Innovative Machine Learning Strategies for DDoS Detection: A Review

1. INTRODUCTION

Distributed denial of service (DDoS) attacks pose a significant threat in the current era of interdependent systems and digital dependencies, where cyber challenges are already numerous. It is understandable that nefarious actors remain resolute in exploiting network infrastructure vulnerabilities, necessitating the need for smarter and more adaptive defense mechanisms. This urgent need has fueled intensive research into DDoS attack detection using Machine learning (ML) methodologies, resulting in an active and rapidly evolving field of study teeming with innovative solutions. We aim to investigate the intricate web of studies dealing with ML and DDoS attack detection through this in-depth analysis. This review aims to give This review seeks to provide a comprehensive overview of cutting-edge methodologies, challenges, and advancements in this crucial field, utilizing 19 diverse references that each contribute to the overall discussion the literature review uses a wide range of DDoS detection and ML algorithms, from classical classifiers, such as Gaussian Naive Bayes, which have been improved through iterative feature selection schemes, to current DMQ ML model the latter, which relies on quantum computing capabilities, significantly alters the dimensionality reduction and encoding paradigm to achieve an unparalleled level of efficiency in identifying DDoS attacks. Identifying the Recognizing the crucial role of software-defined networking (SDN) infrastructures, some references explore optimized models for DDoS detection in SDN environments. have achieved outstanding developments in accuracy and effectiveness by combining mininets, Ryu controllers, and one-dimensional-convolutional neural networks with innovative hyperparameter tuning techniques.

Besides traditional network systems, the review focuses on DDoS attack identification in emerging domains of 5G networks and the Internet of Things (IoT). Among notable techniques, the use of equilibrium optimization algorithms and ensemble learning tactics has emerged as an adaptable approach in response to continually changing cybersecurity challenges. A novel approach to studying the details of application-layer DDoS attacks presents a hybrid ML solution combining a Radial Basis Function neural network and a cuckoo search algorithm. This method has the potential to distinguish between legitimate and attack traffic, filling a significant loophole in current detection mechanisms. The review looks into DDoS attack prediction models that use the UNWS-np-15 dataset and the Random Forest and XGBoost classification algorithms. These models highlighted the power of ML in foreseeing and thwarting DDoS threats beforehand. When protocols-based deep intrusion detection architectures and hybrid approaches, such as autoencoder-multi-layer perceptron (AE-MLP) and evolutionary support vector machine (SVM) [1] models are used in software-defined networks, it makes things more complicated and flexible for using ML to better detect DDoS attacks. This review, after integrating these disparate views, aims to do more than summarize what is already known about the current body of knowledge; it will also reveal potential paths DDoS detection employing ML may take in the future. In doing so, it seeks to add its voice to the ongoing conversation about cybersecurity, which calls for a collaborative and dynamic approach toward mitigating an increasingly changing threat environment.

DDoS attacks form a significant threat in today’s interconnected world and call for efficient and highly intelligent detection using ML. This review analyses a broad range of methods including basic classifiers all the methods up to modern quantum and various hybrid methods and discusses key advancements and future challenges in different types of networks, including SDN, 5G, and IoT. Its purpose is to present a literature review of the DDoS detection field’s current state and possible further development.

2. LITERATURE REVIEW

DDoS attacks pose a major threat to network security in recent times. As a result, researchers have experimented with various methods, particularly ML approaches, to identify and neutralize these attacks. In this literature review, 19 of the most critical references are critically reviewed, providing a comprehensive analysis of how various methodologies can be used to detect DDoS attacks. Naiem et al. proposed a new framework that revives the Gaussian Naive Bayes classifier for DDoS attack detection in cloud computing environments during their research [2]. Their solution seems to be a thoughtfully designed combination of sequential feature selection and data preprocessing with specific targets, which greatly reduces the inadequacies observed by GNB. What distinguishes their approach is their tactical selection of attributes and highly independent characteristics, utilizing techniques such as PCC, MI, and Chi-square. They also handle data skews or sample zero frequencies effectively so that GNB would prove to be effective even under worst-case scenarios. Therefore, the study presents a potential solution to enhance the defense against DDoS in cloud computing destined for achieving consistency and availability of crucial services [2]. Analyzing the results, it is possible to observe the improvement of the identified GNB’s accuracy and precision, which indicate the effectiveness of the system for the case of DDoS detection in the cloud environment.

