Filters








4,130 Hits in 6.0 sec

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

Andrew Churcher, Rehmat Ullah, Jawad Ahmad, Sadaqat ur Rehman, Fawad Masood, Mandar Gogate, Fehaid Alqahtani, Boubakr Nour, William J. Buchanan
2021 Sensors  
In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset.  ...  However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.  ...  IoT Intrusion Detection Using Machine Learning ML is a subset of AI that involves giving an algorithm or in this case a model a dataset which will be used to identify patterns that can be used to make  ... 
doi:10.3390/s21020446 pmid:33435202 fatcat:bxj2qzl3pvf4phv75hpnzj3uei

A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks [article]

Poornima Mahadevappa, Syeda Mariam Muzammal, Raja Kumar Murugesan
2021 arXiv   pre-print
In this paper, a comparative analysis of conventional machine learning classification algorithms has been performed to categorize the network traffic on NSL-KDD dataset using Jupyter on Pycharm tool.  ...  The ML algorithms are used to classify the network traffic into normal and malicious attacks. Intrusion detection is one of the challenging issues in the area of network security.  ...  EXPERIMENTAL SETUP The Jupyter on Pycharm tool has been used to perform the comparative analysis using the NSL-KDD dataset, a benchmark for modern-day traffic.  ... 
arXiv:2111.01383v1 fatcat:nihfvanyazc2ra6puhkrpntoki

EADA: An Algorithm for Early Detection of Attacks on IoT Resources

2021 International Journal of Advanced Trends in Computer Science and Engineering  
This paper presents an algorithm for early detection of attacks on IoT resources through use of predictive descriptor tables.  ...  Effectiveness of the proposed algorithm is evaluated through experimental setup built using Google cloud platform.  ...  This makes use of SQL for training and evaluating machine learning models though numbers of algorithms are limited.  ... 
doi:10.30534/ijatcse/2021/161012021 fatcat:4k4nr7fxh5hsvooahao7mdh4cq

Malicious Traffic Flow Detection in IOT Using Ml Based Algorithms

Sri Vigna Hema V, Devadharshini S, Gowsalya P
2021 International Research Journal on Advanced Science Hub  
So, for a security to this network various machine learning algorithms (ML) has been introduced by various analyst to avoid this flow of error in the network.  ...  Identifying the malicious traffic flows in Internet of things (IOT) is very important to monitor and avoid unwanted errors or the unwanted flows in the network.  ...  The accuracy metric is the most optimal for the classification of network traffic using machine learning technique.  ... 
doi:10.47392/irjash.2021.142 fatcat:pzmbtwjc2ffdplrxise6ubwa2u

Acquiring Data Traffic for Sustainable IoT and Smart Devices Using Machine Learning Algorithm

Yi Huang, Shah Nazir, Xinqiang Ma, Shiming Kong, Youyuan Liu, Muhammad Ahmad
2021 Security and Communication Networks  
Keeping in view the security consideration of data traffic for smart devices and IoT, the proposed study presented machine learning algorithms for securing the data traffic based on a firewall for smart  ...  The experimental results of the approach show that the hybrid deep learning model (based on convolution neural network and support vector machine) outperforms than decision1 rules and random forest by  ...  Keeping in view the security consideration of data traffic for smart cities and IoT, the proposed study presented machine learning algorithms for securing the data traffic based on a firewall for smart  ... 
doi:10.1155/2021/1852466 fatcat:na43fpbgovcqnpv6s7zitlkr4m

An Algorithm for Detection of Traffic Attribute Exceptions Based on Cluster Algorithm in Industrial Internet of Things

Lidong Fu, Wenbo Zhang, Xiaobo Tan, Hongbo Zhu
2021 IEEE Access  
The clustering algorithm used for abnormal traffic detection in the industrial IoT is generally a specific machine learning algorithm, which marks the cluster as normal or abnormal according to the distribution  ...  DEFINITION OF COMPLEX ATTRIBUTE FEATURES CLUSTERING The clustering algorithm is an unsupervised machine learning algorithm.  ...  The simulation experiment demonstrates that the anomaly detection method based on security clustering has higher detection accuracy and efficiency than other algorithms.  ... 
doi:10.1109/access.2021.3068756 fatcat:etwdvcwnr5htlajuxb6t7knota

Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

Shilpa P. Khedkar, R. Aroul Canessane, Moslem Lari Najafi, VIMAL SHANMUGANATHAN
2021 Wireless Communications and Mobile Computing  
In this paper, a complete overview of IoT traffic forecasting model using classic time series and artificial neural network is presented. For prediction of IoT traffic, real network traces are used.  ...  An IoT is the communication of sensing devices linked to the Internet in order to communicate data. IoT devices have extremely critical reliability with an efficient and robust network condition.  ...  Machine learning algorithms are employed for identifying and classifying IoT devices. Artificial intelligence (AI) is used in the prediction of network traffic for data networking.  ... 
doi:10.1155/2021/5366222 fatcat:s3eu62dntveqvptdob6udz7spe

