Prevent Distributed Denial Of Service Attacks Research Articles

Securing IoT Networks from DDoS Attacks Using a Temporary Dynamic IP Strategy.

The progression of the Internet of Things (IoT) has brought about a complete transformation in the way we interact with the physical world. However, this transformation has brought with it a slew of challenges. The advent of intelligent machines that can not only gather data for analysis and decision-making, but also learn and make independent decisions has been a breakthrough. However, the low-cost requirement of IoT devices requires the use of limited resources in processing and storage, which typically leads to a lack of security measures. Consequently, most IoT devices are susceptible to security breaches, turning them into "Bots" that are used in Distributed Denial of Service (DDoS) attacks. In this paper, we propose a new strategy labeled "Temporary Dynamic IP" (TDIP), which offers effective protection against DDoS attacks. The TDIP solution rotates Internet Protocol (IP) addresses frequently, creating a significant deterrent to potential attackers. By maintaining an "IP lease-time" that is short enough to prevent unauthorized access, TDIP enhances overall system security. Our testing, conducted via OMNET++, demonstrated that TDIP was highly effective in preventing DDoS attacks and, at the same time, improving network efficiency and IoT network protection.

ML-DDoS: A Blockchain-Based Multilevel DDoS Mitigation Mechanism for IoT Environments

Distributed denial of service (DDoS) attacks as well as botnet-based attacks are among the most important security vulnerabilities in Internet of Things (IoT) environments. Most of the existing research approaches use centralized defense mechanisms to prevent DDoS attacks in IoT environments. However, it is important to provide a reliable and scalable solution to prevent DDoS attacks. Combining technologies such as distributed blockchain-based mechanisms and smart contracts facilitates the construction of a trusted distributed framework that can defend against DDoS attacks in IoT. In this article, we have proposed a multilevel DDoS mitigation approach (ML-DDoS) to protect IoT devices and other computing resources or machines using the blockchain-based framework. The core concept of the proposed system is to use a device-based verification mechanism using blockchain and exclude malicious devices from IoT environments. The proposed framework was developed using Hyperledger Caliper (a blockchain benchmark tool) and its performance was evaluated using three benchmark applications. Compared to the state of the art, the results show that the proposed framework achieves up to 35% improvement in throughput, up to 40% improvement in latency, and up to 25% better utilization of CPU.

DDos attack detection method based on machine learning

This paper introduces a novel machine learning-based approach for the detection of Distributed Denial of Service (DDoS) attacks. The proposed method employs three different classifiers, namely Support Vector Machine (SVM), Random Forest, and Multilayer Perceptron (MLP), to accurately classify network traffic as normal or malicious. The approach incorporates various features extracted from network traffic, such as packet length, packet inter-arrival time, and destination port number, to train the classifiers. The results demonstrate high accuracy rates, with Random Forest outperforming the other classifiers in terms of detection accuracy. This proposed method offers a promising solution for detecting DDoS attacks in real-time and has the potential to be integrated into existing network security systems. The issue of DDoS attacks is becoming increasingly critical, with the proliferation of connected devices and the growing dependence on the Internet. There is an urgent need for advanced techniques to detect and mitigate such attacks. Machine learning approaches have shown great potential in identifying anomalous behavior and detecting DDoS attacks in real-time. The proposed method is a promising step towards achieving this goal. Furthermore, the proposed approach has practical applications in the context of network security. By integrating this method into existing security systems, it will enhance the system's ability to detect and prevent DDoS attacks. The approach can be implemented in different network environments, making it versatile and applicable to a variety of settings. In conclusion, the proposed machine learning-based approach provides a robust and effective solution for the detection of DDoS attacks. It is capable of accurately classifying network traffic as normal or malicious in real-time, and has the potential to enhance the overall security of networking systems.