Luo et al. authors created a novel model, DEQSVC, which leverages the dynamics of quantum computing to mitigate DDoS. DEQSVC tackles two key challenges: overloading of data and intricate patterns of an attack. First, DEQSVC eliminates a number of features and, therefore, minimizes the amount of data to be processed by the Quantum Support Vector Classifier. This not only leads to an increase in efficiency but also gives the true potential of the QSV in terms of identifying hidden patterns of attacks. 2) DEQSVC utilizes quantum encoding which encodes data in quantum states, and this would help QSV in recognizing even complicated attack patterns that are even impossible for quantum algorithms to determine. “It would be great to decipher digital hazards as easily as the protagonist of Superman with X-ray vision” [3]. The results are impressive. DEQSVC shows a notable increase in DDoS detection accuracy compared to standard approaches. Its capacity to deal with both large and small attacks is why it’s a useful tool in your cybersecurity armory [3].

Through an optimized AI mode, researchers Chen et al. offer hope to SDN environments plagued by DDoS attacks. This warrior is a hybrid of deep learning and SDN-specific features, reducing false positives and improving detection accuracy. Think about an alert AI protector monitoring the network traffic with its LSTM sniffing out irregularities like a bloodhound smelling something. The result is a DDoS-battling machine that operates your SDN with the efficiency of a well-oiled engine. Plunge into the “3 Optimized Artificial Intelligent Model for DDoS Detection in SDN Environment” PDF located in your ML folder and strengthen your network with this state-of-the-art protection [5].

In their work, Aljebreen et al. suggest using MEOADL-ADC as a precise method for distinguishing DDoS attacks in 5G networks. This method uses three strong techniques: important data points identified from the dataset using a method known as the Modified Equilibrium Optimization Algorithm (MEOA); deep learning with LSTM that can distinguish between attack patterns and normal traffic; and last but not least TSA used in hyperparameter optimization. With this integrated approach, MEOADL-ADC has a high accuracy of 97% by comparing with the data obtained from prototypes. 60%, which is above any of the existing state-of-the-art solutions and can present rather a good solution for network security for future generations of infrastructure networks [6].

Aljebreen et al. also have tackled the fact that IoT devices are becoming targeted by DDoS attacks in another work using the proposed technique of DDAD-SOEL. This method effectively applies the best aspect of Snake Optimizer in feature selection as well as strength on LSTM, DBN, and BiLSTM deep learning model through ensemble learning for achieving an enhanced attack detection result. Finally, the Adadelta optimizer further fine-tuned the hyperparameters of the model and hence the proposed DDAD SOEL has outperformed the other methods based on accuracy, precision, recall, and F1 score altogether in securing interconnected systems [7]. Sharif et al. address the growing problem of toolkits that facilitate DDoS attacks. His wonderful method combines a technique for selecting data features that speed up the process with an unusually detailed ML model, yielding notable results such as 99.9% accuracy, 96% precision, 98% recall, and a 97% F1 score. Diverse accessible toolkits efficiently detect network abuse, thereby providing a crucial defense against attacks [8].

Josue et al. His paper reveals a new dataset that addresses the vulnerability of IoT devices to denial of service (DoS) attacks. It shows how hackers can gain easy access and compromise these types of networks or services, thereby affecting the entire connection both at home and in businesses globally. IoT LATAM-DDoS combines real traffic attacks on physical devices with user data for normal users; it is also a critical ground for training robust anomaly detection systems. As it covers a vast field of different attack types and various physical devices, its gap in current resources is vital for researchers and developers to improve defense strategies, test existing ones out there, or even benchmark other approaches toward securing this rapidly developing world of connected things as shown in Figures 1 and 2 [4].

