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Rohan Benhal

Abstract: Machine learning-based (IDS) have become a critical component of safeguarding our economic and national security because of the massive quantities of data produced each day and the growing interconnection of the world's Internet infrastructures. The existing machine Learning Model technique may have difficulty comprehending the ever-increasingly complex distribution of data invasion patterns. With a small number of data points, a single deep learning algorithm may be ineffective at capturing different patterns for intrusive attacks. We presented CNN-LSTM Novel Intrusion Detection Model for Big Data to improve the efficiency of IDS-based CNN-LSTM even further (NIDM). NIDM uses behavioural traits and content functions to understand the characteristics when compared to earlier single learning model tactics, this strategy can improve the rate of intrusive attack detection. Keywords: IDS, Machine Learning, LSTM, CNN.

Dharmapriya M S

Abstract: In the 1950s, the concept of machine learning was discovered and developed as a subfield of artificial intelligence. However, there were no significant developments or research on it until this decade. Typically, this field of study has developed and expanded since the 1990s. It is a field that will continue to develop in the future due to the difficulty of analysing and processing data as the number of records and documents increases. Due to the increasing data, machine learning focuses on finding the best model for the new data that takes into account all the previous data. Therefore, machine learning research will continue in correlation with this increasing data. This research focuses on the history of machine learning, the methods of machine learning, its applications, and the research that has been conducted on this topic. Our study aims to give researchers a deeper understanding of machine learning, an area of research that is becoming much more popular today, and its applications. Keywords: Machine Learning, Machine Learning Algorithms, Artificial Intelligence, Big Data.

2022 ◽  
Vol 195 ◽  
pp. 103298
Sjoukje A. Osinga ◽  
Dilli Paudel ◽  
Spiros A. Mouzakitis ◽  
Ioannis N. Athanasiadis

2021 ◽  
Vol 96 ◽  
pp. 107533
SK Hafizul Islam ◽  
Nimish Mishra ◽  
Souvik Biswas ◽  
Bharat Keswani ◽  
Sherali Zeadally

2022 ◽  
Vol 139 ◽  
pp. 614-628
Liane W.Y. Lee ◽  
Piyush Sharma ◽  
Bradley R. Barnes
Big Data ◽  

Aakriti Shukla ◽  
Dr Damodar Prasad Tiwari ◽  

Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis of their limits. This study contributes to the development of a method for improving the efficiency of feature selection in big complicated data sets.

2021 ◽  
Jiarong Wang ◽  
Tian Yan ◽  
Dehai An ◽  
Zhongtian Liang ◽  
Chaoqi Guo ◽  

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