Machine Learning-Driven Cybersecurity Solutions for Enhanced Smart Grids and Critical Infrastructure: A Review

A Distributed Denial of Service (DDoS) attack occurs when an attacker tries to disrupt a network, service or website by flooding huge numbers of packets on the internet traffic. Detecting DDoS attacks serves the goal of spotting and addressing them promptly to reduce their effects on the network, system or service being targeted. Detecting Distributed Denial of Service (DDoS) attacks is crucial, for people, companies and network managers. The detection of DDoS attacks has ranging uses in industries such as network security safeguarding websites, managing cloud services ensuring the security of online systems and services. Detecting DDoS attacks is essential for safeguarding infrastructure upholding service availability and guaranteeing the security of online systems and services. To achieve this objective, we proposed a framework to detect DDoS attacks including six steps. In step one, we start by gathering information, which includes network activity and system records, for operations as well as instances of DDoS attacks. Step two, we identify characteristics of the data collected such as patterns in network traffic, packet details, IP addresses, types of protocols used and more. Step three, we utilize algorithms for feature selection such as Salp Swarm Algorithm (SSA), Gray Wolf Algorithm (GWA), Particle Swarm Algorithm (PSO) to pinpoint the features that can distinguish between normal activities and DDoS attack patterns. After that in step four, we divide the processed dataset into sections for training and testing purposes to develop and assess the machine learning models such as SVM (support vector machine), and KNN (K-nearest neighbor). Step five we develop a classification model using machine learning techniques like decision trees, forests, support vector machines (SVM) logistic regression models or neural networks. Finally, we assess the effectiveness of models through metrics such as accuracy rates, precision levels, recall rates, and F1 scores. The results show that the proposed models achieve high results (99.9%). In summary detecting DDoS attacks is crucial for protecting networks, systems and online services against disruptions.

This research focuses on enhancing the detection mechanisms for Distributed Denial of Service (DDoS) attacks using advanced machine learning techniques. We explore two innovative approaches: a metaclassifier stacking model and a transfer learning model, utilizing the CICDDoS2019 and CICIDS2017 datasets for training and evaluation. The first approach integrates K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF) algorithms through a logistic regression metaclassifier. This ensemble method harnesses the strengths of each algorithm, leading to improved metrics such as accuracy, precision, recall, and F1-score. The second approach employs transfer learning, where a pre-trained Artificial Neural Network (ANN) on the CICIDS2017 dataset is fine-tuned with the CICDDoS2019 dataset. This technique demonstrates the benefits of knowledge transfer, achieving high detection performance with reduced training time. Our findings reveal that both methods significantly enhance DDoS detection. The metaclassifier approach delivers superior performance metrics but requires more computational resources. In contrast, the transfer learning approach provides an efficient balance between performance and computational demand, ideal for rapid deployment scenarios. In summary, this study highlights the efficacy of combining multiple algorithms and leveraging pre-trained models to improve DDoS detection accuracy and efficiency. These approaches offer promising directions for developing robust and effective DDoS detection systems.

Distributed Denial of Service (DDoS) attack is a serious threat to commercial service network. DDoS attack has been studied for years. However, detecting and relieving DDoS attacks are still a problem. Especially, nowadays more and more DDoS attacks produce heavy network traffic, it is hard to response rapidly because that needs high processing performance to process massive traffic data. With big data technology, volumes of network traffic data can be processed much faster. Apache Spark can process a great amount of data in a reasonable time so that DDoS attack can be detected in time. Besides, it is difficult to modify the network configuration in traditional network. With Software-Defined Networking (SDN), a new paradigm in networking, networking can be controlled by programs, which makes modifying the network configuration easier. In this paper, a DDoS detection and mitigation system framework in SDN is introduced, a framework that can control network based on analyzing the network traffic data. Comparing to the traditional defense methods of DDoS attack, the framework can response to DDoS attack by rules automatically.

