DBD: Deep Learning DGA-Based Botnet Detection

2019 ◽  
pp. 127-149 ◽  
Author(s):  
R. Vinayakumar ◽  
K. P. Soman ◽  
Prabaharan Poornachandran ◽  
Mamoun Alazab ◽  
Alireza Jolfaei
Keyword(s):  
Deep Learning ◽  
Botnet Detection

Botnet detection based on network flow summary and deep learning

2018 ◽  
Vol 28 (6) ◽  
pp. e2039 ◽  
Author(s):  
Abdurrahman Pektaş ◽  
Tankut Acarman
Keyword(s):  
Deep Learning ◽  
Network Flow ◽  
Botnet Detection

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.

scholarly journals Leveraging Image Representation of Network Traffic Data and Transfer Learning in Botnet Detection

2018 ◽  
Vol 2 (4) ◽  
pp. 37 ◽  
Author(s):  
Shayan Taheri ◽  
Milad Salem ◽  
Jiann-Shiun Yuan
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Network Traffic ◽  
Denial Of Service ◽  
Security And Privacy ◽  
Support Vector ◽  
The Internet ◽  
Traffic Data ◽  
Botnet Detection ◽  
Detection Techniques

The advancements in the Internet has enabled connecting more devices into this technology every day. The emergence of the Internet of Things has aggregated this growth. Lack of security in an IoT world makes these devices hot targets for cyber criminals to perform their malicious actions. One of these actions is the Botnet attack, which is one of the main destructive threats that has been evolving since 2003 into different forms. This attack is a serious threat to the security and privacy of information. Its scalability, structure, strength, and strategy are also under successive development, and that it has survived for decades. A bot is defined as a software application that executes a number of automated tasks (simple but structurally repetitive) over the Internet. Several bots make a botnet that infects a number of devices and communicates with their controller called the botmaster to get their instructions. A botnet executes tasks with a rate that would be impossible to be done by a human being. Nowadays, the activities of bots are concealed in between the normal web flows and occupy more than half of all web traffic. The largest use of bots is in web spidering (web crawler), in which an automated script fetches, analyzes, and files information from web servers. They also contribute to other attacks, such as distributed denial of service (DDoS), SPAM, identity theft, phishing, and espionage. A number of botnet detection techniques have been proposed, such as honeynet-based and Intrusion Detection System (IDS)-based. These techniques are not effective anymore due to the constant update of the bots and their evasion mechanisms. Recently, botnet detection techniques based upon machine/deep learning have been proposed that are more capable in comparison to their previously mentioned counterparts. In this work, we propose a deep learning-based engine for botnet detection to be utilized in the IoT and the wearable devices. In this system, the normal and botnet network traffic data are transformed into image before being given into a deep convolutional neural network, named DenseNet with and without considering transfer learning. The system is implemented using Python programming language and the CTU-13 Dataset is used for evaluation in one study. According to our simulation results, using transfer learning can improve the accuracy from 33.41% up to 99.98%. In addition, two other classifiers of Support Vector Machine (SVM) and logistic regression have been used. They showed an accuracy of 83.15% and 78.56%, respectively. In another study, we evaluate our system by an in-house live normal dataset and a solely botnet dataset. Similarly, the system performed very well in data classification in these studies. To examine the capability of our system for real-time applications, we measure the system training and testing times. According to our examination, it takes 0.004868 milliseconds to process each packet from the network traffic data during testing.

A Visualized Botnet Detection System Based Deep Learning for the Internet of Things Networks of Smart Cities

2020 ◽  
Vol 56 (4) ◽  
pp. 4436-4456 ◽  
Author(s):  
R. Vinayakumar ◽  
Mamoun Alazab ◽  
Sriram Srinivasan ◽  
Quoc-Viet Pham ◽  
Soman Kotti Padannayil ◽  
...  
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Detection System ◽  
Smart Cities ◽  
The Internet ◽  
Botnet Detection ◽  
The Internet Of Things

A Closeness Centrality Based P2P Botnet Detection Approach Using Deep Learning

2021 ◽  
Author(s):  
Tirthankar Sengupta ◽  
Sanghamitra De ◽  
Indrajit Banerjee
Keyword(s):  
Deep Learning ◽  
Closeness Centrality ◽  
Botnet Detection ◽  
Detection Approach
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