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Low-rate DDoS attack Detection using Deep Learning for SDN-enabled IoT Networks
2022
International Journal of Advanced Computer Science and Applications
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Many research papers focus on highrate DDoS attacks, while few address low-rate DDoS attacks in SDN-based IoT networks. ...
In this paper, we propose LDDoS attack detection approach based on deep learning (DL) model that consists of an activation function of the Long-Short Term Memory (LSTM) to detect different types of LDDoS ...
The other research direction will be exploring deep reinforcement learning in detecting low-rate DDoS attack in IoT networks. Fig. 1 . 1 Fig. 1. Software Defined Networking Architecture. ...
doi:10.14569/ijacsa.2022.0131141
fatcat:6aktrxadvzd4npww3uz2wkpgki
A Novel SDN-based Application-Awareness Mechanism by Using Deep Learning
2020
IEEE Access
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At present, three ways, i.e., port number, depth packet inspection and deep learning can be used for the application-awareness. ...
To this end, this paper proposes a Convolutional Neural Network (CNN) based deep learning mechanism to do the application-awareness, including three phases, i.e., traffic collection, data pre-processing ...
For example, in [28] , an application layer classifier combining both DPI and deep learning was designed to do classification in SDN. ...
doi:10.1109/access.2020.3021185
fatcat:2rv7jwpmgbh25oq5xx7jqi2bzu
Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction
2022
Sensors
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Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network. ...
Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. ...
Low convergence rate Deep reinforcement learning Decision-making More computational resources are required to train datasets. ...
doi:10.3390/s22207896
fatcat:stzssjxlorhmtnpk4j4wv56wle
SDN-Enabled Hybrid DL-Driven Framework for the Detection of Emerging Cyber Threats in IoT
2021
Electronics
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We present an SDN-enabled architecture leveraging hybrid deep learning detection algorithms for the efficient detection of cyber threats and attacks while considering the resource-constrained IoT devices ...
We use a state-of-the-art dataset, CICDDoS 2019, to train our algorithm. The results evaluated by this algorithm achieve high accuracy with a minimal false positive rate (FPR) and testing time. ...
CNN and LSTM-based detection systems are used to detect adversarial attacks in SDNs [32] . The deep learning approach has proved to have a great potential for the detection of malevolent activities. ...
doi:10.3390/electronics10080918
fatcat:rj4h2wx3zvfedfp5ygzxhyxiya
A Hybrid Deep Learning-Driven SDN Enabled Mechanism for Secure Communication in Internet of Things (IoT)
2021
Sensors
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There is a severe need to secure the IoT environment from such attacks. In this paper, an SDN-enabled deep-learning-driven framework is proposed for threats detection in an IoT environment. ...
The state-of-the-art Cuda-deep neural network, gated recurrent unit (Cu- DNNGRU), and Cuda-bidirectional long short-term memory (Cu-BLSTM) classifiers are adopted for effective threat detection. ...
A deep-learning-driven SDN-based framework is used in [24] for securing IoT infrastructure. ...
doi:10.3390/s21144884
fatcat:yxx6qkpuhrfs3k26q2agkjmx4i
Towards SDN-Enabled, Intelligent Intrusion Detection System for Internet of Things (IoT)
2022
IEEE Access
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The proposed model achieved 99.23 % detection accuracy with a low false-positive rate. ...
Therefore this research aims to propose an intelligent, SDN-enabled hybrid framework leveraging Cuda Long Short Term Memory Gated Recurrent Unit (cuLSTMGRU) for efficient threat detection in IoT environments ...
In [31] , Deep Neural Network (DNN) was used as a detection module, but the authors got a very low detection accuracy of only 75.75%. ...
doi:10.1109/access.2022.3153716
fatcat:iscqpe7fvffhbeaj57sqcq4zbe
Software Defined Network Enabled Fog-to-Things Hybrid Deep Learning Driven Cyber Threat Detection System
2021
Security and Communication Networks
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The authors propose a deep learning (DL) driven SDN-enabled architecture for sophisticated cyber-attacks detection in fog-to-IoT environment to identify new attacks targeting IoT devices as well as other ...
The proposed framework is so effective that it can detect several types of cyber-attacks with 99.92% accuracy rate in multiclass classification. ...
Acknowledgments e authors acknowledge the role of HOSCSCHULE COBURG group in data collection. ...
doi:10.1155/2021/6136670
fatcat:rftmiifz7reg5e7xovvfdcw4h4
A Hybrid Intelligent Framework to Combat Sophisticated Threats in Secure Industries
2022
Sensors
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To combat the threats in the IIoT environment, we proposed a deep-learning SDN-enabled intelligent framework. A hybrid classifier is used for threat detection purposes, i.e., Cu-LSTMGRU + Cu-BLSTM. ...
