Botnet Detection in Software Defined Networks by Deep Learning Techniques

2018 ◽  
pp. 49-62 ◽  
Author(s):  
Ivan Letteri ◽  
Giuseppe Della Penna ◽  
Giovanni De Gasperis
Keyword(s):  
Deep Learning ◽  
Software Defined Networks ◽  
Botnet Detection ◽  
Learning Techniques

scholarly journals Deep Learning Techniques for Android Botnet Detection

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 519
Author(s):  
Suleiman Y. Yerima ◽  
Mohammed K. Alzaylaee ◽  
Annette Shajan ◽  
Vinod P
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
State Of The Art ◽  
Learning Models ◽  
Botnet Detection ◽  
Machine Learning Classifiers ◽  
Learning Techniques ◽  
Form Part ◽  
Android Applications ◽  
Evasive Malware

Android is increasingly being targeted by malware since it has become the most popular mobile operating system worldwide. Evasive malware families, such as Chamois, designed to turn Android devices into bots that form part of a larger botnet are becoming prevalent. This calls for more effective methods for detection of Android botnets. Recently, deep learning has gained attention as a machine learning based approach to enhance Android botnet detection. However, studies that extensively investigate the efficacy of various deep learning models for Android botnet detection are currently lacking. Hence, in this paper we present a comparative study of deep learning techniques for Android botnet detection using 6802 Android applications consisting of 1929 botnet applications from the ISCX botnet dataset. We evaluate the performance of several deep learning techniques including: CNN, DNN, LSTM, GRU, CNN-LSTM, and CNN-GRU models using 342 static features derived from the applications. In our experiments, the deep learning models achieved state-of-the-art results based on the ISCX botnet dataset and also outperformed the classical machine learning classifiers.

Anomaly Detection and Categorization in Cloud Environment using Deep Learning Techniques

2019 ◽  
Vol 7 (5) ◽  
pp. 211-214
Author(s):  
Nidhi Thakkar ◽  
Miren Karamta ◽  
Seema Joshi ◽  
M. B. Potdar
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Cloud Environment ◽  
Learning Techniques

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

Deep Learning Techniques for Koala Activity Detection

2018 ◽  
Author(s):  
Ivan Himawan ◽  
Michael Towsey ◽  
Bradley Law ◽  
Paul Roe
Keyword(s):  
Deep Learning ◽  
Activity Detection ◽  
Learning Techniques
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