Fig. 1. Testbed configuration for the DoS and DDoS attacks launch during the LATAM-DDoS-IoT dataset creation [4].

Fig. 2. DEQSVC detection approach [4].

El Sayed et al. address DDoS threats through SDNs. He picks up important characteristics from network flows, chooses the most informative ones with Chi-square, and reuses KNN to identify normal traffic versus attack patterns. 21 As a result, this efficient and accurate solution, which boasts high detection rates with minimal false alarms, becomes an attractive method of securing the SDNs against the formation of newer DDoS malware.

Researchers Beitollani et al. tackle the challenge of application-layer DDoS attacks with a powerful one-two punch: Researchers Beitollani et al. use the Radial Basis Function (RBF) network to accurately identify attacks, and extend the Cuckoo Search Algorithm to optimize the RBF’s network design. Let’s assume we have a neural network with three layers. The traffic features enter this brain and undergo transformation by the neurons in these layers, resulting in either a normal output or an indication of an attack. The bright brood parasitism of cuckoo father birds motivates the CSA method, which optimizes the hidden layer’s “decision-making machinery” (positioning and neuro width). In the end, it improves the accuracy of detecting DDoS attacks for a network equipped with an optimized RBF that requires fewer training sessions and less resource confit ensures resistance against a variety of attack patterns, making it a valuable tool for safeguarding the constantly expanding realm of online services [10].

Ismail et al. create a strong model that gains not only the ongoing DDoS attacks but forecasts them for an organization to prepare defensive measures early. The proposed multi-stage approach looks at network traffic traits, determines the most significant features, and uses ML models to correctly classify attacks while anticipating future ones. The system’s proactive protection, high accuracy, and adaptability make it a valuable asset in addressing DDoS hazards. Individuals can “instead imagine a flowchart where traffic features come in, are filtered by the process using these crucial indicators, and feed into intelligent models that not only trigger alarms for ongoing attacks but also start alarm bells when danger is approaching.” Its comprehensive ML strategy creates a formidable barrier against persistent DDoS attacks [11]. Zeeshan et al. describe a new deep learning-based method called protocol-based deep intrusion detection (PBDiD) that can stop DoS and DDoS attacks in internal IoT networks. Their strategy uses an LSTM network to analyze network traffic, identifying suspicious anomalies that indicate attacks. Contrasting PB-DID with traditional ML techniques, the authors showcase its superb effectiveness in identifying DoS and DDoS attacks [12].

Wei et al. propose a new hybrid deep learning model, AE-MLP, in their paper that is capable of addressing and classifying different types of DDoS attacks successfully. AE-MLP is a combination of AE and MLP to contain the DDoS attack since they have the best characteristics for tackling them. The AE-MLP approach starts from dimensionality reduction of the data, elimination of noise, and identification of important DDoS patterns. Finally, MLP categorizes features following a compression process into the class of attacks. Dimensionality reduction, feature extraction and capacity to incapsulate noise make AE-MLP more effective than other methods. Using the statistics of the CIC-DDoS2019 dataset entails appropriateness to achieve corresponding F1-scores of 98% from 99%. 0.1–2% for detection only 2% for detection and classification [13].

Jingbo Zhang et al. propose to solve the problem of predicting the ACK flood attacks in WSN by using the Recursive Neural Network. The RNN temporal dependencies are adapted to control network traffic to employ pre-emptive measures that will prevent traffic congestion and service disruption. This method can be a good candidate for preventing various forms of network-based attacks on IoT systems because the method can be adjusted for different traffic conditions [14]. Maranhao et al. continue the research on the identification of DDoS attacks in noisy traffic flows. At the same time, posing an MLP immune to noise, the authors offer a solution capable of handling real-world network challenges. This peculiar MLP design, combined with, let’s say it, proper data pre-processing and training, provides very good results. The model is highly accurate in detecting and distinguishing the DDoS attacks even when there is so much noise, and second, it is more superior to the other methods as far as this aspect is concerned. This is how it is possible to protect different network environments including enterprise centers, cloud offerings, or even IoT ecosystems that are vulnerable to DDoS attacks [15]. Ahmed et al. meet the urgent requirement of providing a DDoS solution in corporate networks at the moment of an attack. This SAD-F is derived from Apache Spark’s real-time analytics speed where large network data streams take seconds to be analyzed and filtered for attributes that scream DDoS attack. In fact, the intelligent algorithm of the SAD-F which is statistical thresholding or ML depending on the type can identify dubious actions much better and more accurately. This entails quicker response and management of the DDoS threat reduction process necessary to safeguard an enterprise’s networks from possible interruption. SAD-F is an extensible approach that can guarantee its effectiveness in enhancing different networks, including clouds or fragile IoT networks [16].

