Botnet Detection Using Recurrent Variational Autoencoder
Botnets are increasingly used by malicious actors, creating increasing threat to a large number of internet users. To address this growing danger, we propose to study methods to detect botnets, especially those that are hard to capture with the commonly used methods, such as the signature based ones and the existing anomaly-based ones. More specifically, we propose a novel machine learning based method, named Recurrent Variational Autoencoder (RVAE), for detecting botnets through sequential characteristics of network traffic flow data including attacks by botnets. We validate robustness of our method with the CTU-13 dataset, where we have chosen the testing dataset to have different types of botnets than those of training dataset. Tests show that RVAE is able to detect botnets with the same accuracy as the best known results published in literature. In addition, we propose an approach to assign anomaly score based on probability distributions, which allows us to detect botnets in streaming mode as the new networking statistics becomes available. This on-line detection capability would enable real-time detection of unknown botnets.
Code (0)
등록된 구현이 없습니다.
Tasks
Line DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Improving Botnet Detection with Recurrent Neural Network and Transfer Learning
Botnet detection is a critical step in stopping the spread of botnets and preventing malicious activities. However, reliable detection is still a challenging task, due to a wide variety of botnets involving ever-increasi…
Transfer LearningA Quantized VAE-MLP Botnet Detection Model: A Systematic Evaluation of Quantization-Aware Training and Post-Training Quantization Strategies
In an effort to counter the increasing IoT botnet-based attacks, state-of-the-art deep learning methods have been proposed and have achieved impressive detection accuracy. However, their computational intensity restricts…
Model CompressionEnhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment
Cyberattacks in an Internet of Things (IoT) environment can have significant impacts because of the interconnected nature of devices and systems. An attacker uses a network of compromised IoT devices in a botnet attack t…
N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders
The proliferation of IoT devices which can be more easily compromised than desktop computers has led to an increase in the occurrence of IoT based botnet attacks. In order to mitigate this new threat there is a need to d…
Anomaly DetectionHow the Graph Construction Technique Shapes Performance in IoT Botnet Detection
The increasing incidence of IoT-based botnet attacks has driven interest in advanced learning models for detection. Recent efforts have focused on leveraging attention mechanisms to model long-range feature dependencies …
Graph Generation