DDoS (Distributed Denial of Service) attacks consist of a large number of compromised computer systems that launch joint attacks at a targeted victim, such as a server, website, or other network equipment, simultaneously. DDoS has become a widespread and severe threat to the integrity of computer networks. DDoS can lead to system paralysis, making it difficult to troubleshoot. As a key building block in the development of an integrated defense system, it is essential to detect DDoS attacks as early as possible. With the popularization of artificial intelligence, more and more researchers apply machine learning (ML) and deep learning (DL) to the detection of DDoS attacks and have achieved satisfactory accomplishments. The complexity and sophistication of DDoS attacks have kept increasing and evolving since the first DDoS attack was reported in 1996. Regarding the headways in this problem, a new type of DDoS attack, named adversarial DDoS attack, is investigated in this study. The generating adversarial DDoS traffic is carried out using a symmetric Generative Adversarial Networks (GAN) architecture called Cycle-GAN to demonstrate the severe impact of adversarial DDoS attacks. Experiment results reveal that the synthesized traffic can easily penetrate ML-based detection systems, including Random Forest, KNN (k-Nearest Neighbor), SVM (Support Vector Machine), and Naïve Bayes. These alarming results intimate the urgent need for countermeasures against adversary DDoS attacks.

Software Defined Network (SDN) is the new era of networking technology based on a centralized controller that separates the switch hardware from its operating software. The most important challenge is the security of SDN and the most prominent attack is the Distributed Denial of Service (DDoS) attack. Some of the research work done so far detects DDoS attacks using a threshold, which is usually assumed without proper scientific reason and hence may not be always accurate. The mitigation techniques used by some researchers block the host from sending the network traffic beyond a threshold, by installing drop rules in the flow table of the switch connected to that host. Doing so will not only block the attack traffic but also the genuine ones from other applications of that host. In this paper, we propose a model that calculates the threshold limit for the type of applications sending data to a particular switch, in real-time using a machine learning (ML) model, and determines whether that application traffic is DDoS traffic. After the detection, only application type sending DDoS traffic is blocked while other genuine applications are allowed to send the network traffic without any interruption. The use of a dynamic threshold, based on the current network traffic, will help in detecting DDoS efficiently.

a serious nuisance for network security could be a Distributed Denial of Service (DDoS) attack. The advent of the technological era has also brought along the threat of DDoS attacks for a variety of services and applications that use the Internet. Firms can incur huge financial losses even if there is a disruption in services for a fraction of a period. Analogous to a DDoS attack is the Flash Crowd (FC), in which a particular service is assessed by many legitimate users concurrently, which results in the denial of service. Overloading of network resources is a common issue associated with both these events, which impact CPU, available bandwidth, and memory for legitimate users, thereby leading to limited accessibility. Many researchers have put effort to differentiate between these two kinds of traffic, but none could come up with an effective solution. In this study, we propose an adaptive agent-based model, known as an adaptive application layer flooding protection (AALFP) model, for DDoS and FC flooding attacks. The AALFP model is aimed to protect the network application layer (NAL) against such attacks. Importantly, the model distinguishes and separates normal from abnormal traffic, and then blocks illegitimate traffic. Note that, to operate two traffic filters, the agent relies on the following three parameters of normal traffic intensity, traffic attack behaviour, and IP address history log. An example scenario is provided to simulate the model's application.

Deep learning techniques for DDoS attack detection: Concepts, analyses, challenges, and future directions

The rapid growth of the internet and the increasing reliance on digital infrastructures have posed significant challenges to cybersecurity. Among the other variants of attacks, Distributed Denial of Service (DDoS) attacks have emerged as one of the most destructive and common threats. These attacks disrupt or slow down network services by overwhelming the network infrastructure with a massive volume of malicious traffic. To effectively identify and mitigate DDoS attacks, machine learning techniques have been extensively employed in intrusion detection systems. Machine learning approaches offer the advantage of automating the detection process by learning patterns and characteristics of DDoS attacks from historical data. Researchers have explored various machine learning algorithms such as K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes to classify and detect DDoS attacks. These algorithms leverage features extracted from network traffic data, including packet size, packet delay patterns, and traffic behaviour, to differentiate between normal and malicious traffic.