In the Bhardwaj study, the authors analyze the increasing problems of DDoS attacks on cloud platforms. For performing the analysis of massive amounts of data on the cloud network [17], the new method employs the proficiency of a suitably designed stacked sparse autoencoder (SSAE) incorporated with the hyperparameters. The SSAE works, such as a detective who sifts the data, extracts certain significant features, and emphasizes the trends that are typical of DDoS attacks. Hyperband tuning is like an army general who fine-tunes a team, especially the SSAE team to the best it can offer. They are combined in harmony, making them a dynamic duo that makes it possible to detect attacks at a very high success rate, especially in crowded traffic situations. This suggests that the cloud services contain a solid shield that protects them from interferences hence assuring them to deliver their services effectively [17]. Rahal also proposes an efficient and extensible distributed system for DDoS prediction, and in general, for botnet detection, that uses ML for the processing of the data coming from the analysis of the traffic of the networks [18]. These three man-oeuvres function as a single unit in giving high accuracy in identifying and forecasting invasions data manipulation, ML algorithms, and real-time surveillance. Since it is decentralized, it can easily cope with large data traffic and therefore can be used in large networks. The real-time monitoring system as informs of mitigation actions that need to be undertaken in realtime. The presented architecture is rather promising for networks’ protection in different environments, such as enterprise networks, cloud solutions, and the IoT context. This distributed architecture naturally scales well, is able to operate in real time, and integrates with existing network architecture and this transforms into an immensely valuable tool for mitigating against DDoS and botnet intrusions and other evolving cyber threats [18]. For instance, Sahoo put forward an ESVM model to deal with the evolving threat of DDoS attacks in SDNs while the ESVM model is made up of components as follows [19]: Unlike other DDOS detection methods that are outdone since they do not adapt to new attack techniques by learning from them, ESVM employs an evolutionary algorithm in the creation of an SVM classifier, thus making it more of a dynamic classifier; an ever-evolving one as it seeks to learn from the newly invented DDOS attacks. Similarly, the dynamic nature of adaptation means that ESVM continues to be extremely effective in identifying DDoS attacks even though the attackers become more intelligent and complex. Besides flexibility, the proposed ESVM has a high precision level that is higher than traditional SVM and given DDoS detection methods. Being capable of handling networks of considerable size and fitting in well with the SDN controllers as was seen above, it is an effective tool for the protection of enterprise networks, cloud solutions, and IoT [1],[21],[22], [23]. Gu et al. explain a new approach to DDoS detection that involves both the labeled and unlabeled data handling limitations of traditional methods due to reliance on only label data. Their unsupervised research model can handle both labeled and unlabeled data, making it suitable for application in real-life settings. As a highly effective and versatile solution, SSKMeans proves useful in protecting networks against DDoS attacks across different scenarios [20].

