Evaluating ML-based DDoS Detection with Grid Search Hyperparameter Optimization

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.

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.

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

Cyber attacks are a major concern for network administrators as the occurrences of such events are continuously increasing on the Internet. Software-defined networks (SDN) enable many management applications, but they may also become targets for attackers. Due to the separation of the data plane and the control plane, the controller appears as a new element in SDN networks, allowing centralized control of the network, becoming a strategic target in carrying out an attack. According to reports generated by security labs, the frequency of the distributed denial of service (DDoS) attacks has seen an increase in recent years, characterizing a major threat to the SDN. However, few research papers address the prevention of DDoS attacks on SDN. Therefore, this work presents a Systematic Mapping of Literature, aiming at identifying, classifying, and thus disseminating current research studies that propose techniques and methods for preventing DDoS attacks in SDN. When answering these questions, it was determined that the SDN controller was vulnerable to possible DDoS attacks. No prevention methods were found in the literature for the first phase of the attack (when attackers try to deceive users and infect the host). Therefore, the security of software-defined networks still needs improvement over DDoS attacks, despite the evident risk of an attack targeting the SDN controller.

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.

The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by using blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems.

Internet of Things (IoT) refers to the networked interconnection of everyday objects. IoT is an upcoming research field and is being regarded as the revolution in the world of communication because of its extensible applications in numerous fields. Due to open and self-assimilation nature of these networks they are highly prone to attacks. Because of this reason security is of primary concern here. The security attack can be of various types, the idea here is to prevent IoT networks from Distributed Denial of Service (DDoS) attack. The objective of Denial of Service (DoS) is to make the server resources unavailable to the intended user, and when several such DoS attacks are present in a network then the attack is known as a DDoS attack. Our strategy is to prevent DDoS attack in IoT networks by using Learning Automata (LA) concepts. In this paper, we present a Service Oriented Architecture (SOA) which is used as a system model for IoT here. SOA provides a platform to the developers using which they can develop various applications for IoT without any concern regarding the nature of the objects, thereby acting as a middleware. The DDoS prevention strategy has been targeted for the SOA based architecture for IoT. The simulation results show that the proposed scheme is effective in preventing DDoS attacks in IoT.

The Internet of Things enabled Wireless Sensor Networks (IoT-enabled WSNs) are highly susceptible to a wide range of Distributed Denial of Service (DDoS) attacks. While several methods have been developed to identify and mitigate these attacks, creating a unified strategy to counter multi-layer DDoS attacks remains a challenge due to the diverse nature of threats presented at different layers of IoT communication networks. To address this, a new protocol called Multi-Layer DDoS threat Detection and Mitigation (MLDDM) has been proposed. MLDDM's core capability lies in its audit trail analysis of trusted networks. By continuously calculating trust parameters across various IoT layers, the trust-based network tree analysis method evaluates each sensor node within the network. This analysis is then used to detect and prevent DDoS attacks. The MLDDM is a comprehensive approach designed to detect and mitigate DDoS attacks across the application, transport, internet, and edge layers. The proposed multi-layer trust-based network tree method analyzes each node to determine whether it is a potential attacker, identifying DDoS threats and their root causes. Once a DDoS attack is detected, MLDDM disconnects the affected nodes from the IoT-enabled WSNs until the next scheduled check. Simulation results demonstrate that the MLDDM protocol outperforms existing solutions in terms of throughput, Packet Delivery Rate (PDR), energy efficiency, communication latency, and overhead.

Distributed Denial of Service (DDoS) attacks continue to be one of the most severe threats in the Internet. The intrinsic challenge in preventing DDoS attacks is to distinguish them from legitimate flash crowds since two have many traffic characteristics in common. Today most DDoS detection techniques focus on finding parametric differences between the patterns in attack and legitimate traffic. However, such techniques are very sensitive to the threshold values set on the parameters and more importantly legitimate traffic features might be mimicked by smart attackers to generate requests that look like flash crowds. In this paper, we propose a framework for training networks for such smart attacks. Our framework is based on Deep Generative Network models and our contributions are two-fold.We first show that legitimate traffic features can be mimicked without explicitly modeling their distributions. Second, we introduce the concept of context-aware DDoS attacks. We show that an attacker can generate traffic that looks similar to flash crowds to be undetected for long periods of time. However, the ability of generating such attacks is constrained by the budget of the attacker. A context-aware attacker is the one that can intelligently use its budget to maximize the damage in the victim network. Our study provides a framework for training networks for such DDoS attack scenarios.

