Detecting_and_Mitigating_Botnet_Attacks_in_Software-Defined_Networks_Using_Deep_Learning_Techniques.docx
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The paper explores the use of deep learning techniques to detect and mitigate botnet attacks in Software-Defined Networks (SDNs), emphasizing the challenges and opportunities in this emerging architecture. It presents a new dataset and highlights the performance of CNN methods, achieving detection rates of 99% for normal flows and 97% for attack flows. The study aims to develop a lightweight deep learning method with baseline hyper-parameters suitable for detecting such threats efficiently.
![Base paper Title: Detecting and Mitigating Botnet Attacks in Software-Defined Networks
Using Deep Learning Techniques
Modified Title: Using Deep Learning Techniques to Identify and Reduce Botnet Attacks in
Software-Defined Networks
Abstract
Software-Defined Networking (SDN) is an emerging architecture that enables flexible
and easy management and communication of large-scale networks. It offers programmable and
centralized interfaces for making complex network decisions dynamically and seamlessly.
However, SDN provides opportunities for businesses and individuals to build network
applications based on their demands and improve their services. In contrast, it started to face a
new array of security and privacy challenges and simultaneously introduced the threats of a
single point of failure. Usually, attackers launch malicious attacks such as botnets and
Distributed Denial of Service (DDoS) to the controller through OpenFlow switches. Deep
learning (DL)-based security applications are trending, effectively detecting and mitigating
potential threats with fast response. In this article, we analyze and show the performance of the
DL methods to detect botnet-based DDoS attacks in an SDN-supported environment. A newly
self-generated dataset is used for the evaluation. We also used feature weighting and tuning
methods to select the best subset of features. We verify the measurements and simulation
outcomes over a self-generated dataset and real testbed settings. The main aim of this study is
to find a lightweight DL method with baseline hyper-parameters to detect botnet-based DDoS
attacks with features and data that can be easily acquired. We observed that the best subset of
features influences the performance of the DL method, and the prediction accuracy of the same
method could be variated with a different set of features. Finally, based on empirical results,
we found that the CNN method outperforms the dataset and real testbed settings. The detection
rate of CNN reaches 99% for normal flows and 97% for attack flows.
Existing System
The development of the internet is rapidly growing; the limitations of traditional
networks have been explored. The emerging issues of the conventional networks can be solved
by patching the network, which makes the network more bloated and the control ability of the
network becomes weaker. The invention of Software-Defined Networking (SDN) [1], [2] has](https://image.slidesharecdn.com/detectingandmitigatingbotnetattacksinsoftware-definednetworksusingdeeplearningtechniques-231213055229-d2d5e241/85/Detecting_and_Mitigating_Botnet_Attacks_in_Software-Defined_Networks_Using_Deep_Learning_Techniques-docx-1-320.jpg)
![resolved these problems by decoupling the data and control planes. SDN became famous
among thenetwork community due to its novel architecture and can fulfill the demands of fast-
growing networks. SDN has a centralized control architecture, so the SDN controllers can
access all the OpenFlow switches in their range and control the entire network through the open
south API interfaces. It is also known as the three-layer network architecture, application,
control, and data layers. The application layer runs all the policies and rules the network
administrator defines, and the SDN controller can adopt these rules dynamically. Any
modification in the application layer may change the behavior of the whole network. The
application layer is an excellent development by the open-source platform, which does not
force the administrator to entirely relies on vendors [5]. Positively, the SDN allows
administrators to eliminate license constraints and cloud-develop customized network
applications over general-purpose hardware. The control layer is known as the brain of the
architecture, and SDN controllers run in this layer. The controllers receive the rules from the
application layer, decode them into readable messages, and forward them to the underlying
data layer; after that, they collect the feedback from the data layer and pass it back to the
application layer. Moreover, a decision is made on the control layer, and the rules are
implemented in the data layer. The data layer is non-intelligent, and different hardware devices,
such as routers, OpenFlow switches, etc., exist in this layer, and instructions are passed by the
control layer.
Drawback in Existing System
Data Availability and Quality: Deep learning models require large amounts of high-
quality data for effective training. Obtaining labeled data for botnet attacks in SDNs
can be challenging due to the dynamic and evolving nature of cyber threats.
Complexity of Network Traffic Patterns: SDNs generate complex and varied
network traffic patterns, making it difficult to accurately identify malicious activities
from normal network behavior. Deep learning models may struggle with understanding
these intricate patterns.
Resource Intensiveness: Deep learning models often demand significant
computational resources and time for training, especially for large-scale networks. This
can be a bottleneck in real-time threat detection and mitigation.
