scholarly journals Forging a deep learning neural network intrusion detection framework to curb the distributed denial of service attack

2021 ◽  
Vol 11 (2) ◽  
pp. 1498
Author(s):  
Arnold Adimabua Ojugo ◽  
Rume Elizabeth Yoro
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Intelligent Systems ◽  
Denial Of Service ◽  
The Internet ◽  
Distributed Denial Of Service ◽  
Data Traffic ◽  
Denial Of Service Attack ◽  
Network Intrusion ◽  
Deep Learning Neural Network

Today’s popularity of the internet has since proven an effective and efficient means of information sharing. However, this has consequently advanced the proliferation of adversaries who aim at unauthorized access to information being shared over the internet medium. These are achieved via various means one of which is the distributed denial of service attacks-which has become a major threat to the electronic society. These are carefully crafted attacks of large magnitude that possess the capability to wreak havoc at very high levels and national infrastructures. This study posits intelligent systems via the use of machine learning frameworks to detect such. We employ the deep learning approach to distinguish between benign exchange of data and malicious attacks from data traffic. Results shows consequent success in the employment of deep learning neural network to effectively differentiate between acceptable and non-acceptable data packets (intrusion) on a network data traffic.

scholarly journals CNN-Based Network Intrusion Detection against Denial-of-Service Attacks

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 916 ◽  
Author(s):  
Jiyeon Kim ◽  
Jiwon Kim ◽  
Hyunjung Kim ◽  
Minsun Shim ◽  
Eunjung Choi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Denial Of Service ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
National Defense ◽  
Dos Attacks ◽  
Detection Systems ◽  
Network Intrusion

As cyberattacks become more intelligent, it is challenging to detect advanced attacks in a variety of fields including industry, national defense, and healthcare. Traditional intrusion detection systems are no longer enough to detect these advanced attacks with unexpected patterns. Attackers bypass known signatures and pretend to be normal users. Deep learning is an alternative to solving these issues. Deep Learning (DL)-based intrusion detection does not require a lot of attack signatures or the list of normal behaviors to generate detection rules. DL defines intrusion features by itself through training empirical data. We develop a DL-based intrusion model especially focusing on denial of service (DoS) attacks. For the intrusion dataset, we use KDD CUP 1999 dataset (KDD), the most widely used dataset for the evaluation of intrusion detection systems (IDS). KDD consists of four types of attack categories, such as DoS, user to root (U2R), remote to local (R2L), and probing. Numerous KDD studies have been employing machine learning and classifying the dataset into the four categories or into two categories such as attack and benign. Rather than focusing on the broad categories, we focus on various attacks belonging to same category. Unlike other categories of KDD, the DoS category has enough samples for training each attack. In addition to KDD, we use CSE-CIC-IDS2018 which is the most up-to-date IDS dataset. CSE-CIC-IDS2018 consists of more advanced DoS attacks than that of KDD. In this work, we focus on the DoS category of both datasets and develop a DL model for DoS detection. We develop our model based on a Convolutional Neural Network (CNN) and evaluate its performance through comparison with an Recurrent Neural Network (RNN). Furthermore, we suggest the optimal CNN design for the better performance through numerous experiments.

scholarly journals DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1533 ◽  
Author(s):  
Tuan Anh Tang ◽  
Lotfi Mhamdi ◽  
Des McLernon ◽  
Syed Ali Raza Zaidi ◽  
Mounir Ghogho ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Software Defined Networking ◽  
Cost Effective ◽  
The Internet ◽  
Network Intrusion ◽  
Effective Network ◽  
Control Application

Software Defined Networking (SDN) is developing as a new solution for the development and innovation of the Internet. SDN is expected to be the ideal future for the Internet, since it can provide a controllable, dynamic, and cost-effective network. The emergence of SDN provides a unique opportunity to achieve network security in a more efficient and flexible manner. However, SDN also has original structural vulnerabilities, which are the centralized controller, the control-data interface and the control-application interface. These vulnerabilities can be exploited by intruders to conduct several types of attacks. In this paper, we propose a deep learning (DL) approach for a network intrusion detection system (DeepIDS) in the SDN architecture. Our models are trained and tested with the NSL-KDD dataset and achieved an accuracy of 80.7% and 90% for a Fully Connected Deep Neural Network (DNN) and a Gated Recurrent Neural Network (GRU-RNN), respectively. Through experiments, we confirm that the DL approach has the potential for flow-based anomaly detection in the SDN environment. We also evaluate the performance of our system in terms of throughput, latency, and resource utilization. Our test results show that DeepIDS does not affect the performance of the OpenFlow controller and so is a feasible approach.

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