevention actions as well as detection if in misfire situations. This notification will be helpful to know about intruder's presence even if right persons are away from the location.

scholarly journals Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1977 ◽  
Author(s):  
Geethapriya Thamilarasu ◽  
Shiven Chawla
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Cyber Attacks ◽  
The Internet ◽  
Critical Systems ◽  
The Internet Of Things

Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.

Basic Concepts of Internet of Things and Game Theory

2017 ◽  
pp. 1-12
Keyword(s):  
Internet Of Things ◽  
Network Connectivity ◽  
Modern Technology ◽  
The Internet ◽  
New Paradigm ◽  
Computer Communications ◽  
Basic Concepts ◽  
Industry Sectors ◽  
The Internet Of Things ◽  
Exchange Data

With the evolution of the Internet and related technologies, there has been an evolution of new paradigm, which is the Internet of Things (IoT). IoT is the network of physical objects, such as devices, embedded with electronics, software, sensors, and network connectivity that enables these objects to collect and exchange data. In the IoT, a large number of objects are connected to one another for information sharing, irrespective of their locations (Corcoran, 2016). Even though the IoT was defined at 1999, the concept of IoT has been in development for decades. As the technology and implementation of the IoT ideas move forward, different views for the concept of the IoT have appeared (Ma, 2011). Based on different views, in this book, the IoT is defined as a kind of modern technology, implicating machine to machine communications and person to computer communications will be extended to everything from everyday household objects to sensors monitoring the movement. Currently, we can see a few key areas of focus for the Internet of Things (IoT) that will require special attention over the course of the next decade on the part of computer science, energy technology, networks, wireless communication, and system platform. There are already a number of implementation case studies emerging from companies across a range of industry sectors.

scholarly journals An Intrusion Detection System for the Internet of Things Based on Machine Learning: Review and Challenges

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1011
Author(s):  
Ahmed Adnan ◽  
Abdullah Muhammed ◽  
Abdul Azim Abd Ghani ◽  
Azizol Abdullah ◽  
Fahrul Hakim
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Concept Drift ◽  
The Internet ◽  
Ongoing Research ◽  
Active Research ◽  
The Internet Of Things

An intrusion detection system (IDS) is an active research topic and is regarded as one of the important applications of machine learning. An IDS is a classifier that predicts the class of input records associated with certain types of attacks. In this article, we present a review of IDSs from the perspective of machine learning. We present the three main challenges of an IDS, in general, and of an IDS for the Internet of Things (IoT), in particular, namely concept drift, high dimensionality, and computational complexity. Studies on solving each challenge and the direction of ongoing research are addressed. In addition, in this paper, we dedicate a separate section for presenting datasets of an IDS. In particular, three main datasets, namely KDD99, NSL, and Kyoto, are presented. This article concludes that three elements of concept drift, high-dimensional awareness, and computational awareness that are symmetric in their effect and need to be addressed in the neural network (NN)-based model for an IDS in the IoT.

Intrusion Detection System Through Advance Machine Learning for the Internet of Things Networks

2021 ◽  
Vol 23 (2) ◽  
pp. 58-64
Author(s):  
Tanzila Saba ◽  
Tariq Sadad ◽  
Amjad Rehman ◽  
Zahid Mehmood ◽  
Qaisar Javaid
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
The Internet ◽  
The Internet Of Things

scholarly journals Application of Temperature Prediction Based on Neural Network in Intrusion Detection of IoT

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xuefei Liu ◽  
Chao Zhang ◽  
Pingzeng Liu ◽  
Maoling Yan ◽  
Baojia Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Defense Mechanism ◽  
Detection System ◽  
The Internet ◽  
Adaptive Capabilities ◽  
The Neural Network ◽  
The Internet Of Things

The security of network information in the Internet of Things faces enormous challenges. The traditional security defense mechanism is passive and certain loopholes. Intrusion detection can carry out network security monitoring and take corresponding measures actively. The neural network-based intrusion detection technology has specific adaptive capabilities, which can adapt to complex network environments and provide high intrusion detection rate. For the sake of solving the problem that the farmland Internet of Things is very vulnerable to invasion, we use a neural network to construct the farmland Internet of Things intrusion detection system to detect anomalous intrusion. In this study, the temperature of the IoT acquisition system is taken as the research object. It has divided which into different time granularities for feature analysis. We provide the detection standard for the data training detection module by comparing the traditional ARIMA and neural network methods. Its results show that the information on the temperature series is abundant. In addition, the neural network can predict the temperature sequence of varying time granularities better and ensure a small prediction error. It provides the testing standard for the construction of an intrusion detection system of the Internet of Things.

scholarly journals Intrusion Detection into Cloud-Fog-Based IoT Networks Using Game Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Poria Pirozmand ◽  
Mohsen Angoraj Ghafary ◽  
Safieh Siadat ◽  
Jiankang Ren
Keyword(s):  
Game Theory ◽  
Nash Equilibrium ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Detection System ◽  
Human Life ◽  
Intrusion Detection Systems ◽  
The Internet ◽  
Detection Systems ◽  
The Internet Of Things

The Internet of Things is an emerging technology that integrates the Internet and physical smart objects. This technology currently is used in many areas of human life, including education, agriculture, medicine, military and industrial processes, and trade. Integrating real-world objects with the Internet can pose security threats to many of our day-to-day activities. Intrusion detection systems (IDS) can be used in this technology as one of the security methods. In intrusion detection systems, early and correct detection (with high accuracy) of intrusions is considered very important. In this research, game theory is used to develop the performance of intrusion detection systems. In the proposed method, the attacker infiltration mode and the behavior of the intrusion detection system as a two-player and nonparticipatory dynamic game are completely analyzed and Nash equilibrium solution is used to create specific subgames. During the simulation performed using MATLAB software, various parameters were examined using the definitions of game theory and Nash equilibrium to extract the parameters that had the most accurate detection results. The results obtained from the simulation of the proposed method showed that the use of intrusion detection systems in the Internet of Things based on cloud-fog can be very effective in identifying attacks with the least amount of errors in this network.

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