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

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.

The exponential growth of interconnected systems across the Internet of Things (IoT), Software-Defined Networks (SDN), and cloud environments has led to a drastic increase in the scale and advancement of Distributed Denial of Service (DDoS) attacks. Conventional defences based on machine learning are often static, traffic-centric, and lack adaptivity to dynamic network behaviour, resulting in high false-positive rates and delay in mitigation responses. To address these challenges, this paper presents a conceptual framework for an AI-driven, context-aware DDoS detection and mitigation system. The proposed approach employs a optimized Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) model for efficient, real-time detection of DDoS attacks. The model captures spatial and temporal correlations in traffic while incorporating contextual parameters such as Quality of Service (QoS), Service Level Agreement (SLA) priority, and workload telemetry. These contextual attributes enable the model to distinguish legitimate network fluctuations from malicious activities, so that the false positive rate will be reduced without compromising accuracy. For adaptive response, a Reinforcement Learning (RL)-based mitigation layer—exploiting Deep Q-Network (DQN) and Actor–Critic (AC) variants—continuously learns optimal mitigation actions based on real-time feedback. The interaction between detection and mitigation components forms a context-aware feedback loop, enabling dynamic policy refinement and continuous system improvement. Comprehensive literature analysis reveals that existing AI-based DDoS defense mechanisms lack integration between detection and mitigation, and often overlook contextual intelligence. The proposed framework bridges this gap by introducing a self-adaptive, feedback-driven defense system capable of maintaining detection precision, low latency, and QoS preservation in heterogeneous environments.

Internet-based computing has lead to an emergence of a large number of threats. One of the major threat is DDoS (Distributed Denial of Service) attack. Recent incidents have shown that DDoS attacks have the capability of shutting a business not for a day but weeks. DDoS attacks have a greater impact on multi-tenant clouds than traditional infrastructure. DDoS attacks in the cloud, take the shape of EDoS (Economic denial of sustainability) attacks. In EDoS, instead of “Service Denial”, economic harms occur due to fake resource usage and subsequent addition or buying of resources using on-demand provisioning. To detect and mitigate DDoS attacks in the cloud, we argue that on-demand resource allocation (known as auto-scaling) should also be looked, in addition to network or application layer mitigation. We have proposed a novel mitigation strategy, DARAC, which makes auto-scaling decisions by accurately differentiating between legitimate requests and attacker traffic. Attacker traffic is detected and dropped based on human behavior analysis based detection. We also argue that most of the solutions in the literature, do not pay much attention to the service quality to legitimate requests during an attack. We calculate the share of legitimate clients in resource addition/buying and make subsequent accurate auto-scaling decisions. Experimental results show that DARAC mitigates various DDoS attack sets and take accurate and quick auto-scaling decisions for various legitimate and attacker traffic combinations saving from EDoS. We also show how proposed mechanism could make “arms-race” very difficult for the attackers as the resource need to defeat DARAC mechanism on a very small capacity server is huge. Results also show significant improvements in the average response time of the web-service under attack, in addition to infrastructure cost savings up to 50 % in heavy attack cases.KeywordsCloud ComputingVirtual MachineAttack ScenarioIdle ResourceCloud ConsumerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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

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.

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 absence of standards and the diverse nature of the Internet of Things (IoT) have made security and privacy concerns more acute. Attacks such as distributed denial of service (DDoS) are becoming increasingly widespread in IoT, and the need for ways to stop them is growing. The use of newly formed Software-Defined Networking (SDN) significantly lowers the computational burden on IoT network nodes and makes it possible to perform more security measurements. This paper proposes an SDN-based, four-module DDoS attack detection and mitigation framework for IoT networks called FMDADM. The proposed FMDADM framework comprises four main modules and five-tier architecture. The first module implements an early detection process based on the average drop rate (ADR) principle using a 32-packet window size. The second module uses a novel double-check mapping function (DCMF), that aids in earlier attack detection at the data plane level. The third module is an ML-based detection application comprising four phases: data preprocessing, feature extraction, training and testing, and classification. This module detects DDoS attacks using only seven features: two selected and five newly computed features. The last module introduces an attack mitigation process. We applied the proposed framework to three test cases: one single-node attack test case and two multi-node attack test cases, all with real IoT traffic generated and deployed in Mininet-IoT. The proposed FMDADM framework efficiently detects DDoS attacks at high and low rates, can discriminate between attack traffic and flash crowds, and protects both local and remote IoT nodes by preventing infection from propagating to the ISP level. The FMDADM outperformed most existing cutting-edge approaches across ten different evaluation criteria. According to the experimental results, FMDADM achieved the following accuracy, precision, F-measure, recall, specificity, negative predictive value, false positive rate, false detection rate, false negative rate, and average detection time benchmarks:- 99.79%, 99.43%, 99.77%, 99.79%, 99.95%, 00.21%, 00.91%, 00.23%, and 2.64 μs, respectively.

