Payload-Based Web Attack Detection Using Deep Neural Network

2017 ◽  
pp. 482-488 ◽  
Author(s):  
Xiaohui Jin ◽  
Baojiang Cui ◽  
Jun Yang ◽  
Zishuai Cheng
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Attack Detection

Network Attack Detection With SNMP-MIB Using Deep Neural Network

2020 ◽  
pp. 66-76
Author(s):  
Mouhammd Sharari Alkasassbeh ◽  
Mohannad Zead Khairallah
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Information Technologies ◽  
Deep Neural Network ◽  
Detection System ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Network Attack ◽  
Security Breaches ◽  
Security Issues

Over the past decades, the Internet and information technologies have elevated security issues due to the huge use of networks. Because of this advance information and communication and sharing information, the threats of cybersecurity have been increasing daily. Intrusion Detection System (IDS) is considered one of the most critical security components which detects network security breaches in organizations. However, a lot of challenges raise while implementing dynamics and effective NIDS for unknown and unpredictable attacks. Consider the machine learning approach to developing an effective and flexible IDS. A deep neural network model is proposed to increase the effectiveness of intrusions detection system. This chapter presents an efficient mechanism for network attacks detection and attack classification using the Management Information Base (MIB) variables with machine learning techniques. During the evaluation test, the proposed model seems highly effective with deep neural network implementation with a precision of 99.6% accuracy rate.

scholarly journals Development of an IoT Architecture Based on a Deep Neural Network against Cyber Attacks for Automated Guided Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8467
Author(s):  
Mahmoud Elsisi ◽  
Minh-Quang Tran
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Online Monitoring ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Automated Guided Vehicles ◽  
Gradient Boosting ◽  
Machine Model ◽  
Extreme Gradient Boosting

This paper introduces an integrated IoT architecture to handle the problem of cyber attacks based on a developed deep neural network (DNN) with a rectified linear unit in order to provide reliable and secure online monitoring for automated guided vehicles (AGVs). The developed IoT architecture based on a DNN introduces a new approach for the online monitoring of AGVs against cyber attacks with a cheap and easy implementation instead of the traditional cyber attack detection schemes in the literature. The proposed DNN is trained based on experimental AGV data that represent the real state of the AGV and different types of cyber attacks including a random attack, ramp attack, pulse attack, and sinusoidal attack that is injected by the attacker into the internet network. The proposed DNN is compared with different deep learning and machine learning algorithms such as a one dimension convolutional neural network (1D-CNN), a supported vector machine model (SVM), random forest, extreme gradient boosting (XGBoost), and a decision tree for greater validation. Furthermore, the proposed IoT architecture based on a DNN can provide an effective detection for the AGV status with an excellent accuracy of 96.77% that is significantly greater than the accuracy based on the traditional schemes. The AGV status based on the proposed IoT architecture with a DNN is visualized by an advanced IoT platform named CONTACT Elements for IoT. Different test scenarios with a practical setup of an AGV with IoT are carried out to emphasize the performance of the suggested IoT architecture based on a DNN. The results approve the usefulness of the proposed IoT to provide effective cybersecurity for data visualization and tracking of the AGV status that enhances decision-making and improves industrial productivity.

scholarly journals Exploiting Magnitude and Phase Aware Deep Neural Network for Replay Attack Detection

2020 ◽  
Vol 18 (2) ◽  
pp. 89-97
Author(s):  
Khomdet Phapatanaburi ◽  
Prawit Buayai ◽  
Watcharaphon Naktong ◽  
Jakkree Srinonchat
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Speaker Verification ◽  
Gaussian Mixture ◽  
Attack Detection ◽  
Replay Attack ◽  
Model Classification ◽  
Novel Method ◽  
Additional Improvement ◽  
Speech Classification

Magnitude and phase aware deep neural network (MP aware DNN) based on Fast Fourier Transform information, has recently been received more attention to many speech applications. However, little attention has been paid to its aspect in terms of replay attack detection developed for the automatic speaker verification and countermeasures (ASVspoof 2017). This paper aims to investigate the MP aware DNN as a speech classification for detecting non-replayed (genuine) and replayed speech. Also, to exploit the advantage of the classifier-based complementary to improve the reliable detection decision, we propose a novel method by combining MP aware DNN with standard replay attack detection (that is, the use of constant Q transform cepstral coefficients-based Gaussian mixture model classification: CQCC-based GMM). Experiments are evaluated using ASVspoof 2017 and a standard measure of detection performance called equal error rate (EER). The results showed that MP aware DNN -based detection performed conventional DNN method using only the magnitude/phase features. Moreover, we found that score combination of CQCC-based GMM with MP aware DNN achieved additional improvement, indicating that MP aware DNN is very useful, especially when combined with the CQCC-based GMM for replay attack detection.

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