Distributed Denial Of Service Detection Research Articles

A Robust DDoS Intrusion Detection System Using Convolutional Neural Network

In today’s digital age, the proliferation of network-connected devices has triggered a surge in cyberattacks. Distributed Denial-of-Service (DDoS) attacks pose a particularly formidable challenge to network security by disrupting access to vital services. While numerous researchers have proposed DDoS detection methods utilizing machine learning and deep learning techniques, developing a robust and reliable DDoS intrusion detection system remains challenging. This challenge is exacerbated by issues such as highly imbalanced data, multi-classification, and computational complexity. This paper proposes an innovative feature selection approach to create a robust intrusion detection system capable of detecting and classifying recent common DDoS attack types. We evaluate the performance of our model on the CICDDoS2019 benchmark dataset. Our experimental results demonstrate that our proposed model outperforms existing methods, achieving a detection accuracy of 96.82%, a recall of 96.82%, a precision of 96.76%, and an F1 score of 96.50%. Additionally, our model exhibits faster prediction times, with the ability to predict an attack in just 0.189 ms. Notably, our approach, combined with preprocessing and feature selection techniques, outperforms previous works and baseline models in DDoS attack classification.

VAIDANSHH: Adaptive DDoS detection for heterogeneous hosts in vehicular environments

Vehicular networks are vulnerable to Distributed Denial of Service (DDoS), an extension of a Denial of Service (DoS) attack. The existing solutions for DDoS detection in vehicular networks use various Machine Learning (ML) algorithms. However, these algorithms are applicable only in a single layer in a vehicular network environment and are incapable of detecting DDoS dynamics for different layers of the network infrastructure. The recently reported attacks on transport networks reveal the fact that a research gap exists between the existing solutions and the multi-layer DDoS detection strategy requirements. Additionally, the majority of the current detection methods fail in the consideration of traffic heterogeneity and are not rate-adaptive, where both the mentioned parameters are important for an effective detection system.In this paper, we introduce a comprehensive ML-based Network Intrusion Detection System (NIDS) against DDoS attacks in vehicular networks. Our proposed NIDS combines a three-tier security model, traffic adaptivity, and heterogeneity traffic provisions. We call our model Vehicular Adaptive Intrusion Detection And Novel System for Heterogeneous Hosts (VAIDANSHH). As mentioned earlier, VAIDANSHH has a three-tier security system: at RSU's hardware, communication channel, and RSU application level. VAIDANSHH combines the Adaptive Alarming Module (AAM) and the Detection Module (DM) for data generation, collection of generated data, flow monitoring, pre-processing, and classification. We use the NS3 simulation tool for our experiments to generate synthetic data and apply ML with WEKA. We run a thorough set of experiments, which show that VAIDANSHH detects UDP flooding, a form of DDoS attack, with 99.9% accuracy within a very short time. We compare VAIDANSHH with other state-of-the-art models; the comparative analysis shows that VAIDANSHH is superior in terms of accuracy and its multi-tier workflow.

Machine learning for network defense: automated DDoS detection with telegram notification integration

As the prevalence and sophistication of distributed denial of service (DDoS) attacks escalate, the imperative for advanced defense mechanisms becomes paramount, especially in rapidly growing digital landscapes like Indonesia. This research presents the development of an innovative intrusion detection system (IDS) that harnesses machine learning (ML) algorithms to automate the detection of DDoS attacks in real time. By monitoring TCP streams, the system utilizes ML-enhanced IDS components to identify malicious traffic patterns indicative of DDoS activities. An automatic alert is dispatched to network administrators via Telegram upon detection, ensuring immediate awareness and facilitating swift countermeasures. Additionally, the system embodies a self-improving architecture by retraining its ML model with newly encountered attack data, thus continuously refining its detection capabilities. The system's efficacy, marked by its adaptive learning and proactive notification system, not only contributes to the fortification of network security but also underscores the potential for ML in cybersecurity within Indonesia’s expanding digital domain. The deployment of this system is anticipated to significantly bolster cybersecurity infrastructure by addressing the urgent need for advanced and responsive defense strategies against the evolving landscape of cyber threats.

