Detecting Payment Fraud Using Automatic Feature Engineering with Harris Grey Wolf Deep Neural Network

2022 ◽  
pp. 71-80
Author(s):  
Chandra Sekhar Kolli ◽  
Mohan Kumar Ch ◽  
Ganeshan Ramasamy ◽  
Gogineni Krishna Chaitanya
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Feature Engineering ◽  
Grey Wolf

scholarly journals Improved Deep Neural Network for OFDM Signal Recognition Using Hybrid Grey Wolf Optimization

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 133622-133632
Author(s):  
Yan Zhang ◽  
Dejun Liu ◽  
Jialin Liu ◽  
Yixuan Xian ◽  
Xu Wang
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Signal Recognition ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Ofdm Signal

scholarly journals RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

2021 ◽  
Author(s):  
Shubo Yang ◽  
Yang Luo ◽  
Wang Miao ◽  
Changhao Ge ◽  
Wenjian Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Deep Neural Network ◽  
Feature Engineering ◽  
Network Approach ◽  
Neural Network Approach ◽  
Aerial Vehicles ◽  
Accurate Detection ◽  
Rf Signal

With the proliferation of Unmanned Aerial Vehicles (UAVs) to provide diverse critical services, the accurate detection of these small devices and the efficient classification of their flight modes are of paramount importance. In this paper, we propose a joint Feature Engineering Generator (FEG) and Multi-Channel Deep Neural Network (MC-DNN) approach.

scholarly journals Dynamic Malware Analysis with Feature Engineering and Feature Learning

2020 ◽  
Vol 34 (01) ◽  
pp. 1210-1217
Author(s):  
Zhaoqi Zhang ◽  
Panpan Qi ◽  
Wei Wang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Expert Knowledge ◽  
Malware Detection ◽  
Feature Representation ◽  
Feature Engineering ◽  
Malware Analysis ◽  
Neural Network Architecture

Dynamic malware analysis executes the program in an isolated environment and monitors its run-time behaviour (e.g. system API calls) for malware detection. This technique has been proven to be effective against various code obfuscation techniques and newly released (“zero-day”) malware. However, existing works typically only consider the API name while ignoring the arguments, or require complex feature engineering operations and expert knowledge to process the arguments. In this paper, we propose a novel and low-cost feature extraction approach, and an effective deep neural network architecture for accurate and fast malware detection. Specifically, the feature representation approach utilizes a feature hashing trick to encode the API call arguments associated with the API name. The deep neural network architecture applies multiple Gated-CNNs (convolutional neural networks) to transform the extracted features of each API call. The outputs are further processed through bidirectional LSTM (long-short term memory networks) to learn the sequential correlation among API calls. Experiments show that our solution outperforms baselines significantly on a large real dataset. Valuable insights about feature engineering and architecture design are derived from the ablation study.

scholarly journals RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1678
Author(s):  
Shubo Yang ◽  
Yang Luo ◽  
Wang Miao ◽  
Changhao Ge ◽  
Wenjian Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Moving Average ◽  
Feature Engineering ◽  
Neural Network Approach ◽  
Accurate Detection ◽  
Channel Input ◽  
Rf Signals ◽  
Frequency Components ◽  
Rf Signal

With the proliferation of Unmanned Aerial Vehicles (UAVs) to provide diverse critical services, such as surveillance, disaster management, and medicine delivery, the accurate detection of these small devices and the efficient classification of their flight modes are of paramount importance to guarantee their safe operation in our sky. Among the existing approaches, Radio Frequency (RF) based methods are less affected by complex environmental factors. The similarities between UAV RF signals and the diversity of frequency components make accurate detection and classification a particularly difficult task. To bridge this gap, we propose a joint Feature Engineering Generator (FEG) and Multi-Channel Deep Neural Network (MC-DNN) approach. Specifically, in FEG, data truncation and normalization separate different frequency components, the moving average filter reduces the outliers in the RF signal, and the concatenation fully exploits the details of the dataset. In addition, the multi-channel input in MC-DNN separates multiple frequency components and reduces the interference between them. A novel dataset that contains ten categories of RF signals from three types of UAVs is used to verify the effectiveness. Experiments show that the proposed method outperforms the state-of-the-art UAV detection and classification approaches in terms of 98.4% and F1 score of 98.3%.

scholarly journals RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

2021 ◽  
Author(s):  
Shubo Yang ◽  
Yang Luo ◽  
Wang Miao ◽  
Changhao Ge ◽  
Wenjian Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Deep Neural Network ◽  
Feature Engineering ◽  
Network Approach ◽  
Neural Network Approach ◽  
Aerial Vehicles ◽  
Accurate Detection ◽  
Rf Signal

With the proliferation of Unmanned Aerial Vehicles (UAVs) to provide diverse critical services, the accurate detection of these small devices and the efficient classification of their flight modes are of paramount importance. In this paper, we propose a joint Feature Engineering Generator (FEG) and Multi-Channel Deep Neural Network (MC-DNN) approach.

scholarly journals A review of deep-neural automated essay scoring models

2021 ◽  
Author(s):  
Masaki Uto
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Main Idea ◽  
Feature Engineering ◽  
Automated Essay Scoring ◽  
Comprehensive Review ◽  
The Past ◽  
Comprehensive Survey ◽  
Essay Scoring ◽  
Different Characteristics

AbstractAutomated essay scoring (AES) is the task of automatically assigning scores to essays as an alternative to grading by humans. Although traditional AES models typically rely on manually designed features, deep neural network (DNN)-based AES models that obviate the need for feature engineering have recently attracted increased attention. Various DNN-AES models with different characteristics have been proposed over the past few years. To our knowledge, however, no study has provided a comprehensive review of DNN-AES models while introducing each model in detail. Therefore, this review presents a comprehensive survey of DNN-AES models, describing the main idea and detailed architecture of each model. We classify the AES task into four types and introduce existing DNN-AES models according to this classification.

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