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Deep Learning Approach for Intelligent Intrusion Detection System
TLDR
A highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet is proposed which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks. Expand
Stock price prediction using LSTM, RNN and CNN-sliding window model
TLDR
This work uses three different deep learning architectures for the price prediction of NSE listed companies and compares their performance and applies a sliding window approach for predicting future values on a short term basis. Expand
Applying convolutional neural network for network intrusion detection
TLDR
This paper models network traffic as time-series, particularly transmission control protocol / internet protocol (TCP/IP) packets in a predefined time range with supervised learning methods such as multi-layer perceptron (MLP), CNN, CNN-recurrent neural network (CNN-RNN), CNN-long short-term memory ( CNN-LSTM) and CNN-gated recurrent unit (GRU), using millions of known good and bad network connections. Expand
Overview of the track on HASOC-Offensive Language Identification-DravidianCodeMix
TLDR
The results and main findings of the HASOC-Offensive Language Identification on code mixed Dravidian languages and the system submission and methods used by participants are presented. Expand
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security
TLDR
DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS) and it is concluded that a DNN of 3 layers has superior performance over all the other classical machine learning algorithms. Expand
Evaluating shallow and deep networks for ransomware detection and classification
TLDR
This paper evaluates shallow and deep networks for the detection and classification of ransomware, and finds that MLP has performed well in detecting and classifying ransomwares in comparison to the other classical machine learning classifiers. Expand
NSE Stock Market Prediction Using Deep-Learning Models
TLDR
Four types of deep learning architectures are used i.e Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for predicting the stock price of a company based on the historical prices available for day-wise closing price of two different stock markets. Expand
Applying deep learning approaches for network traffic prediction
TLDR
This work uses various RNN networks to leverage the efficacy of RNN approaches towards traffic matrix estimation in large networks, and finds LSTM has performed well in comparison to the other RNN and classical methods. Expand
Robust Intelligent Malware Detection Using Deep Learning
TLDR
A novelty in combining visualization and deep learning architectures for static, dynamic, and image processing-based hybrid approach applied in a big data environment is the first of its kind toward achieving robust intelligent zero-day malware detection. Expand
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