Adversarial Attacks: Deep learning models can be susceptible to adversarial attacks
where attackers manipulate inputs to deceive the model's predictions, leading to false
negatives or false positives in identifying botnet activities.](https://image.slidesharecdn.com/detectingandmitigatingbotnetattacksinsoftware-definednetworksusingdeeplearningtechniques-231213055229-d2d5e241/85/Detecting_and_Mitigating_Botnet_Attacks_in_Software-Defined_Networks_Using_Deep_Learning_Techniques-docx-2-320.jpg)
Detecting_and_Mitigating_Botnet_Attacks_in_Software-Defined_Networks_Using_Deep_Learning_Techniques.docx
- 1. Base paper Title: Detecting and Mitigating Botnet Attacks in Software-Defined Networks Using Deep Learning Techniques Modified Title: Using Deep Learning Techniques to Identify and Reduce Botnet Attacks in Software-Defined Networks Abstract Software-Defined Networking (SDN) is an emerging architecture that enables flexible and easy management and communication of large-scale networks. It offers programmable and centralized interfaces for making complex network decisions dynamically and seamlessly. However, SDN provides opportunities for businesses and individuals to build network applications based on their demands and improve their services. In contrast, it started to face a new array of security and privacy challenges and simultaneously introduced the threats of a single point of failure. Usually, attackers launch malicious attacks such as botnets and Distributed Denial of Service (DDoS) to the controller through OpenFlow switches. Deep learning (DL)-based security applications are trending, effectively detecting and mitigating potential threats with fast response. In this article, we analyze and show the performance of the DL methods to detect botnet-based DDoS attacks in an SDN-supported environment. A newly self-generated dataset is used for the evaluation. We also used feature weighting and tuning methods to select the best subset of features. We verify the measurements and simulation outcomes over a self-generated dataset and real testbed settings. The main aim of this study is to find a lightweight DL method with baseline hyper-parameters to detect botnet-based DDoS attacks with features and data that can be easily acquired. We observed that the best subset of features influences the performance of the DL method, and the prediction accuracy of the same method could be variated with a different set of features. Finally, based on empirical results, we found that the CNN method outperforms the dataset and real testbed settings. The detection rate of CNN reaches 99% for normal flows and 97% for attack flows. Existing System The development of the internet is rapidly growing; the limitations of traditional networks have been explored. The emerging issues of the conventional networks can be solved by patching the network, which makes the network more bloated and the control ability of the network becomes weaker. The invention of Software-Defined Networking (SDN) [1], [2] has
- 2. resolved these problems by decoupling the data and control planes. SDN became famous among thenetwork community due to its novel architecture and can fulfill the demands of fast- growing networks. SDN has a centralized control architecture, so the SDN controllers can access all the OpenFlow switches in their range and control the entire network through the open south API interfaces. It is also known as the three-layer network architecture, application, control, and data layers. The application layer runs all the policies and rules the network administrator defines, and the SDN controller can adopt these rules dynamically. Any modification in the application layer may change the behavior of the whole network. The application layer is an excellent development by the open-source platform, which does not force the administrator to entirely relies on vendors [5]. Positively, the SDN allows administrators to eliminate license constraints and cloud-develop customized network applications over general-purpose hardware. The control layer is known as the brain of the architecture, and SDN controllers run in this layer. The controllers receive the rules from the application layer, decode them into readable messages, and forward them to the underlying data layer; after that, they collect the feedback from the data layer and pass it back to the application layer. Moreover, a decision is made on the control layer, and the rules are implemented in the data layer. The data layer is non-intelligent, and different hardware devices, such as routers, OpenFlow switches, etc., exist in this layer, and instructions are passed by the control layer. Drawback in Existing System Data Availability and Quality: Deep learning models require large amounts of high- quality data for effective training. Obtaining labeled data for botnet attacks in SDNs can be challenging due to the dynamic and evolving nature of cyber threats. Complexity of Network Traffic Patterns: SDNs generate complex and varied network traffic patterns, making it difficult to accurately identify malicious activities from normal network behavior. Deep learning models may struggle with understanding these intricate patterns. Resource Intensiveness: Deep learning models often demand significant computational resources and time for training, especially for large-scale networks. This can be a bottleneck in real-time threat detection and mitigation. Adversarial Attacks: Deep learning models can be susceptible to adversarial attacks where attackers manipulate inputs to deceive the model's predictions, leading to false negatives or false positives in identifying botnet activities.
- 3. Proposed System The proposed study and the adopted scene. Its accuracy reaches 99.37% with subset-3 features using generated dataset. During real testbed traffic, the detection rate of CNN for normal flows is 99% and 97% for attack flows. The authors proposed a distributed method based on CNN and LSTM with an additional cloud-based component for detecting DDoS and phishing attacks. The overhead of the switches and controller. Another hybrid method based on Artificial Neural Networks (ANNs) and DNN was proposed The proposed system produced effective results on the NID dataset compared to BoT- IoT. Algorithm They do the hyper parameterization of SVM using the ‘‘Grey Wolf Optimization (GWO) algorithm’’ to determine the critical features for a botnet attack. A hybrid method of PSO algorithms with a voting mechanism to detect botnet attacks in IoT. All five algorithms for subset-3 features. It is observed that all the algorithms RNN, CNN, MLP, LSTM, and DNN Advantages Anomaly Detection: Deep learning models excel at recognizing patterns and anomalies in complex data, allowing them to identify unusual or suspicious behaviors within network traffic more effectively than traditional rule-based systems. Adaptability to Evolving Threats: Deep learning models can adapt and learn from new data, making them potentially more resilient against evolving botnet attack strategies that may have previously gone undetected. Automation and Real-Time Detection: Once trained, deep learning models can perform automated real-time analysis of network traffic, enabling swift detection and response to potential botnet activities without human intervention. Scalability: Deep learning models can scale efficiently to handle large volumes of network traffic, making them suitable for monitoring and protecting expansive SDN environments.
- 4. Software Specification Processor : I3 core processor Ram : 4 GB Hard disk : 500 GB Software Specification Operating System : Windows 10 /11 Frond End : Python Back End : Mysql Server IDE Tools : Pycharm