Implementasi Anti-DDOS Menggunakan Intrusion Prevention System (IPS) terhadap Serangan DDOS

Distributed Denial of Service (DDoS) is a type of attack that can exhaust server resources. This attack results in a decrease in server quality so that it cannot be accessed by authorized users. Servers that are commonly victimized by this attack belong to companies from various sectors. PT Datacomm Diangraha provides solutions to these problems. As PT Datacomm Diangraha will do to Company X, which is to implement an Intrusion Prevention System (IPS) device as Anti-DDoS on its customers according to the customer's needs. This paper will test IPS devices in preventing DDoS attacks such as TCP Flood, UDP Flood, and ICMP Flood. The test is conducted by connecting the attacker and victim to the IPS device in the local network. The analysis will be done by comparing the network traffic and throughput of the victim when the attack is carried out when protected by IPS, no protection, and when traffic is normal. Experiments were conducted by performing a one-minute attack. The results of the experiments show that the traffic when protected by an IPS is similar to that during normal traffic. In addition, tests were conducted to prevent XSS malware to prove that IPS can prevent other attacks besides DDoS. From the test results, it was found that IPS can prevent DDoS attacks with 100% accuracy. The throughput data obtained when a DDoS attack occurs without IPS protection is 260978.9 - 1080732.32 bps. Throughput data when a DDoS attack occurs with IPS protection of 42.55 - 49.95 bps, which shows similarity in value with throughput during normal traffic which is 43.43 bps.

An Intelligent System for DDoS Attack Prediction Based on Early Warning Signals

Among the different threats causing significant losses in cyberspace, the distributed denial of service (DDoS) attack is one of the most dangerous. The literature shows that the most reasonable manner to reduce the impacts of a DDoS attack is to prevent an attacker from launching it. Prevention is essential because attack sophistication allows them to reach massive traffic volumes, bypassing defenses. Defense mechanisms need time to detect and mitigate attacks. Hence, it is paramount to manage signals of the attack preparation before the attacker effectively launches it. This work presents COOPRED DDoS, a cooperative system for predicting DDoS attacks based on early warning signals extracted from the preparation of DDoS attacks. Its goal lies in increasing the time to prevent DDoS attacks. This work has followed four experiments utilizing two datasets widely employed in the literature. The results show that COOPRED DDoS identifies signals of attacks before the attacker effectively launches them. The system predicts one of the investigated attacks up to 3 minutes and 49 seconds in advance and the other attack up to 3 minutes and 55 seconds. The accuracy of the experiments varies from 99.60% to 99.87%.

Prevention of DDoS attacks using an optimized deep learning approach in blockchain technology

AbstractThe attack named Distributed Denial of Service (DDoS) that takes place in the large blockchain network requires an efficient and robust attack detection and prevention mechanism for authenticated access. Blockchain is a distributed network in which the attacker tries to hack the network by utilizing all the resources with the application of enormous requests. Several methods like Rival Technique, filter modular approach and so on, were developed to detect and prevent the DDoS attack in the blockchain; still, detection accuracy is a challenging task. Hence, this research introduces an efficient technique using optimization‐based deep learning by considering the blockchain network and smart contract for the detection and prevention of DDoS attacks. Based on the user request, the traffic is analyzed, and the verification using the smart contract is made to find the authenticated user. After the verification, the response is provided for the authenticated user, and the suspicious traffic is utilized for the detection of DDoS attacks using the Poaching Raptor Optimization‐based deep neural network (Poaching Raptor‐based DNN), in which the classifier is tuned using the proposed optimization algorithm to reduce the training loss. The proposed algorithm is designed by hybridizing the habitual practice of the raptor by considering the concurring behavior, hunting style along with poaching behavior of the Lobo to enhance the detection accuracy. After the attack detection, the nonattacker is responded, and the attacker is prevented by entering the IP/MAC address in the logfile. The performance of the proposed method is evaluated in terms of recall, precision, FPR, and accuracy and obtained the values of 96.3%, 98.22%, 3.33%, and 95.12%, respectively.

Implementation of IPS (Intrusion Prevention System) Fail2ban on Server for DDoS and Brute Force Attacks

Server security is an important thing that must be considered so that the server can work well and serve users. Attacks on servers can threaten server performance and data security in it. According to the National Cyber and Crypto Agency 2020 report, ports 22 and 80 were the top ports with the most attacks. One of the attacks on port 22 is brute force and an attack on port 80 is Distributed Denial of Service (DDoS). To solve this problem, a study was conducted to implement fail2ban IPS (Intrusion Prevention System) to increase server security. The attacks tested focused on brute force attacks on port 22 and DDoS attacks on port 80 using the TCP protocol. The fail2ban system is equipped with a website interface and notifications via telegram. The test results show that DDoS attacks have more impact on CPU performance with the highest increase in CPU being 92%, while brute force attacks have more impact on server memory performance with the highest increase in memory by 100%. The increase in server performance results in slowed server performance. The system managed to prevent DDoS attacks with an average speed of 0.5 seconds while brute force attacks were 6.1 seconds. The system managed to prevent DDoS attacks with a total of 88 attacks and brute force attacks with a total of 864 attacks.