In this connection, a number of scholars have suggested various ML techniques for identifying DDoS attacks [24]. The CIC IDS 2017 is used in Nalayini and Katiravan [25] to detect DDoS attacks. In this work, we apply data preprocessing and K-fold cross-validation to evaluate several models. The study found that the Random Forest algorithm is way better and more efficient than other models in identifying DDoS attacks within a short period. Such conclusion results from assessment measures such as recall, accuracy, precision, and FAR (False Alarm Rate). As Lunkad and Singh [26], another work performs a study that provides information on different DDoS attack motivations and ways of operation. The details of the gradual change in the types of DDoS attacks and the measures applied in their prevention are presented in this text. Furthermore, it assesses several means for identifying such unauthorized access violations. Two approaches to identifying DDoS attacks are described in the work of [27]. Subsequently, a mathematical model is used to analyze the relationship between the time taken by the requests to reach the network and the performance of the network. Besides, the paper employs logistic regression and naive Bayes techniques for constructing the ML model aimed at DDoS attack detection through throughput analysis. Dell offers an elaborate system for the detection of DDoS attacks. The authors of [28] propose a separate algorithm aimed at the classification of DDoS attacks and achieve it by using the CCIDS2017 dataset. The future work of the proposed methodology is that the SVM classification algorithm is used to get a precision rate of 99%. 68%. Others include the size of the packets, the length of the packets, the time each flow is on the network, and the forward and backward packets among other characteristics of the packet. This shows how effective the method is in identifying the DDOS attacks as shown in the video. In particular, the study focuses on the CCIDS2017 dataset to detect DDoS attacks as demonstrated by Nath Rimal and Praveen [29].

To lessen the dimensions for the feature vector and cut down the time of complexity, we also employed PCA and considerably compressed the neural network model. In the selection of the output dimension, the use of PCA turns out to be more flexible than when LDA and other linear techniques of dimensionality reduction are used [30]. As for the packets dataset, the parameters are diverse including the transmission control protocol (TCP) flag, flow duration, header length, and length of the packet. By a SVM classification algorithm trained on this dataset, the proposed method is very effective at seeing DDOS traffic 99. 68% of the time. This establishes its efficiency in establishing the differences between an attack and a normal flow of traffic. The research indicated in Sudheer et al. [31] uses different types of ML models for the evaluation of the input information for the purpose of detecting DDoS attacks. To increase this accuracy, the authors utilize PCA and a random forest classifier for ranking the features based on their importance. The accuracy of the Decision Tree model is higher than the accuracy of the rest of the classifiers that were tested, and therefore the Decision Tree model can be considered as a very promising and valuable instrument for the detection of DDoS attacks [32]. Furthermore, prevention, as it is pointed out in the case of DDoS assaults, is the best way. The authors recommend packet filtering as a way of filtering out the bad packets in other to minimize DDoS attacks. It demonstrates the need for proactive defensive measures possible through an analysis of various approaches such as ingress/egress packet filtering, router-based packet filtering, and some statistical approaches like Packets Core.

3. DISCUSSION

Table 1 discusses ML and various methodologies used to combat DDoS attacks in multiple technological areas. Ranging from cloud computing and SDN environments to 5G networks and IoT devices, the table displays 19 entries.

TABLE 1: Discusses ML and various methodologies

4. CONCLUSION

In the end, this extensive literature review critically appraises 19 core studies that discuss the serious dangers posed by DDoS attacks. It also reviews various methodologies, with a primary focus on ML methods. The review includes new app the review includes some new methods, such as a revisited Gaussian Naive Bayes classifier for DDoS detection in cloud computing settings, a DEQSVC framework based on quantum computers, and a better AI model for finding DDoS attacks in SDN settings, promising technique to categorize DDoS attacks in 5G networks, showing unprecedented accuracy. Furthermore, the DDAD-SOEL approach is designed to counteract IoT device attacks caused by DDoS using Snake Optimizer and multiple deep learning models ahead of other methods in terms of precision, accuracy, as well as F1 score. In addition, the literature review stresses considering DDoS attacks in certain scenarios, such as SDNs, IoT devices, and clouds. The provided solutions have significant achievements, namely improved accuracy and precision, as well as suitability in the protection of infrastructure items. These innovations add significant value to the field, providing a wide variety of techniques that strengthen defensive mechanisms and reduce challenges caused by DDoS attacks. All in all, the results reflected ML and alternative methodologies needed to protect network security through reliable delivery of critical services due to expanding cyber threats.