Objectives: To identify the Distributed Denial Of service (DDOS) attack in the network and generate a preventive path while performing the V2V communication. The objective of work is to reduce the communication delay and communication loss. Methods/Statistical Analysis: In this work a group controlled method is provided for vehicular network to observe the attack within the group. The parameter driven analysis is applied within the group and identified the attacker node. Once the attacker is identified, the preventive path is generated. The paper has presented the algorithmic model for preventive route formulation. Findings: In Vehicular Ad Hoc Network (VANET), the large node traffic exist which results heavy network communication. DDOS in this attack degrades overall network reliability. In this paper, a group controlled analysis model is proposed to identify the DDOS attacked node and to generate the preventive communication path. At the early stage, the Road Side Unit (RSU) region is processed under mobility parameters to generate the node groups. Each group is also defined with an established controller. Finally, the group communication is observed under communication parameters to identify the attacker node. The paper has explored the model and the associated work stages in detail. Application/Improvements: The work is defined specifically for vehicular network and to identify the DDOS attack. The group specific restricted region analysis is provided to recognize the attacker node and to generate the preventive path.

Today Distributed Denial of Service (DDoS) attacks are causing major threat to perform online business over the Internet. Our previous work proposed an automated model with a new packet marking technique and agent design to counteract DDoS within a single ISP domain. Our approach has many features that are required to minimize the DDoS attacks. For example, our model is invoked only during attack times, identifies the approximate source of attack with a single packet even in case of spoofed source address, identifies different attack signatures for different attacking sources, prevents the attack nearest to the attacking source, has very fast response for any changes in attack traffic pattern, is simple in its implementation and can be incrementally deployed. Though the proposed model has several advantages, prevention of the attack is limited to a single ISP domain. In this paper we extend our model to prevent DDoS attacks in multiple ISP domains by retaining all the advantages achieved in our previous work. We also propose a practical implementation of the extended model with a presently working architecture.

According to experts, by 2025, the IoT cyber infrastructure will incorporate 55 billion interconnected devices. Among other threats, this infrastructure and its hosts will be vulnerable to distributed denial of service (DDoS) attacks, in which the network and its hosts are flooded with false requests, thereby slowing the system or causing it to cease functioning completely. Because of the large amounts of traffic the devices comprising the IoT network will generate in the future, protecting it from such attacks is becoming increasingly important. This paper proposes a defensive algorithm designed to thwart DDoS attacks on IoT networks and presents the results of tests of the algorithm in various scenarios. The method defined by the algorithm consists of three steps: wavelet decomposition of the attack, checking the standard deviation, and collaborative prevention with Paxos protocol. The test results indicate that the proposed method can reduce amount of traffic, detection time, and number of false positives when network traffic exhibits a temporary, sudden increase.

Whispering through DDoS attack

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.

The paper aims to provide a summary with a reference to the Internet of Things (IoT) of the distributed denial of service (DDoS). The network research community has known about the denial of service (DoS), which is intended to stop legitimate users from accessing a particular network tool since the early 1980s. The aim of the paper is to define DDoS scope, classifications and opportunities for assailants who use IoT to conduct such operations and to use IoT in healthcare. The research approach is to analyze DDoS, IoT and the use of IoT literature in the health sector. The study found that DDOS attacks on IoT-specific devices have become a very easy phenomenon because network attacks are much simpler due to limited security protocols. IOT distribution companies have made limited efforts to enable security of these devices and protecting the data, thus making them susceptible to vulnerabilities. Due to weak security layers, the confidentiality, authenticity and integrity of private data collected by IoT devices are threatened by attackers. The current research identified the gap and importance for further research to provide a mechanism for detection and prevention of DDOS attack in the application layer of IoT healthcare devices in India. Considering the major challenges in IOTs in defending against external attacks and hacking, it is important that they have inherent frameworks warding off such attacks.