Enhancing DDoS Attack Detection and Network Resilience Through Ensemble-Based Packet Processing and Bandwidth Optimization

It is critical to identify Distributed Denial of Service (DDoS) attacks to preserve network integrity and guarantee continuous service delivery. Our research suggests a novel way to lower the network's packet drop ratio and improve the accuracy of DDoS attack detection. Conventional techniques occasionally just use anomaly detection or signature-based detection, which might not be sufficient to protect against DDoS assault schemes that are always changing. To increase the precision and resilience of DDoS detection, our system incorporates several detection strategies, such as signature-based, anomaly-based, and machine learning-based techniques. Additionally, we use network traffic analysis and anomaly detection tools to quickly discover and block harmful traffic patterns. During suspected DDoS attempts, we dynamically modify network parameters and reroute data to reduce the packet drop ratio and maintain service for authorized users. Additionally, our system has feedback systems that allow us to continuously adjust and improve detection algorithms, improving the overall dependability and effectiveness of DDoS attack detection. We illustrate how successfully our method lowers packet drop ratios and strengthens network resilience against DDoS attacks using both simulation and real-world experience.

Smart network security using advanced ensemble-DDoS attack detection and hybrid JA-SLOA-linked optimal routing-based mitigation

ABSTRACT Distributed Denial of Service (DDoS) attacks are distributed at a faster rate, and they are considered to be fatal threats over the Internet. Moreover, several deep learning approaches are insufficient to attain the maximum efficiency and appropriate detection due to the complexity and diversity of DDoS attack traffic under fast fast-speed network environment since they are providing with individual performance. Hence, an ensemble learning model is developed for DDoS detection and mitigation with the hybrid optimization algorithm to ensure good detective performance against DDoS attacks. The pre-processed data is fed to the feature extraction process where it is done through the Deep Belief Network (DBN) and Autoencoder techniques for acquiring the deep features. The optimized fusion of deep features takes place using hybrid meta-heuristic algorithm of Adaptive Sound Speed-based Jaya Sea Lion Optimization (ASS-JSLnO). The attack detection is performed by the Improved Ensemble Learning (IEL) approach. Then, the mitigation strategy is applied by the optimal routing and multi-objective function. Finally, the experiments are made to establish the high detection efficacy of the implemented attack detection and mitigation framework.

Adversarially robust and real-time DDoS detection and classification framework using AutoML

ABSTRACT Denial of Service (DoS) attacks target the availability part of the CIA triad (Confidentiality, Availability, and Integrity). A special category of these attacks is the Distributed DoS (DDoS) attack, where the attacker uses a network of compromised systems called a botnet to flood a target server with requests and refuses to serve legitimate users. DDoS attacks can cost an organization millions of dollars in terms of lost revenue, remediation costs, and damage to brand reputation. Hence, all organizations need speedy real-time detection of DDoS attacks. This work presents a DDoS detection and classification framework using the flow-based approach for feature engineering and the AutoML technique. Our detection system is trained on the latest DDoS datasets – CIC-DDoS 2019 and CIC-IDS 2017, which contain various categories of DDoS attacks. We use various tools to perform adversarial attacks on our trained model. We retrain our models using adversarially crafted network packet captures and then test our models for robustness against practical adversarial attacks that an attacker might use to evade detection. Finally, we deploy our model in real-time using a GUI-based tool. Our model achieves a validation accuracy of 99.9% and a low false positive rate of 0.05%.

Toward Generating a New Cloud-Based Distributed Denial of Service (DDoS) Dataset and Cloud Intrusion Traffic Characterization

The distributed denial of service attack poses a significant threat to network security. Despite the availability of various methods for detecting DDoS attacks, the challenge remains in creating real-time detectors with minimal computational overhead. Additionally, the effectiveness of new detection methods depends heavily on well-constructed datasets. This paper addresses the critical DDoS dataset creation and evaluation domain, focusing on the cloud network. After conducting an in-depth analysis of 16 publicly available datasets, this research identifies 15 shortcomings across various dimensions, emphasizing the need for a new approach to dataset creation. Building upon this understanding, this paper introduces a new public DDoS dataset named BCCC-cPacket-Cloud-DDoS-2024. This dataset is meticulously crafted, addressing challenges identified in previous datasets through a cloud infrastructure featuring over eight benign user activities and 17 DDoS attack scenarios. Also, a Benign User Profiler (BUP) tool has been designed and developed to generate benign user network traffic based on a normal user behavior profile. We manually label the dataset and extract over 300 features from the network and transport layers of the traffic flows using NTLFlowLyzer. The experimental phase involves identifying an optimal feature set using three distinct algorithms: ANOVA, information gain, and extra tree. Finally, this paper proposes a multi-layered DDoS detection model and evaluates its performance using the generated dataset to cover the main issues of the traditional approaches.