Detection and Prevention of DDoS Attacks on the IoT

The Internet of Things (IoT) system has been a hot topic in recent years. Its operation is a system that stores data in data storage and is completed by the exchange of network information about things. Therefore, the security of information between network transmissions is very important. In recent years, the most likely cause of information security problems has been a distributed denial of service (DDoS) attack. In this paper, we proposed an autonomous defense system that combines edge computing with a two-dimensional convolutional neural network (CNN) to recognize whether the data server in IoT suffers from DDoS attacks and identify the attack mode. The accuracy of trained two-dimensional CNN is up to 99.5% and 99.8% for packet traffic and packet features training, respectively. A field experiment’s results show that the data server in the proposed system can effectively distinguish the difference between the DDoS attacks and the normal transmission to reduce the impact of DDoS attacks on the IoT data storage while it is under attack.

RTL-DL: A Hybrid Deep Learning Framework for DDoS Attack Detection in a Big Data Environment

A distributed denial of service (DDoS) attack is one of the most common cyber threats to the Internet of Things (IoT). Several deep learning (DL) techniques have been utilized in intrusion detection systems to prevent DDoS attacks. However, their performance is greatly affected by a large class imbalance nature of the training datasets as well as the presence of redundant and irrelevant features in them. This study proposes RTL-DL, a new framework for an effective intrusion detection model based on the random oversampling technique and the Tomek-Links sampling technique (RTL), to minimize the effects of data imbalance in the CICIDS2017 dataset used to evaluate the proposed model. This study achieved 98.3% accuracy, 98.8% precision, 98.3% recall, 97.8% f-score, and 4.6% hamming loss. In comparison to current approaches, the suggested model has demonstrated promising results in identifying network threats in imbalanced data sets.

Detection of DDoS attacks in D2D communications using machine learning approach

In device-to-device (D2D) communications, distributed Denial-of-Service (DDoS) attacks can be quite detrimental because it can result in network structure destruction. Towards this end, the research objective of this paper is to identify and prevent DDoS and Denial-of-Service (DoS) attacks (i.e., SYN, Slowloris) in a D2D communication environment. Specifically, by replicating a real-world scenario, we emulate SLowloris attacks in a D2D communication network and generate a D2D Network-specific Slowloris dataset. This dataset along with the CICDDoS2019 dataset was then used to train our proposed Machine learning (ML) model that aids in the detection and prevention of DDoS attacks (Slowloris and SYN) in the considered D2D framework. The whole process of how to construct an emulation network for D2D communication and test it against a variety of attacks and implementations is also demonstrated in the paper. To quantify the detection accuracy in the context of DDoS and DoS attacks, we use various ML algorithms such as Random Forest, Light GBM, XGBoost, and Ada Boost and study their performance with the aid of extensive emulation. The results collected revealed that both Slowloris and CICDDoS2019 datasets achieve greater accuracy with Random Forest. Consequently, the results compel us to develop a technique for combining the identification of DDoS and DoS attacks in binary classification Random Forests with the binary decision. The proposed technique has been evaluated and compared with other related approaches in the open literature demonstrating significant performance in terms of identification and prevention time, processing and memory resources required, and device battery consumption, without affecting the accuracy of the attack identification. Hence, we advocate that our proposed technique can be extremely beneficial in preventing DDoS and DoS attacks in a D2D communication environment, where its lifetime and capabilities are mainly associated with the resources of the D2D device (i.e., CPU, Memory, and battery life).

Literature Review of Distributed Denial of Service (DDoS) Attacks, its Detection Techniques and Prevention Mechanisms

Abstract: Denial of service (DoS) or distributed denial of service (DDoS) are usually deliberate attempts to eat up the victim's bandwidth or obstruct the use of services by authorized users. The traditional internet architecture is susceptible to DDoS attacks, giving an attacker the chance to set up attack networks or "Botnets" that provide them access to a huge number of infected computers. An attacker launches a massive, well-coordinated attack against one or more targets after setting up an attack network or botnet. Numerous DDoS attack Detection, Prevention, and Trace-back procedures have been proposed as a result of the ongoing development of new assaults and the expanding variety of vulnerable hosts on the internet. When a system is targeted by Distributed Denial of Service (DDoS) assaults for commercial monitoring typically involves many packets. They clog up the network, overburden the bandwidth, and overwhelm thousands of infected hosts. Due to DDoS, there is no effective leverage on the crucial support of infrastructure. This entirely mitigate the legitimate end users of the system resources. In this paper, we tend to examine several DDoS attack kinds, their methodologies, and related countermeasures. This paper also elaborates treatment of numerous DDoS assault defense strategies, including as detection, defense, and mitigation

DDoS Attack Prevention for Internet of Thing Devices Using Ethereum Blockchain Technology.

