Deep Learning-Based Big Data-Assisted Anomaly Detection in Cellular Networks

2018 ◽  
Author(s):  
Bilal Hussain ◽  
Qinghe Du ◽  
Pinyi Ren
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Cellular Networks

scholarly journals Big Data Driven Anomaly Detection for Cellular Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 31398-31408
Author(s):  
Qiqi Zhu ◽  
Li Sun
Keyword(s):  
Big Data ◽  
Anomaly Detection ◽  
Cellular Networks ◽  
Data Driven

Big Data Analytics With Machine Learning and Deep Learning Methods for Detection of Anomalies in Network Traffic

2020 ◽  
pp. 317-346 ◽  
Author(s):  
Valliammal Narayan ◽  
Shanmugapriya D.
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Traffic Flow ◽  
Cyber Security ◽  
Learning Algorithms ◽  
Cyber Attacks ◽  
Structured Data ◽  
Cyber Attack ◽  
Imbalance Problem

Information is vital for any organization to communicate through any network. The growth of internet utilization and the web users increased the cyber threats. Cyber-attacks in the network change the traffic flow of each system. Anomaly detection techniques have been developed for different types of cyber-attack or anomaly strategies. Conventional ADS protect information transferred through the network or cyber attackers. The stable prevention of anomalies by machine and deep-learning algorithms are applied for cyber-security. Big data solutions handle voluminous data in a short span of time. Big data management is the organization and manipulation of huge volumes of structured data, semi-structured data and unstructured data, but it does not handle a data imbalance problem during the training process. Big data-based machine and deep-learning algorithms for anomaly detection involve the classification of decision boundary between normal traffic flow and anomaly traffic flow. The performance of anomaly detection is efficiently increased by different algorithms.

Anomaly detection for cellular networks using big data analytics

2019 ◽  
Vol 13 (20) ◽  
pp. 3351-3359
Author(s):  
Bing Li ◽  
Shengjie Zhao ◽  
Rongqing Zhang ◽  
Qingjiang Shi ◽  
Kai Yang
Keyword(s):  
Big Data ◽  
Anomaly Detection ◽  
Cellular Networks ◽  
Data Analytics ◽  
Big Data Analytics

Big Data Analytics With Machine Learning and Deep Learning Methods for Detection of Anomalies in Network Traffic

2022 ◽  
pp. 678-707
Author(s):  
Valliammal Narayan ◽  
Shanmugapriya D.
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Traffic Flow ◽  
Cyber Security ◽  
Learning Algorithms ◽  
Cyber Attacks ◽  
Structured Data ◽  
Cyber Attack ◽  
Imbalance Problem

Information is vital for any organization to communicate through any network. The growth of internet utilization and the web users increased the cyber threats. Cyber-attacks in the network change the traffic flow of each system. Anomaly detection techniques have been developed for different types of cyber-attack or anomaly strategies. Conventional ADS protect information transferred through the network or cyber attackers. The stable prevention of anomalies by machine and deep-learning algorithms are applied for cyber-security. Big data solutions handle voluminous data in a short span of time. Big data management is the organization and manipulation of huge volumes of structured data, semi-structured data and unstructured data, but it does not handle a data imbalance problem during the training process. Big data-based machine and deep-learning algorithms for anomaly detection involve the classification of decision boundary between normal traffic flow and anomaly traffic flow. The performance of anomaly detection is efficiently increased by different algorithms.

scholarly journals Artificial Intelligence for anomaly detection by employing deep learning strategies in IoT networks using the trendy IoT-23 big data from Google's Tensorflow2.2

2021 ◽  
Author(s):  
Kanimozhi V ◽  
T. Prem Jacob
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Multilayer Perceptron ◽  
Network Traffic ◽  
Recurrent Neural Networks ◽  
Learning Models ◽  
Research Article

Abstract Although numerous profound learning models have been proposed, this research article contributed to symbolize the investigation of artificial deep learning models on sensible IoT gadgets to perform online protection in IoT network traffic by using the realistic IoT-23 dataset. This dataset is a recent network traffic dataset generated from the real-time network traffic data of IoT appliances. IoT products are utilized in various program applications such as home, commercial, mechanization, and various forms of wearable technologies. IoT security is more critical than network security because of its massive attack surface and multiplied weak spots of IoT gadgets. Universally, the general amount of IoT gadgets conveyed by 2025 is foreseen to achieve 41600 million. Henceforth, IoT anomaly detection systems based on the realistic Iot-23 big data for detecting IoT-based attacks on the artificial neural networks of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Multilayer perceptron (MLP) in IoT- cybersecurity has implemented and executed in this research article. As a result, Convolutional Neural Networks produces an outstanding performance of metric accuracy score is 0.998234, and minimal loss function is 0.008842, compare to Multilayer perceptron and Recurrent Neural Networks in IoT Anomaly Detection. Also generated well-displayed graph plots of Model_Accuracy, Learning curve of artificial Intelligence deep learning algorithms such as MLP, CNN, and RNN.

scholarly journals Anomaly detection optimization using big data and deep learning to reduce false-positive

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Khloud Al Jallad ◽  
Mohamad Aljnidi ◽  
Mohammad Said Desouki
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Anomaly Detection ◽  
False Positive

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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