Using the method of nonlinear recursive analysis for detecting DDOS anoma-lies in time series data

This research endeavors to address this gap by determining a qualitative char-acteristic for server network traffic and use it to construct the corresponding recur-rence plot (RP). The goal of this study is to develop and assess a novel technique based on nonlinear recursive analysis to detect Distributed Denial of Service (DDoS) anomalies in network traffic time series data. With the increasing frequency of DDoS attacks on modern digital infrastructures, there is a pressing need for more efficient and accurate detection methods. There has been some attempts to apply nonlinear analysis to network traffic [2-4], but those studies lack critical steps in determining parameters for embedding space dimension and delay time . More recent studies have explored machine learning and deep learning approaches [7], which offer improved accuracy but can be computationally intensive and require extensive training data. Despite the advance-ments, there remains a need for a method that is both accurate and efficient, espe-cially in real-time detection scenarios. The researchers employed nonlinear recursive analysis by estimating RQA pa-rameters and determining a qualitative characteristic of data points of DDoS attack contained in CIC-IDS2017 dataset. A technique for determining hidden information for this series and its use for constructing the corresponding recurrence diagram (RP) at the points of information retrieval are described. It is shown that the use of RP has significant drawbacks associated with the visualization of information on a computer monitor screen, so another way of research is proposed - the calculation of numerical indicators of RP The given calculated RP indicators made it possible to typify the received data and determine the type, which was named "DDOS-RP", which makes it possible to distinguish some types of DoS/DDoS type attacks. The study concludes by recom-mending further exploration of this method in diverse network environments and against more complex DDoS attack patterns.

Drift Adaptive Online DDoS Attack Detection Framework for IoT System

Internet of Things (IoT) security is becoming important with the growing popularity of IoT devices and their wide applications. Recent network security reports revealed a sharp increase in the type, frequency, sophistication, and impact of distributed denial of service (DDoS) attacks on IoT systems, making DDoS one of the most challenging threats. DDoS is used to commit actual, effective, and profitable cybercrimes. The current machine learning-based IoT DDoS attack detection systems use batch learning techniques, and hence are unable to maintain their performance over time in a dynamic environment. The dynamicity of heterogeneous IoT data causes concept drift issues that result in performance degradation and automation difficulties in detecting DDoS. In this study, we propose an adaptive online DDoS attack detection framework that detects and adapts to concept drifts in streaming data using a number of features often used in DDoS attack detection. This paper also proposes a novel accuracy update weighted probability averaging ensemble (AUWPAE) approach to detect concept drift and optimize zero-day DDoS detection. We evaluated the proposed framework using IoTID20 and CICIoT2023 dataset containing benign and DDoS traffic data. The results show that the proposed adaptive online DDoS attack detection framework is able to detect DDoS attacks with an accuracy of 99.54% and 99.33% for the respective datasets.

An optimal secure defense mechanism for DDoS attack in IoT network using feature optimization and intrusion detection system

Now, the Cyber security is facing unprecedented difficulties as a result of the proliferation of smart devices in the Internet of Things (IoT) environment. The rapid growth in the number of Internet users over the past two decades has increased the need for cyber security. Users have provided new opportunities for attackers to do harm. Limited security budgets leave IoT devices vulnerable and easily hacked to launch distributed denial-of-service (DDoS) attacks, with disastrous results. Unfortunately, due to the unique nature of the Internet of Things environment, most security solutions and intrusion detection systems (IDS) cannot be directly adapted to the IoT with acceptable security performance and are vulnerable to various attacks that do not benefit. In this paper we propose an optimal secure defense mechanism for DDoS in IoT network using feature optimization and intrusion detection system (OSD-IDS). In OSD-IDS mechanism, first we introduce an enhanced ResNet architecture for feature extraction which extracts more deep features from given traffic traces. An improved quantum query optimization (IQQO) algorithm for is used feature selection to selects optimal best among multiple features which reduces the data dimensionality issues. The selected features have given to the detection and classification module to classify the traffic traces are affected by intrusion or not. For this, we design a fast and accurate intrusion detection mechanism, named as hybrid deep learning technique which combines convolutional neural network (CNN) and diagonal XG boosting (CNN-DigXG) for the fast and accurate intrusion detection in IoT network. Finally, we validate the performance of proposed technique by using different benchmark datasets are BoNeSi-SlowHTTPtest and CIC-DDoS2019. The simulation results of proposed IDS mechanism are compared with the existing state-of-art IDS mechanism and analyze the performance with respects to different statistical measures. The results show that the DDoS detection accuracy of proposed OSD-IDS mechanism is high as 99.476% and 99.078% for BoNeSi-SlowHTTPtest, CICDDoS2019, respectively.

