scholarly journals IDS PROTOTYPE FOR INTRUSION DETECTION WITH MACHINE LEARNING MODELS IN IOT SYSTEMS OF THE INDUSTRY 4.0

10.6036/10011 ◽  
2021 ◽  
Vol 96 (3) ◽  
pp. 270-275
Author(s):  
JOSE AVELEIRA MATA ◽  
ANGEL LUIS MUÑOZ CASTAÑEDA ◽  
MARÍA TERESA GARCÍA ORDÁS ◽  
CARMEN BENAVIDES CUELLAR ◽  
José Alberto Benítez Andrades ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Industry 4.0 ◽  
Detection System ◽  
Remote Access ◽  
Security Requirements ◽  
Cloud Processing ◽  
Machine Learning Model ◽  
Great Growth

Industry 4.0 significantly improves productivity by collecting and analyzing data in real time. This, combined with remote access functions, and cloud processing that allows Internet of Things IoT, provides information that optimizes processes and decision support. Also involves a great growth of new networks and systems with special features, which mean that they are vulnerable to different attacks. So new security requirements are emerging in the IoT network. To improve the security of an IoT system for a transparent way, it is proposed the development of a prototype intrusion detection system IDS, which detects anomalies in IoT environments using the MQTT protocol (Message Queuing Telemetry Transport), widely used in IoT systems. For this purpose, it is generated a dataset of an IoT system in which perform different attacks on the MQTT protocol. This dataset is used to train a machine learning model, which is implemented in the IDS that captures the network frames in real time from the system to classify and detect the different attacks. Keywords: IoT, industry 4.0, cybersecurity, IDS, MQTT protocol, Machine Learning.

scholarly journals Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4736
Author(s):  
Sk. Tanzir Mehedi ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Kawsar Ahmed
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Transfer Learning ◽  
Security Requirements ◽  
Detection Accuracy ◽  
Area Network ◽  
Complex Data ◽  
Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

scholarly journals Internet of Things: A Comprehensive Study on Machine Learning-based Intrusion Detection approaches

2021 ◽  
Author(s):  
Priyanka Gupta ◽  
Lokesh Yadav ◽  
Deepak Singh Tomar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Real Time ◽  
Detection System ◽  
Research Work ◽  
Security And Privacy ◽  
Iot Security ◽  
Advantages And Disadvantages ◽  
Iot Devices

The Internet of Things (IoT) connects billions of interconnected devices that can exchange information with each other with minimal user intervention. The goal of IoT to become accessible to anyone, anytime, and anywhere. IoT has engaged in multiple fields, including education, healthcare, businesses, and smart home. Security and privacy issues have been significant obstacles to the widespread adoption of IoT. IoT devices cannot be entirely secure from threats; detecting attacks in real-time is essential for securing devices. In the real-time communication domain and especially in IoT, security and protection are the major issues. The resource-constrained nature of IoT devices makes traditional security techniques difficult. In this paper, the research work carried out in IoT Intrusion Detection System is presented. The Machine learning methods are explored to provide an effective security solution for IoT Intrusion Detection systems. Then discussed the advantages and disadvantages of the selected methodology. Further, the datasets used in IoT security are also discussed. Finally, the examination of the open issues and directions for future trends are also provided.

scholarly journals A Real-time LAN/WAN and Web Attack Prediction Framework Using Hybrid Machine Learning Model

2018 ◽  
Vol 7 (3.12) ◽  
pp. 1128
Author(s):  
Mohammad Arshad ◽  
Md. Ali Hussain
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Learning Model ◽  
Attack Detection ◽  
Buffer Size ◽  
Learning Classifier ◽  
Network Intrusion ◽  
Machine Learning Model ◽  
Tools And Techniques

Real-time network attacks have become an increasingly serious issue to LAN/WAN security in recent years. As the size of the network flow increases, it becomes difficult to pre-process and analyze the network packets using the traditional network intrusion detection tools and techniques. Traditional NID tools and techniques require high computational memory and time to process large number of packets in incremental manner due to limited buffer size. Web intrusion detection is also one of the major threat to real-time web applications due to unauthorized user’s request to web server and online databases. In this paper, a hybrid real-time LAN/WAN and Web IDS model is designed and implemented using the machine learning classifier. In this model, different types of attacks are detected and labelled prior to train the machine learning model. Future network packets are predicted using the trained machine learning classifier for attack prediction. Experimental results are simulated on real-time LAN/WAN network and client-server web application for performance analysis. Simulated results show that the proposed machine learning based attack detection model is better than the traditional statistical and rule based learning models in terms of time, detection rate are concerned.  

scholarly journals A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 219 ◽  
Author(s):  
Sweta Bhattacharya ◽  
Siva Rama Krishnan S ◽  
Praveen Kumar Reddy Maddikunta ◽  
Rajesh Kaluri ◽  
Saurabh Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Comprehensive Evaluation ◽  
Detection System ◽  
Human Life ◽  
Principal Component ◽  
Cyber Attacks ◽  
Classification Model ◽  
Learning Approaches ◽  
Machine Learning Model

The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

scholarly journals Feature Reduction using Lasso Hybrid Algorithm in Wireless Intrusion Detection System

2019 ◽  
Vol 8 (11) ◽  
pp. 1476-1483
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Vital Role ◽  
Feature Reduction ◽  
False Alarms ◽  
Security Breaches ◽  
Number Of False Alarms ◽  
Machine Learning Model

To maintain the integrity and protection of networks, intrusion detection systems play a vital role. Growth of wireless networks turned the globe to perform all pecuniary tasks online resulting a lot of security breaches in the network. One of the common breaches happening in network is the intruders who eventually tries to bypass the adopted security framework. Every day new intrusions arises and new solutions as well, however the research in making the intrusion detection system intelligent holds energetic. Today most of the systems are becoming intelligent by adopting machine learning and artificial intelligence algorithms. Success of building an efficient machine learning model to make intelligent intrusion detection system is relied on the effective features considered for classification and prediction. Thus, feature reduction is an integral part for discarding irrelevant and redundant features to produce a computationally decisive system that can identify defects with high accuracy. This implementation is an attempt to identify the smaller feature set possible for the well adopted wireless intrusion detection dataset AWID. Here, we proposed a LASSO based implementation to produce a smaller decisive set of features. Incorporation of Lasso on feature reduction not only provides a smaller set of features, but also allow to adopt prediction algorithms inside Lasso resulting lesser number of false alarms as well

Supervised Classifier Approach for Intrusion Detection on KDD with Optimal MapReduce Framework Model in Cloud Computing

2019 ◽  
Vol 12 ◽  
Author(s):  
M. Ilayaraja ◽  
S. Hemalatha ◽  
P. Manickam ◽  
K. Sathesh Kumar ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Learning Strategies ◽  
Nearest Neighbor ◽  
Detection System ◽  
K Nearest Neighbor ◽  
Mapreduce Model ◽  
The Web

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).

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