Fiber optic networks (FONs) are considered the backbone of telecom companies worldwide. However, the network elements of FONs are scattered over a wide area and managed through a centralized controller based on intelligent devices and the internet of things (IoT), with actuators used to perform specific tasks at remote locations. During the COVID-19 pandemic, many telecom companies advised their employees to manage the network using the public internet (e.g., working from home while connected to an IoT network). Theses IoT devices mostly have weak security algorithms that are easily taken-over by hackers, and therefore can generate Distributed Denial of Service (DDoS) attacks in FONs. A DDoS attack is one of the most severe cyberattack types, and can negatively affect the stability and quality of managing networks. Nowadays, software-defined networks (SDN) constitute a new approach that simplifies how the network can be managed through a centralized controller. Moreover, machine learning algorithms allow the detection of incoming malicious traffic with high accuracy. Therefore, combining SDN and ML approaches can lead to detecting and stopping DDoS attacks quickly and efficiently, especially compared to traditional methods. In this paper, we evaluated six ML models: Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, and Random Forest. The accuracy reached 100% while detecting DDoS attacks in FON with two approaches: (1) using SVM with three features (SOS, SSIP, and RPF) and (2) using Random Forest with five features (SOS, SSIP, RPF, SDFP, and SDFB). The training time for the first approach was 14.3 s, whereas the second approach only requires 0.18 s; hence, the second approach was utilized for deployment.

Cloud computing facilitates the users with on-demand services over the Internet. The services are accessible from anywhere at any time. Despite the valuable services, the paradigm is, also, prone to security issues. A Distributed Denial of Service (DDoS) attack affects the availability of cloud services and causes security threats to cloud computing. Detection of DDoS attacks is necessary for the availability of services for legitimate users. The topic has been studied by many researchers, with better accuracy for different datasets. This article presents a method for DDoS attack detection in cloud computing. The primary objective of this article is to reduce misclassification error in DDoS detection. In the proposed work, we select the most relevant features, by applying two feature selection techniques, i.e., the Mutual Information (MI) and Random Forest Feature Importance (RFFI) methods. Random Forest (RF), Gradient Boosting (GB), Weighted Voting Ensemble (WVE), K Nearest Neighbor (KNN), and Logistic Regression (LR) are applied to selected features. The experimental results show that the accuracy of RF, GB, WVE, and KNN with 19 features is 0.99. To further study these methods, misclassifications of the methods are analyzed, which lead to more accurate measurements. Extensive experiments conclude that the RF performed well in DDoS attack detection and misclassified only one attack as normal. Comparative results are presented to validate the proposed method.

Software Defined Networking (SDN) is widely used in many practical contexts and provides a method for managing networks by separating the data plane from the control plane. However, because of its centralized control logic, SDN is particularly susceptible to Distributed Denial of Service (DDoS) attacks. In numerous studies, methods based on Machine Learning (ML) have been proposed to identify DDoS attacks in the context of SDN. However, the proposed methods are resource intensive and unreliable in a large-scale SDN where a huge amount of traffic data is produced from control and data planes. This may exhaust computational resources, deteriorate the performance of the network, or even result in the shutdown of network systems as a result of resource exhaustion. To deal with the above issues, this paper presents a real-time DDoS detection and mitigation method in a multi-controller SDN environment using cloud computing. We propose to run the Machine Learning development cycle on a secured server in a virtual machine. Then we deploy back the trained model into the SDN controllers. All the exchanges between the virtual machine and the controllers are secured. In this way, we configure these controllers to detect and mitigate DDoS in SDN in real-time. Through useful experiments, we demonstrate that our method is secure, reliable, and efficient.

The complexity of handling modern apps is causing older network architectures to struggle. As a result, Software Defined Networking (SDN) has developed into a paradigm-shifting network framework that enables administrators to exercise programmable control over whole network infrastructures. This development makes it easier to configure and operate large networks, but it also adds vulnerabilities, including the possibility of Distributed Denial of Service (DDoS) assaults that could take down the SDN controller. Integrating security tools like firewalls, intrusion detection and prevention systems, and anti-DDoS solutions is crucial to reducing this danger. In the context of SDN, leveraging the analytical power of Machine Learning (ML) algorithms to analyse network traffic patterns is a key tactic for fending off DDoS attacks. Effective defence systems are supported by these ML models, which can distinguish between normal network behaviour and DDoS assault traffic with ease. Decision trees (DT), support vector machines (SVM), and artificial neural networks (ANN) are examples of supervised ML techniques that have evolved into crucial tools in this field. These algorithms are developed utilising datasets with annotations that include examples of both legitimate and malicious network traffic. Once trained, the models may be used to instantly categorise incoming traffic, making it easier to identify and contain any DDoS attacks in real-time.