Research on Detection and Defense Methods for Software‐Defined Network Architecture after Hybrid Attack by Distributed Denial of Service

The architecture of software‐defined network (SDN)enhances the openness of the network by separating the control and forwarding functions, but the centralized SDN control form is susceptible to distributed denial of service (DDoS) attacks. In this paper, entropy value and back‐propagation neural network (BPNN) were applied to the DDoS attack detection of SDN, and then the two detection algorithms were simulated in MATLAB software and compared with the K‐means algorithm. The results showed that in the face of four DDoS attacks, SYN Flood, ACK Flood, UDP Flood and ICMP Flood, the BPNN‐based DDoS detection had higher accuracy and less detection time; the switch that adopted the BPNN‐based DDoS detection algorithm adjusted the traffic ratio back to normal level faster when facing DDoS attacks, reducing the impact on other switches and maintaining the traffic stability of the network. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Comparing Metaheuristic Search Techniques in Addressing the Effectiveness of Clustering-Based DDoS Attack Detection Methods

Distributed Denial of Service (DDoS) attacks have increased in frequency and sophistication over the last ten years. Part of the challenge of defending against such attacks requires the analysis of very large volumes of data. Metaheuristic algorithms can assist in selecting relevant features from the network traffic data for use in DDoS detection models. By efficiently exploring different combinations of features, these methods can identify subsets that are informative for distinguishing between normal and attack traffic. However, identifying an optimized solution in this area is an open research question. Tuning the parameters of metaheuristic search techniques in the optimization process is critical. In this study, a switching approximation is used in a variety of metaheuristic search techniques. This approximation is used to find the best solution for the analysis of the network traffic features in either lower or upper values between 0 and 1. We compare the fine-tuning of this parameter against standard approaches and find that it is not substantially better than the BestFirst algorithm (a standard default approach for feature selection). This study contributes to the literature by testing and eliminating various fine-tuning strategies for the metaheuristic approach.

Multi-domain collaborative two-level DDoS detection via hybrid deep learning

In this paper, we investigate the problem of multiple network domains being threatened by Distributed Denial-of-Service (DDoS) attacks, in which a DDoS attack detection scheme is constructed based on the Software Defined Networks (SDN) hierarchical distributed control plane architecture. Specifically, we propose a two-level detection framework for collaborative DDoS attack detection in multi-domain scenarios. To detect the signs of DDoS attacks as early as possible on the attack path, a first-level coarse-grained anomaly detection method based on the Rényi entropy algorithm is proposed. The purpose is to calculate the feature entropy of normal and abnormal traffic in a simple statistical way within the local network domain, achieving rapid perception of network anomalies. Then, the root server aggregates all abnormal traffic data uploaded by each local network domain, and the DCNN-LSTM algorithm based on a hybrid deep learning model as the second-level detection method extracts the features of the suspicious traffic from both temporal and spatial dimensions to achieve fine-grained DDoS attack classification. Finally, theoretical analysis and experimental results indicate that the proposed two-level detection method in multi-domain scenarios is effective and feasible, while with high detection accuracy.

DDOS Attacks Detection via Deep Learning Approaches

Abstract: The proliferation of Distributed Denial of Service (DDoS) attacks presents a pressing challenge to network security. Conventional rule-based detection methods are increasingly inadequate against the evolving tactics employed by cyber adversaries. This journal proposes a novel approach integrating Support Vector Machines (SVM) into advanced machine learning architectures for fortified DDoS detection. The research methodology initiates with comprehensive data collection, gathering diverse network traffic scenarios and DDoS attack instances. This dataset becomes the foundation for subsequent phases, employing sophisticated feature engineering to extract vital patterns for model development. The feature selection process involves using feature engineering techniques including Data Collection, Model Development and SVM Integration, to extract the most discriminative and relevant features from the network traffic data. The primary innovation lies in the creation of a hybrid CNN-LSTM model, capable of discerning spatial and temporal dependencies within network data, thereby augmenting DDoS threat identification. The integration of SVM into machine learning and deep learning paradigms forms the crux of this study. By leveraging SVM's classification proficiency alongside the CNN-LSTM model's capabilities, a sophisticated DDoS detection system aims to surpass conventional rule-based limitations.