The proposed model achieved a better detection accuracy with low false-positive rate. We have conducted 10-fold cross-validation to show the unbiasdness of the results. ...
For attacks and threat detection in SDN networks, authors in [17] presented a DL (deep learning) system in which multilayer perception (MLP) is used. ...
doi:10.3390/s22041582
pmid:35214481
pmcid:PMC8875738
fatcat:7rsu74m6inbybp32c2dm7vk4oq
LRDDoS Attack Detection on SD-IoT Using Random Forest with Logistic Regression Coefficient
2022
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
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However, Low-Rate Distributed Denial of Service (LRDDoS) attacks are a major problem in SD-IoT networks, because they can overwhelm centralized control systems or controllers. ...
In this paper, the authors built an LRDDoS detection system using the Random Forest (RF) algorithm as the classification method. ...
The method used in this study [20] applied a Deep Learning-based Low-Rate DDoS attack detection approach on an SDN network using the Hybrid Convolutional Neural Network-Long-Short Term Memory (CNN-LSTM ...
doi:10.29207/resti.v6i2.3878
fatcat:tcswsle3dvcmvikccn4u5rccse
Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network
2023
Sustainability
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This paper proposes an SDN-based framework using machine learning techniques for intrusion detection in an industrial IoT environment. ...
The results indicate that the proposed framework can detect attacks in industrial IoT networks and devices with an accuracy of 99.7%. ...
[18] proposed a deep learning-based model that can learn using the information gathered from TCP/IP packets for anomaly detection in IICSs. ...
doi:10.3390/su15119001
fatcat:q4fr5q2puvb7hmmbuv7pwpoqd4
A Machine Learning Security Framework for IoT Systems
2020
IEEE Access
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In particular, the distribution of the attacks using the data mining approach is highly successful in detecting the attacks with high performance and low cost. ...
Indeed, IoT devices are prone to various security attacks varying from Denial of Service (DoS) to network intrusion and data leakage. ...
Though, it shows a very low precision for U2R and R2L attacks. J48 detects attacks with very good accuracy and low miss-classification rate (or CPE). ...
doi:10.1109/access.2020.2996214
fatcat:qu2fq5t7pjejhd7wkj4lrprvoe
SDN based Intrusion Detection and Prevention Systems using Manufacturer Usage Description: A Survey
2020
International Journal of Advanced Computer Science and Applications
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ML is used in the IDPS systems for the detection of security attacks and to predict future threats to the system. ...
In traditional networks, detection of malicious traffic and classification of a network attack is achieved using predefined rules and specifications which are limited to address new kinds of attacks. ...
Gaps in Machine Learning, SDN and MUD Deep learning is expected to improve the security of the network, but infect it is also vulnerable to cyber-attacks. ...
doi:10.14569/ijacsa.2020.0111283
fatcat:xhnac6wchnbxdfztz2b666l4ra
Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework
2022
IEEE Access
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In this scientific study, we proposed Deep Learning (DL) driven Software Defined Networking (SDN) enabled Intrusion Detection System (IDS) to combat emerging cyber threats in IoT. ...
INDEX TERMS Deep learning (DL), Internet of Things (IoT), intrusion detection system (IDS), distributed denial of service (DDoS), software-defined networking (SDN). information security with the Software ...
All these consequential impressions are the core motivation that prodigiously fascinated us to propose a deep learning-driven, SDN-based, intrusion detection system for IoT based communication environments ...
doi:10.1109/access.2022.3172304
fatcat:djblowz7z5brlniyjan2qve3ea
Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments
2021
IEEE Access
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INDEX TERMS DDoS attacks and detection, Internet of Things (IoT), machine learning (ML), network functions virtualization (NFV), software-defined network (SDN). ...
In recent years, machine learning (ML) techniques have been widely used to prevent DDoS attacks. ...
[15] proposed a deep learning framework for DDoS attack detection in the context of an SDN. ...
doi:10.1109/access.2021.3062909
fatcat:xtj576lfsffrbpiqyk2kv5wuam
A Taxonomy of DDoS Attack Mitigation Approaches Featured by SDN Technologies in IoT Scenarios
2020
Sensors
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The use of emerging technologies such as those based on the Software-Defined Networking (SDN) paradigm has proved to be a promising alternative as a means of mitigating DDoS attacks. ...
The Internet of Things (IoT) has attracted much attention from the Information and Communication Technology (ICT) community in recent years. ...
• Do not consider low traffic rate attacks 6.3.2 IoT Scenario
Advantages
Disadvantages
Generic
• Collaborative
• ...
doi:10.3390/s20113078
pmid:32485943
pmcid:PMC7309081
fatcat:v4dd357ednbkdewpvmue6xwdsa
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