scholarly journals A Self-Adaptive and Self-Learning Methodology for Wireless Intrusion Detection using Deep Neural Network

2021 ◽  
Vol 12 (6) ◽  
pp. 2084-2094
Author(s):  
R. Sathya Et al.
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Cyber Physical Systems ◽  
Cyber Attacks ◽  
Detection Mechanism ◽  
Vast Number ◽  
Physical Systems ◽  
Computation Process ◽  
Self Learning

Cyber physical systems combine both the physical as well as the computation process. Embedded computers and systems monitor to control the physical forms with feedback loops which have an effect on computations and contrariwise. A vast number of failures and cyber-attacks are present in the cyber physical systems, which leads to a limited growth and accuracy in the intrusion detection system and thus implementing the suitable actions which may be taken to reduce the damage to the system. As Cyber-physical systems square measure but to be made public universally, the applying of the instruction detection mechanism remains open presently. As a result, the inconvenience is made to talk about the way to suitably apply the interruption location component to Cyber physical frameworks amid this paper. By analysing the unmistakable properties of Cyber-physical frameworks, it extraordinary to diagram the exact necessities 1st. At that point, the arranging characterize of the intrusion discovery component in Cyber-physical frameworks is introduced in terms of the layers of framework and particular location procedures. At long last, a few imperative investigation issues unit known for edifying the following considers.

scholarly journals Scrutinizing Attacks and Evaluating Performance Appraisal Parameters via Feature Selection in Intrusion Detection System

2021 ◽  
Author(s):  
Navroop Kaur ◽  
Meenakshi Bansal ◽  
Sukhwinder Singh S
Keyword(s):  
Feature Selection ◽  
Performance Evaluation ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Evaluation Parameters

Abstract In modern times the firewall and antivirus packages are not good enough to protect the organization from numerous cyber attacks. Computer IDS (Intrusion Detection System) is a crucial aspect that contributes to the success of an organization. IDS is a software application responsible for scanning organization networks for suspicious activities and policy rupturing. IDS ensures the secure and reliable functioning of the network within an organization. IDS underwent huge transformations since its origin to cope up with the advancing computer crimes. The primary motive of IDS has been to augment the competence of detecting the attacks without endangering the performance of the network. The research paper elaborates on different types and different functions performed by the IDS. The NSL KDD dataset has been considered for training and testing. The seven prominent classifiers LR (Logistic Regression), NB (Naïve Bayes), DT (Decision Tree), AB (AdaBoost), RF (Random Forest), kNN (k Nearest Neighbor), and SVM (Support Vector Machine) have been studied along with their pros and cons and the feature selection have been imposed to enhance the reading of performance evaluation parameters (Accuracy, Precision, Recall, and F1Score). The paper elaborates a detailed flowchart and algorithm depicting the procedure to perform feature selection using XGB (Extreme Gradient Booster) for four categories of attacks: DoS (Denial of Service), Probe, R2L (Remote to Local Attack), and U2R (User to Root Attack). The selected features have been ranked as per their occurrence. The implementation have been conducted at five different ratios of 60-40%, 70-30%, 90-10%, 50-50%, and 80-20%. Different classifiers scored best for different performance evaluation parameters at different ratios. NB scored with the best Accuracy and Recall values. DT and RF consistently performed with high accuracy. NB, SVM, and kNN achieved good F1Score.

SwiftIDS: Real-time intrusion detection system based on LightGBM and parallel intrusion detection mechanism

2020 ◽  
Vol 97 ◽  
pp. 101984 ◽  
Author(s):  
Dongzi Jin ◽  
Yiqin Lu ◽  
Jiancheng Qin ◽  
Zhe Cheng ◽  
Zhongshu Mao
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Mechanism

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.

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