The Internet of Things (IoT) has widely expanded due to its advantages in enhancing the business, industrial, and social ecosystems. Nevertheless, IoT infrastructure is susceptible to several cyber-attacks due to the endpoint devices’ restrictions in computation, storage, and communication capacity. As such, distributed denial-of-service (DDoS) attacks pose a serious threat to the security of the IoT. Attackers can easily utilize IoT devices as part of botnets to launch DDoS attacks by taking advantage of their flaws. This paper proposes an Ethereum blockchain model to detect and prevent DDoS attacks against IoT systems. Additionally, the proposed system can be used to resolve the single points of failure (dependencies on third parties) and privacy and security in IoT systems. First, we propose implementing a decentralized platform in place of current centralized system solutions to prevent DDoS attacks on IoT devices at the application layer by authenticating and verifying these devices. Second, we suggest tracing and recording the IP address of malicious devices inside the blockchain to prevent them from connecting and communicating with the IoT networks. The system performance has been evaluated by performing 100 experiments to evaluate the time taken by the authentication process. The proposed system highlights two messages with a time of 0.012 ms: the first is the request transmitted from the IoT follower device to join the blockchain, and the second is the blockchain response. The experimental evaluation demonstrated the superiority of our system because there are fewer I/O operations in the proposed system than in other related works, and thus it runs substantially faster.

Efficient Key Management Mechanism with Trusted Gateways for Wireless Mesh Networks

Securing wireless mesh networks (WMNs) is a crucial issue due to its intrinsic characteristics. Several endangered features might emerge due to the exposure of the networks to a diversity of medium access control (MAC) layers such as distributed denial of service (DDoS) attacks, false reply attacks, and other identity attacks. Against these attacks, the determination of existing techniques is insufficient to ensure the complete security solutions to protect the backbone network at multiple levels. As a result, effective, scalable, and integrated security solutions for WMNs are required. In WMNs, protecting legitimate gateway nodes and internal mesh routers against malicious attacks at the MAC layer remains a difficult problem. Our proposed trust-based security mechanism includes distributed authentication and deauthentication algorithms that validates backbone mesh routers as well as gateway nodes. Particularly, this proposed model targets DDoS attacks in the network. The proposed DDoS attack prevention mechanism (DAPM) uses distributed authentication and deauthentication algorithms to build trusted group heads for managing secure data communication in the network. Our research and practical results show that the proposed mechanism decreases the severity of malicious nodes and strengthens the security compared to existing centralized schemes such as digital signature authentication (DSA-Mesh, MENSA, Mobisec, and AHKM). The experimental solutions show the significance of the proposed work with 10% to 12% of better performance than the existing techniques.

Distributed Denial of Service Attack Detection and Prevention Model for IoT based Computing Environment using Ensemble Machine Learning Approach

Defending against Distributed Denial of Service (DDoS) in the Internet of Things (IoT) computing environment is a challenging task. DDoS attacks are type of collective attack in which attackers work together to compromise internet security and services. The resource-constrained devices used in IoT deployments have made it even easier for an attacker to break, because of the vast number of vulnerable IoT devices with significant compute power. This paper proposed an ensemble machine learning (ML) model using the bagging technique to detect and prevent DDoS attacks in the IoT computing environment. We carried out an Machine Learning experiment and evaluated our proposed model with the most recent DDoS attacks (CICDoS2019) dataset. We use seven validation metrics (classification accuracy, precision rate, recall rate, f1-score, Matthews Correlation Coefficient, false negative rate and false positive rate) to evaluate the performance of the proposed model. The results obtained in our experiment shows an improved performance with an overall maximum classification accuracy of 99.75%, precision rate of 99.99%, recall rate of 99.76%, f1-score of 99.87%, Matthews Correlation Coefficient of 0.000000214, false negative rate of 0.24% and 4.42% false positive rate.