Software-defined networking (SDN) has reformed the traditional approach to managing and configuring networks by isolating the data plane from control plane. This isolation helps enable centralized control over network resources, enhanced programmability, and the ability to dynamically apply and enforce security and traffic policies. The shift in architecture offers numerous advantages such as increased flexibility, scalability, and improved network management but also introduces new and notable security challenges such as Distributed Denial-of-Service (DDoS) attacks. Such attacks focus on affecting the target with malicious traffic and even short-lived DDoS incidents can drastically impact the entire network’s stability, performance and availability. This comprehensive review paper provides a detailed investigation of SDN principles, the nature of DDoS threats in such environments and the strategies used to detect/mitigate these attacks. It provides novelty by offering an in-depth categorization of state-of-the-art detection techniques, utilizing machine learning, deep learning, and federated learning in domain-specific and general-purpose SDN scenarios. Each method is analyzed for its effectiveness. The paper further evaluates the strengths and weaknesses of these techniques, highlighting their applicability in different SDN contexts. In addition, the paper outlines the key performance metrics used in evaluating these detection mechanisms. Moreover, the novelty of the study is classifying the datasets commonly used for training and validating DDoS detection models into two major categories: legacy-compatible datasets that are adapted from traditional network environments, and SDN-contextual datasets that are specifically generated to reflect the characteristics of modern SDN systems. Finally, the paper suggests a few directions for future research. These include enhancing the robustness of detection models, integrating privacy-preserving techniques in collaborative learning, and developing more comprehensive and realistic SDN-specific datasets to improve the strength of SDN infrastructures against DDoS threats.

A distributed denial of service (DDoS) attack represents a major threat to service providers. More specifically, a DDoS attack aims to disrupt and deny services to legitimate users by overwhelming the target with a massive number of malicious requests. A cyberattack of this kind is likely to result in tremendous economic losses for businesses and service providers due to increasing both operating and financial costs. In recent years, machine learning (ML) techniques have been widely used to prevent DDoS attacks. Indeed, many defense systems have been transformed into smart and intelligent systems through the use of ML techniques, which allow them to defeat DDoS attacks. This paper analyzes recent studies concerning DDoS detection methods that have adapted single and hybrid ML approaches in modern networking environments. Additionally, the paper discusses different DDoS defense systems based on ML techniques that make use of a virtualized environment, including cloud computing, software-defined network, and network functions virtualization environments. As the development of the Internet of Things (IoT) has been the subject of significant research attention in recent years, the paper also discusses ML approaches as security solutions against DDoS attacks in IoT environments. Furthermore, the paper recommends a number of directions for future research. This paper is intended to assist the research community with the design and development of effective defense systems capable of overcoming different types of DDoS attacks.

As organizations increasingly rely on network services, the prevalence and severity of Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks have emerged as significant threats. The cornerstone of effectively addressing these challenges lies in the timely and precise detection capabilities offered by advanced intrusion detection systems (IDS). Hence, an innovative IDS framework is introduced that seamlessly integrates the extended Berkeley Packet Filter (eBPF) with powerful machine learning algorithms—specifically Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and TwinSVM—enabling unparalleled real‐time detection of DDoS attacks. This cutting‐edge solution provides a robust and scalable IDS framework to combat DoS and DDoS threats with high efficiency, leveraging eBPF's capabilities within the Linux kernel to bypass typical user space constraints. The methodology encompasses several key steps: (a) Collection of data from the renowned CIC‐IDS‐2017 repository; (b) Processing the raw data through a meticulous series of steps, including transmission, cleaning, reduction, and discretization; (c) Utilizing an ANOVA F‐test for the extraction of critical features from the preprocessed data; (d) Application of various ML algorithms (DT, RF, SVM, and TwinSVM) to analyze the extracted features for potential intrusion; (e) Implementing an eBPF program to capture network traffic and harness trained model parameters for efficient attack detection directly within the kernel. The experimental results reveal outstanding accuracy rates of 99.38%, 99.44%, 88.73%, and 93.82% for DT, RF, SVM, and TwinSVM, respectively, alongside remarkable precision values of 99.71%, 99.65%, 84.31%, and 98.49%. This high‐speed, accurate detection model is ideally suited for high‐traffic environments such as data centers. Furthermore, its foundational architecture paves the way for future advancements, including the potential integration of eBPF with XDP to achieve even lower‐latency packet processing. The experimental code is available at the GitHub repository link: https://github.com/NemalikantiAnand/Project .