Unsupervised Models with LOF and PCA for Robust DDOS Attack Detection

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

Prevention and detection of DDOS attack in virtual cloud computing environment using Naive Bayes algorithm of machine learning

The internet network is mostly victimized to the Distributed Denial of Service (DDOS) attack, which is one that intentionally occupies the computing resources and bandwidth in order to deny that services to potential users. The attack scenario is to flood the packets immensely. If the attack source is single, then the attack is referred as denial of service (DOS) and if attack is sourced from divergent servers, then it is referred as DDOS. Over a decade many of the researchers considered the detection and prevention of DDOS attack as research objective and succeeded to deliver few significant DDOS detection and prevention strategies. How fast and early detection of DDOS attack is done in streaming network transactions is still a significant research objective in present level of internet usage. Unfortunately the current benchmarking DDOS attack detection strategies are failing to justify the objective called “fast and early detection of DDOS attack”. In order to this, in this paper we devised a Bio-Inspired Anomaly based application layer DDoS attack (App-DDOS Attack) detection that is in the aim of achieving fast and early detection. The proposed model is a bio-inspired bat algorithm that used to achieve the fast and early detection of the App-DDOS by HTTP flood. The experiments were carried out on bench marking CAIDA dataset and the results delivered are boosting the significance of the proposed model to achieve the objective of the paper.

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.

Abstract: DDoS attacks also known as distributed denial of service (DDoS) attacks have emerged as one of the most serious and fastest-growing threats on the Internet. Denial-of-service (DDoS) attacks are an example of cyber-attacks that target a specific system or network in an attempt to render it inaccessible or unusable for a period of time. As a result improving the detection of diverse types of DDoS cyber threats with better algorithms and higher accuracy while keeping the computational cost under control has become the most significant component of detecting DDoS cyber threats. DDoS (distributed denial-of-service) attack originates from many sources scattered over multiple network locations. DoS attacks are primarily motivated by the desire to significantly degrade the performance or completely consume a certain resource and a process to exploit a machine defect and cause failure of a processing or exhausting the system resources by exploiting a system flaw. Yet another method of assaulting the target system is to flood the network and monopolies it so preventing anyone else from utilizing it. DDoS attack has a high impact on crashing the network resources making the target servers unable to support the valid users.

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.

— In the modern computer world, use of the internet is increasing day by day. However, the increasing use of the internet creates some security issues. These days, such new type of security attacks occurs every day and it is not easy to detect and prevent those attacks effectively. One common method of attack involves sending large amount of request to site or server and server will be unable to handle such huge requests and site will be offline for many days. This type of attack is called distributed denial of service (DDOS) attack, which act as a major security threat to internet services and most critical attack for cyber security world. Detection and prevention of Distributed Denial of Service Attack (DDoS) becomes a crucial process for the commercial organizations that uses the internet. Different approaches have been adapted to process traffic information collected by monitoring stations (Routers and Servers) to distinguish malicious traffic such as DDoS attack from normal traffic in Intrusion Detection Systems (IDS). In general, Machine learning techniques can be designed and implemented with the intrusion systems to protect the organizations from malicious traffic. Specifically, supervised clustering techniques allow to effectively distinguishing the normal traffic from malicious traffic with good accuracy. In this paper, machine learning algorithms are used to detect DDoS attacks collected from “KDD cup 99 Dataset”, pre- processing and feature selection technique is used on the dataset to enhance the performance of the classifiers and reduce the detection time. The classification algorithms such as C4.5 decision tree and Navie Bayes is applied on the training dataset and the implementation of the algorithm is done using spyder tool. The performance comparison of algorithms is shown using confusion matrix and it is found that C4.5 decision is more efficient in detection of DDOS attack. The proposed method can be used as DDoS defense system.

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.

The emergency response system, water and wastewater treatment facilities, oil and gas pipelines, electrical power transmission and distribution, wind farms, defense systems, and large communication networks are all expanding and becoming more vulnerable. That includes DDoS (Distributed Denial of Service) attacks on distributed control systems (DCS) and supervisory control and data acquisition (SCADA) systems. Most cyberattacks on the internet take the form of DOS or DDOS attacks, and their prevalence is rapidly increasing. Forcing people to go without essential services for even a short period of time can cost millions of dollars and put lives at risk. The primary focus of this review paper is an examination and evaluation of some of the more recent recommendations for improving the SCADA system. This review paper focuses on the key research problems that need to be solved to fill the gaps in SCADA system security.

Distributed denial of service (DDoS) attacks pose a significant security risk, particularly with the increasing reliance on cloud computing and information technology (IT). These attacks not only allow unauthorized users to access services but also deny legitimate users the ability to utilize them. Traditional antivirus solutions and firewalls prove insufficient in detecting DDoS attacks within large networks. Intrusion detection systems (IDS) are essential for detecting unauthorized or malicious activities and ensuring the confidentiality, integrity, and availability of services. However, traditional IDS often rely on predefined signatures and patterns, making them susceptible to evasion tactics. In response, this research introduces a deep learning (DL)-based IDS that integrates convolutional neural networks (CNN) with principal component analysis (PCA) and explores the application of vision transformers (ViT). The proposed hybrid model was tested on the CICDDoS2019 dataset, achieving a notable improvement in detection accuracy. Specifically, the CNN-based model initially identified DDoS attacks with an accuracy of 99.72%. Upon integrating ViT, the model’s accuracy further improved to 99.99%. This innovative approach signifies a considerable advancement in the detection capabilities for DDoS attacks and underscores the potential for integrating more sophisticated DL models into cybersecurity defenses.