A State of the Art Survey of Machine Learning Algorithms for IoT Security

Alan Fuad Jahwar, Subhi R. M. Zeebaree
2021 Asian Journal of Research in Computer Science  
In this review, we focus on recent Machine Learning (ML) and Deep Learning (DL) algorithms proposed in IoT security, which can be used to address various security issues.  ...  Finally, we discuss the latest ML and DL strategies for solving various security issues in IoT networks.  ...  M [109] Classification of IoT networks using SVM. This classification aims to distinguish malicious from regular traffic.  ... 
doi:10.9734/ajrcos/2021/v9i430226 fatcat:f7vsso3saregjn462cyel2kukm

Prediction of DDoS Attacksusing Machine Learning and Deep Learning Algorithms

2019 International journal of recent technology and engineering  
With the emergence of network-based computing technologies like Cloud Computing, Fog Computing and IoT (Internet of Things), the context of digitizing the confidential data over the network is being adopted  ...  The DDoS attack is the most significant network-based attack in the domain of computer security that disrupts the internet traffic of the target server.  ...  Initially, malware is injected to the computer machines over the network which in turn transform each machine as an intruder through which the targeted server accessed using its IP address or Network traffic  ... 
doi:10.35940/ijrte.d8162.118419 fatcat:yrug2c32cfge3go6fatnpp2y2a

Ping Flood Attack Pattern Recognition Using a K-Means Algorithm in an Internet of Things (IoT) Network

Deris Stiawan, Meilinda Eka Suryani, Susanto, Mohd Yazid Idris, Muawya N. Aldalaien, Nizar Alsharif, Rahmat Budiarto
2021 IEEE Access  
Each scenario created an associated dataset. The datasets were then grouped into two clusters: normal and attack. The K-Means algorithm was used to produce the clustering results.  ...  Denial of service (DoS) is the most popular method used to attack IoT networks, either by flooding services or crashing services.  ...  [35] discussed an analysis of threats on an IoT network.  ... 
doi:10.1109/access.2021.3105517 fatcat:alige2yrurfcrpyume37ij7vwy

Analysis of Eight Data Mining Algorithms for Smarter Internet of Things (IoT)

Furqan Alam, Rashid Mehmood, Iyad Katib, Aiiad Albeshri
2016 Procedia Computer Science  
These include, among others, the deep learning artificial neural networks (DLANNs), which build a feed forward multi-layer artificial neural network (ANN) for modelling high-level data abstractions.  ...  In this paper, we examine the applicability of eight well-known data mining algorithms for IoT data.  ...  Machine learning is among the top methods to gain hidden insights from IoT data.  ... 
doi:10.1016/j.procs.2016.09.068 fatcat:wnvm2yzk5bhljo4eryjhzjgegu

GOAMLP: Network Intrusion Detection with Multilayer Perceptron and Grasshopper Optimization Algorithm

Shadi Moghanian, Farshid B. Saravi, Giti Javidi, Ehsan O. Sheybani
2020 IEEE Access  
In this paper, an intrusion detection system is introduced that uses data mining and machine learning concepts to detect network intrusion patterns.  ...  In the proposed method, an artificial neural network (ANN) is used as a learning technique in intrusion detection.  ...  She is an IEEE member.  ... 
doi:10.1109/access.2020.3040740 fatcat:jp5s5eotlbhtlhxaus5re2mp5u

Intrusion Detection System to Advance Internet of Things Infrastructure-Based Deep Learning Algorithms

Hasan Alkahtani, Theyazn H. H. Aldhyani, M. Irfan Uddin
2021 Complexity  
The obtained features were processed using deep learning algorithms.  ...  The experimental results confirmed that the proposed framework based on deep learning algorithms for an intrusion detection system can effectively detect real-world attacks and is capable of enhancing  ...  Combined CNN-LSTM Network. We proposed combining two advanced deep learning algorithms to detect intrusion from an IoT network dataset.  ... 
doi:10.1155/2021/5579851 fatcat:prxygr6phrhh7hl4o2tbbrdnka

DDOS Detection on Internet of Things Using Unsupervised Algorithms

Victor Odumuyiwa, Rukayat Alabi
2021 Journal of Cyber Security and Mobility  
This paper focusses on detecting DDOS attack in IOT networks by classifying incoming network packets on the transport layer as either "Suspicious" or "Benign" using unsupervised machine learning algorithms  ...  Mirai, BASHLITE and CICDDOS2019 datasets were used in training the algorithms during the experimentation phase.  ...  Recently, the awareness of using machine learning to secure network has increased rapidly.  ... 
doi:10.13052/jcsm2245-1439.1034 fatcat:x3mwq7svxjhpdkqsoerb72dpee

Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things

Abdullah Alharbi, Wael Alosaimi, Hashem Alyami, Hafiz Tayyab Rauf, Robertas Damaševičius
2021 Electronics  
The proposed LGBA-NN algorithm was tested on an N-BaIoT data set with extensive real traffic data with benign and malicious target classes.  ...  Instant detection facilitates network security by speeding up warning and disconnection from the network of infected IoT devices, thereby preventing the botnet from propagating and thereby stopping additional  ...  Data Availability Statement: The data set used for this study is publicly available at [68] . Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/electronics10111341 fatcat:dlxuqf7qyjchrbwzqasxrlllv4
« Previous Showing results 1 — 15 out of 4,130 results