DoS Attack Detection Using Machine Learning and Neural Network

2018 ◽  
Author(s):  
Shreekh Wankhede ◽  
Deepak Kshirsagar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Attack Detection ◽  
Dos Attack

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 Deep Learning Based-Phishing Attack Detection

2019 ◽  
Vol 8 (3) ◽  
pp. 8428-8432
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Human Life ◽  
Rapid Development ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector

Due to the rapid development of the communication technologies and global networking, lots of daily human life activities such as electronic banking, social networks, ecommerce, etc are transferred to the cyberspace. The anonymous, open and uncontrolled infrastructure of the internet enables an excellent platform for cyber attacks. Phishing is one of the cyber attacks in which attackers open some fraudulent websites similar to the popular and legal websites to steal the user’s sensitive information. Machine learning techniques such as J48, Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB) and Artificial Neural Network (ANN) were widely to detect the phishing attacks. But, getting goodquality training data is one of the biggest problems in machine learning. So, a deep learning method called Deep Neural Network (DNN) is introduced to detect the phishing Uniform Resource Locators (URLs). Initially, a feature extractor is used to construct a 30-dimension feature vector based on URL-based features, HTML-based features and domain-based features. These features are given as input to the DNN classifier for phishing attack detection. It consists of one input layer, multiple hidden layers and one output layer. The multiple hidden layers in DNN try to learn high-level features in an incremental manner. Finally, the DNN returns a probability value which represent the phishing URLs and legitimate URLs. By using DNN the accuracy, precision and recall of phishing attack detection is improved.

Physical Data Auditing for Attack Detection in Cyber-Manufacturing Systems: Blockchain for Machine Learning Process

2019 ◽  
Author(s):  
Jinwoo Song ◽  
Diksha Shukla ◽  
Mingtao Wu ◽  
Vir V. Phoha ◽  
Young B. Moon
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Learning Process ◽  
Manufacturing Systems ◽  
Detection System ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Comparative Performance ◽  
Physical Data

Abstract Auditing physical data using machine learning can enhance the security in Cyber-Manufacturing System (CMS). However, the physical data processing itself is prone to cyber-attacks. Connections based on the internet in CMS opens the route for adversaries to compromise the attack detection system itself. To prevent data from malicious data injection in CMS, this paper proposes an enhanced Simple Convolutional Neural Network (SCNN) based attack detection system employing a blockchain. There are three contributions of this paper: (i) introducing a secure attack detection system using blockchain, (ii) optimizing the cost and time for CMS by training on the simulated images, and (iii) presenting a real-time attack detection system for CMS by simplifying the convolutional neural network. The paper demonstrates the effectiveness of the blockchain implementation by presenting the comparative performance analysis of the proposed attack detection system with and without blockchain implementation using an example of a simulated attack on the machine learning process.

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