The popularization of the Internet of Vehicles (IoV) technology has not only brought many conveniences but also caused security issues such as distributed denial of service (DDoS) attacks. Using deep learning methods to deal with DDoS attacks has achieved remarkable results. However, to improve the effectiveness of the model, a large amount of high-quality data is needed. This data is often scattered among different vehicle operating companies or organizations, making it difficult to train effective attack detection models by centralizing the data. To solve this problem, this paper proposes a DDoS attack detection method based on reinforced federated learning in the IoV environment. This method applies reinforcement learning to the aggregation process of federated learning to optimize the DDoS attack detection algorithm. In this method, the fog node server of the IoV serves as a participant in learning, uses local data to conduct reinforcement learning, and uploads the trained model parameters to the edge server of the IoV for aggregation. By learning the adaptability of the fog node model to the environment, reinforced federated learning can improve the accuracy of the DDoS attack detection model in terms of unbalanced data distribution and model convergence efficiency. Experiments were carried out using the public DDoS attack dataset. The experimental results showed that the proposed method exhibited excellent performance in the IoV environment, providing new ideas and methods for the research of IoV security.

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.

Distributed Denial of Service (DDOS) attack is one of the most common network attacks. DDoS attacks are becoming more and more diverse, which makes it difficult for some DDoS attack detection methods based on single network flow characteristics to detect various types of DDoS attacks, while the detection methods of multi-feature DDoS attacks have a certain lag due to the complexity of the algorithm. Therefore, it is necessary and urgent to monitor the trend of traffic change and identify DDoS attacks timely and accurately. In this paper, a method of DDoS attack detection based on deep belief network feature extraction and LSTM model is proposed. This method uses deep belief network to extract the features of IP packets, and identifies DDoS attacks based on LSTM model. This scheme is suitable for DDoS attack detection technology. The model can accurately predict the trend of normal network traffic, identify the anomalies caused by DDoS attacks, and apply to solve more detection methods about DDoS attacks in the future.

Throughout the past few years, the Internet of Things (IoT) has grown in popularity because of its ease of use and flexibility. Cyber criminals are interested in IoT because it offers a variety of benefits for users, but it still poses many types of threats. The most common form of attack against IoT is Distributed Denial of Service (DDoS). The growth of preventive processes against DDoS attacks has prompted IoT professionals and security experts to focus on this topic. Due to the increasing prevalence of DDoS attacks, some methods for distinguishing different types of DDoS attacks based on individual network features have become hard to implement. Additionally, monitoring traffic pattern changes and detecting DDoS attacks with accuracy are urgent and necessary. In this paper, using Modified Whale Optimization Algorithm (MWOA) feature extraction and Hybrid Long Short Term Memory (LSTM), shown that DDoS attack detection methods can be developed and tested on various datasets. The MWOA technique, which is used to optimize the weights of the LSTM neural network to reduce prediction errors in the hybrid LSTM algorithm, is used. Additionally, MWOA can optimally extract IP packet features and identify DDoS attacks with the support of MWOA-LSTM model. The proposed MWOA-LSTM framework outperforms standard support vector machines (SVM) and Genetic Algorithm (GA) as well as standard methods for detecting attacks based on precision, recall and accuracy measurements.

Distributed Denial of Service (DDoS) constitutes major threat to both traditional and SDN networks. An attacker can launch a DDoS attack to exhaust either the controller or other network resources, such as switches, or both. There are different DDoS attacks such as UDP flood, SYN flood, Ping of death, ICMP flood and HTTP flood. Among these, SYN and HTTP flood are the most common attacks these days. In this thesis, we focus on developing a security scheme to alleviate the DDoS attacks with spoofed and non-spoofed IP addresses in the SDN environment. First we use a simple detection mechanism that utilizes a time series window-based traffic statistic measurement to detect possible SYN flood and/or HTTP flood DDoS attacks. To reduce false positives, further investigation of traffic is done based on valid source IP address scheme and single flow packet scheme to separate legitimate traffic from attack traffic. Once the attack is detected, the security scheme deploys a number of mitigation methods to alleviate the attack. For the SYN flood attack, the mitigation method of Source IP address filtering is used to permit traffic only with valid source IP addresses to enter the network. For HTTP flood attack mitigation, a mitigation method is used to identify the attack sources and discard the traffic from those sources. We test our proposed scheme with other DDoS attacks such as ICMP flood attack and UDP flood attacks. We also compare our scheme with other security schemes found in the literature. The result shows that our proposed scheme can effectively protect controller and other network resources from some common DDoS attacks, and that our scheme allows more legitimate traffic connections with less false positives in comparison with other schemes.