Cyber-Secure SDN: A CNN-Based Approach for Efficient Detection and Mitigation of DDoS attacks

Software Defined Networking (SDN) has become popular due to its flexibility and agility in network management, enabling rapid adaptation to changing business requirements, enhancing network performance, and reducing operational costs. However, the ubiquity of internet-based services has given rise to an alarming increase in cyber-attacks, posing serious threats to the security and stability of modern networks. Among these attacks, Distributed Denial of Service (DDoS) attacks have emerged as one of the most devastating, capable of disrupting critical services. Recent studies have shown that Deep Learning (DL) techniques with Software-defined networking have the potential to mitigate these threats effectively. However, existing solutions suffer from issues such as reliance on pre-defined rules and signatures, computational efficiency, low detection rates, and inefficient notification mechanisms, making them ineffective in detecting DDoS attacks. This paper proposes an efficient approach (BRS + CNN) using Balanced Random Sampling (BRS) and Convolutional Neural Networks (CNNs) to detect DDoS attacks in SDN environments. We have applied various mitigation techniques to mitigate these threats, such as filtering, rate limiting, and iptables rule for blocking spoofed IPs. In addition, we introduce a monitoring system that utilizes rate-limiting to oversee blocked IP addresses, ensuring that legitimate traffic is processed efficiently. The proposed model achieves high performance in binary and multi-classification, with an accuracy of over 99.99% for binary classification and 98.64% for multi-classification. Our proposed DDoS detection system not only detects the attack but also sends detailed contextual information to a designated email address. We compare our model with existing literature and demonstrate its superiority using Area Under The Curve (AUC) analysis. Moreover, we evaluated the efficiency and effectiveness of our proposed DDoS mitigation system by conducting a series of experiments across three distinct scenarios: Attack-Free, Attack-No Mitigation, and Attack-Mitigation. These results demonstrate the robustness of our proposed mitigation system in effectively combating DDoS attacks while also safeguarding the seamless continuity of regular network operations.

Mitigating DDoS attacks in cloud computing using machine learning algorithms

Distributed Denial of Service (DDoS) attacks pose a significant threat to the availability and performance of cloud computing systems. As these attacks continue to evolve in complexity and scale, traditional mitigation techniques may prove insufficient. This research explores the application of machine learning algorithms as an intelligent and adaptive approach to enhance DDoS detection and mitigation in cloud environments.The study leverages the dynamic and scalable nature of cloud computing to implement a robust defence mechanism against DDoS attacks. Machine learning models, such as supervised and unsupervised learning algorithms, are trained on network traffic data to identify patterns indicative of DDoS activity. The proposed system adapts to evolving attack strategies and is capable of real-time analysis, ensuring swift responses to emerging threats.

Detection of distributed denial of service attack using enhanced adaptive deep dilated ensemble with hybrid meta‐heuristic approach

AbstractThe biggest firms throughout the world now are the ones that offer services in the cloud. One of the top problems for cloud users (CUs) and cloud service providers (CSPs) is the availability of cloud‐based services whenever needed. A distributed denial of service (DDoS) assault has been a significant threat to the security of the system in recent years. DDoS defense and detection of these attacks have become hot topics of research for academia and business. However, most approaches cannot accomplish efficient detection outcomes with few false alarms. As a result, minimizing the consequences of DDoS attacks allows CSPs to offer CUs high‐quality services. In the cloud sector, a collective deep structured algorithm is suggested to identify DDoS attacks successfully. The recommended method contains several stages: data acquisition, pre‐processing, optimal feature detection, and selection. The first step is the acquisition of data using the help of publicly available sources. Further, the input data undergoes preprocessing. Consequently, the optimal weighted feature selection takes place on the preprocessed data, where the optimization is done with the aid of the Hybrid Border Collie and Dragonfly Algorithm (HBCDA). Finally, DDoS attack detection is achieved via a novel method known as adaptive deep dilated ensemble (ADDE), which includes, one‐dimensional convolutional neural network (1DCNN), deep temporal convolutional neural network (DTCNN), recurrent neural network (RNN), and bidirectional long short‐term memory (Bi‐LSTM). For the attainment of optimal results, the parameter tuning is accomplished by using the HBCDA approach. The detection outcome is computed by using the fuzzy ranking mechanism. The validation is done for the suggested method model, and its corresponding findings are validated with conventional techniques. Hence, the suggested approach outperforms the detection performance and ensures more efficiency than traditional approaches.

