Malicious traffic detection combined deep neural network with hierarchical attention mechanism
نویسندگان
چکیده
منابع مشابه
Anomaly-based Web Attack Detection: The Application of Deep Neural Network Seq2Seq With Attention Mechanism
Today, the use of the Internet and Internet sites has been an integrated part of the people’s lives, and most activities and important data are in the Internet websites. Thus, attempts to intrude into these websites have grown exponentially. Intrusion detection systems (IDS) of web attacks are an approach to protect users. But, these systems are suffering from such drawbacks as low accuracy in ...
متن کاملDeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction
In this paper, we consider the temporal pattern in traffic flow time series, and implement a deep learning model for traffic flow prediction. Detrending based methods decompose original flow series into trend and residual series, in which trend describes the fixed temporal pattern in traffic flow and residual series is used for prediction. Inspired by the detrending method, we propose DeepTrend...
متن کاملEstimate Traffic with Combined Neural Network Approach
Many operating agencies are currently developing computerized freeway traffic management systems to support traffic operations as part of the Intelligent Transportation System (ITS) user service improvements. This study illustrates the importance of using simplified data analysis and presents a promising approach for improving demand prediction and traffic data modeling to support pro-active tr...
متن کاملPedestrian Detection with Deep Convolutional Neural Network
The problem of pedestrian detection in image and video frames has been extensively investigated in the past decade. However, the low performance in complex scenes shows that it remains an open problem. In this paper, we propose to cascade simple Aggregated Channel Features (ACF) and rich Deep Convolutional Neural Network (DCNN) features for efficient and effective pedestrian detection in comple...
متن کاملLearning Invariant Representation for Malicious Network Traffic Detection
Statistical learning theory relies on an assumption that the joint distributions of observations and labels are the same in training and testing data. However, this assumption is violated in many real world problems, such as training a detector of malicious network traffic that can change over time as a result of attacker’s detection evasion efforts. We propose to address this problem by creati...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Scientific Reports
سال: 2021
ISSN: 2045-2322
DOI: 10.1038/s41598-021-91805-z