Distributed Denial of Service (DDoS) attacks pose a significant threat to network applications. Many countermeasures have been proposed to tackle such attacks. This paper focuses on DDoS mitigation techniques, the practical way to filter attack traffic and keep victims alive. To rate limit attack traffic with as little normal traffic affected as possible, not just the amount of increased volume, but also how increased traffic is propagated in the network, denoted by traffic increasing patterns, is considered. In this paper, we propose unfair rate limiting (URL), in which traffic aggregates are given different priority by extracting increasing patterns and analyzing their relationship with DDoS attacks. Aggregates more likely to include attacks traffic are punished harder during mitigation. Two URL mechanisms are presented, Local URL (LoURL) and Collaborative URL (CoURL). LoURL works locally, while CoURL deals with locally indeterminate patterns based on global information, and thus achieves more effective mitigation. We evaluate the performance of proposed mechanisms through simulation. The simulation results show that both LoURL and CoURL can effectively mitigate DDoS attacks. CoURL outperforms LoURL with regard to the percentage of filtered attack traffic.

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.

Distributed Denial of Service (DDoS) is a type of attack in which the attacker tries to degrade the performance of server (or) network so that the server may not provide service to legitimate users. Since there is a huge increase in DDoS attacks which has created many financial losses in the E-Commerce world. To avoid the losses incurred because of DDoS attacks, efficient mechanisms are required to counter these attacks. In the proposed approach routers collectively try to mitigate the DDoS attack on the server. There are three steps in the proposed approach, initially, for attack detection and classification destination router (which is attached to the victim) monitors continuously the traffic pattern. Second, once the attack is detected destination router tries to balance the load using the NAT (Network Address Translator). Third, whenever the attack is detected to mitigate different types of attacks, the signature is pushback to upstream routers so that the upstream routers start monitoring the traffic and apply the mitigation mechanism depending on type of attack detected.

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

Strong defence mechanisms are necessary to defend the availability and integrity of network infrastructure in the ever-changing cybersecurity landscape, where Distributed Denial of Service (DDoS) attacks are becoming more common. The capacity of Deep Learning (DL) models to automatically learn feature representations and discern complicated patterns within network traffic data has made them a potential strategy for DDoS attack finding and mitigation. The design of a critical cybersecurity threat that disrupts network operations and causes considerable economic losses internationally also affects how well DL models fight against developing attacks. The Shepard Interpolation Neural Network (SINN) classifier, fine-tuned with the Artificial Rabbit Optimiser (ARO), is proposed as a new method for DDoS assault detection in this paper. The technique takes advantage of the Kaggle DDoS dataset, which is naturally skewed and poses problems for conventional ML models. The solution is to use the Synthetic Minority Oversampling Technique (SMOTE) to level the playing field in the dataset, which will result in better model presentation in every class. Thanks to ARO’s hyperparameter optimisation, the SINN classifier—which is already well-known for its interpolation capabilities and resilience when dealing with nonlinear patterns—achieves even better detection accuracy. Experiments show that the suggested model outdoes state-of-the-art machine learning tactics for DDoS attack finding in terms of accuracy, recall, and F1-score. Reliable classification results are contributed to by the incorporation of SMOTE, which successfully mitigates bias induced by class imbalance. In addition to establishing a scalable framework for practical applications, this work showcases SINN-ARO’s capabilities in protecting networks from ever-changing cyber threats. Investigating how well this approach works in real-time, dynamic network settings and expanding its applicability to identify different forms of cyberattacks are the primary goals of future studies.

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.

One of the most notorious security issues in the IoT is the Distributed Denial of Service (DDoS) attack. Using a large number of agents, DDoS attack floods the host server with a huge number of requests causing interrupting and blocking the legitimate user requests. This paper proposes a detection and prevention algorithm for DDoS attacks. It is divided into two parts, one for detecting the DDoS attack in the IoT end devices and the other for mitigating the impact of the attack placed on the border router. Also, it has the ability to differentiate the High-rate from the Lowrate DDoS attack accurately and defend against these two types of attacks. It is implemented and tested against different scenarios to dissect their efficiency in detecting and mitigating the DDoS attack.

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.