Deep Learning based DDoS Attack Detection in Internet of Things: An Optimized CNN-BiLSTM Architecture with Transfer Learning and Regularization Techniques

In recent days, with the rapid advancement of technology in informatics systems, the Internet of Things (IoT) becomes crucial in many aspects of daily life. IoT applications have gained popularity due to the availability of various IoT enabler gadgets, such as smartwatches, smartphones, and so on. However, the vulnerability of IoT devices has led to security challenges, including Distributed Denial-of-Service (DDoS) attacks. These limitations result from the dynamic communication between IoT devices due to their limited data storage and processing resources. The primary research challenge is to create a model that can recognize legitimate traffic while effectively protecting the network against various classes of DDoS attacks. This article proposes a CNN-BiLSTM DDoS detection model by combining three deep-learning algorithms. The models are evaluated using the CICIDS2017 dataset against commonly used performance criteria which the models perform well, achieving an accuracy of around 99.76%, except for the CNN model, which achieves an accuracy of 98.82%. The proposed model performs best, achieving an accuracy of 99.9%.

Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems.

Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due to increased size, complexity, and device diversity. Using Software Defined Networking (SDN) technology in traditional SCADA network infrastructure offers management, scaling and flexibility benefits. However, as the integration of SDN-based SCADA systems with modern technologies such as the Internet of Things, cloud computing, and big data analytics increases, cybersecurity becomes a major concern for these systems. Therefore, cyber-physical energy systems (CPES) should be considered together with all energy systems. One of the most dangerous types of cyber-attacks against SDN-based SCADA systems is Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt the management of energy resources, causing service interruptions and increasing operational costs. Therefore, the first step to protect against DDoS attacks in SDN-based SCADA systems is to develop an effective intrusion detection system. This paper proposes a Decision Tree-based Ensemble Learning technique to detect DDoS attacks in SDN-based SCADA systems by accurately distinguishing between normal and DDoS attack traffic. For training and testing the ensemble learning models, normal and DDoS attack traffic data are obtained over a specific simulated experimental network topology. Techniques based on feature selection and hyperparameter tuning are used to optimize the performance of the decision tree ensemble models. Experimental results show that feature selection, combination of different decision tree ensemble models, and hyperparameter tuning can lead to a more accurate machine learning model with better performance detecting DDoS attacks against SDN-based SCADA systems.

DDoS attack detection and mitigation using deep neural network in SDN environment

In the contemporary digital landscape, the escalating threat landscape of cyber attacks, particularly distributed denial-of-service (DDoS) attacks, has become a paramount concern for network security. This research introduces an innovative approach to DDoS detection leveraging a deep neural network (DNN) architecture rooted in deep learning (DL) principles. The proposed model exhibits a scalable and adaptable framework, enabling meticulous analysis of network traffic data to discern intricate patterns indicative of DDoS attacks. To validate the efficacy of our methodology, rigorous evaluations were conducted using authentic real-world traffic data. The results unequivocally establish the superiority of our DNN-based approach over traditional DDoS detection techniques. This research holds significant promise for bolstering network security, particularly within the dynamic landscape of software-defined network (SDN) environments. The study's findings contribute to the continual refinement and eventual deployment of advanced measures in fortifying digital infrastructure against the evolving threat landscape. Performance metrics, including detection accuracy and loss rates, further emphasize the effectiveness of our approach across different datasets. With detection accuracy rates of 99.98%, 100%, and 99.99% for the InSDN, CICIDS2018, and Kaggle DDoS datasets, respectively, coupled with low loss rates, our DNN-based model demonstrates robust capabilities in mitigating contemporary DDoS threats. This study not only presents a novel DDoS detection approach within SDN infrastructures but also offers insights into practical implications and challenges associated with deploying DNNs in real-world SDN environments. Network security professionals can benefit from the nuanced perspectives provided, contributing to the ongoing discourse on fortifying digital networks against evolving cyber threats.