Intelligent software defined networking: Long short term memory‐graded rated unit enabled block‐attack model to tackle distributed denial of service attacks

AbstractSoftware defined networking (SDN) is the next‐generation network. SDN enhances the programming flexibility, speed and automation to improve the network's performance. In recent times SDN has played a vital role in networking technology. It communicates with underlying hardware infrastructure and directs traffic on a network. The most complicated issue in SDN is the control plane's single point of failure (SPF). The main reason for raising the SPF problem in SDN by distributed denial of service (DDoS) attacks. The network collapses during failures in SDN, and the control plane is considered a management controller. Therefore, a novel intrusion detection and prevention system (IDPS) is proposed in the proposed approach to address SDN's problems mentioned above. In the proposed approach, a long short term memory (LSTM) and graded rated unit (GRU) deep learning model is proposed as the “Block‐Attack” model. The main objective of using LSTM and GRU in the proposed approach is to enhance the rate of accuracy in detecting DDoS attacks in an SDN environment. The CICDDoS2019 dataset is used for experimental result analysis in the proposed approach. Initially, the dataset is fed into the preprocessing stage. Using the K‐medoid technique, raw datasets are preprocessed to reduce the model's sensitivity to low density. In the proposed approach, the support vector machine based machine learning (SVM‐ML) technique is utilized to prevent DDoS attacks in a Mininet‐based emulation. Then, LSTM and GRU deep learning (DL) techniques are used to define the block‐attack model to enhance the detection performance. The experimental results of the proposed approach “Block‐Attack” model attain 98.5% of accuracy to detect and prevent the DDoS attacks and 95.5% of accuracy for SVM based method.

A long short-term memory (LSTM)-based distributed denial of service (DDoS) detection and defense system design in public cloud network environment

The fact that cloud systems are under the increasing risks of cyber attacks has made the phenomenon of information security first a need and then a necessity for these systems. Distributed Denial of Service (DDoS) attacks can exploit, disrupt, change, prevent or damage cloud services. Accurate and timely detection and prevention of these attacks are very important in terms of ensuring information security. During the COVID-19 period, the increase in the use of information technologies and especially the internet has made cyber attacks a real concern. Deep learning (DL) has become widely used for the purpose of detecting and preventing cyber attacks to provide information security. In this study, a Long Short-Term Memory (LSTM) based system (LSTM-CLOUD) which was designed for the detection and prevention of DDoS attacks in a public cloud network environment was proposed. The design of the system is based on a signature-based attack detection approach. The LSTM-CLOUD has two modules defined in the study: detection and defense. The function of the first module of the system was determined as detecting the occurrence of DDoS attacks with the LSTM DL model developed in this study with an accuracy rate of 99.83% on the CICDDoS2019 data set. The function of the second module was determined as activating the defense mechanism to protect the cloud systems when attacks are detected. The comparison results showed that our LSTM model had a performance as good as those in the previous studies conducted with different DL algorithms on the same and different datasets. The results obtained show the effectiveness of the LSTM model developed in this study in detecting the occurrence of attacks.

An Effective Probabilistic Technique for DDoS Detection in OpenFlow Controller

Distributed denial of service (DDoS) attacks have always been a nightmare for network infrastructure for the last two decades. Existing network infrastructure is lacking in identifying and mitigating the attack due to its inflexible nature. Currently, software-defined networking (SDN) is more popular due to its ability to monitor and dynamically configure network devices based on the global view of the network. In SDN, the control layer is accountable for forming all decisions in the network and data plane for just forwarding the message packets. The unique property of SDN has brought a lot of excitement to network security researchers for preventing DDoS attacks. In this article, for the identification of DDoS attacks in the OpenFlow controller, a probabilistic technique with a central limit theorem has been utilized. This method primarily detects resource depletion attacks, for which the DARPA dataset is used to train the probabilistic model. In different attack scenarios, the probabilistic approach outperforms the entropy-based method in terms of false negative rate (FNR). The emulation results demonstrate the efficacy of the approach, by reducing the FNR by 98% compared to 78% in the existing entropy mechanism, at 50% attack rate.

Early DDoS Detection and Prevention with Traced-Back Blocking in SDN Environment

The flow of information is a valuable asset for every company and its consumers, and Distributed Denial-of-Service (DDoS) assaults pose a substantial danger to this flow. If we do not secure security, hackers may steal information flowing across a network, posing a danger to a business and society. As a result, the most effective ways are necessary to deal with the dangers. A DDoS attack is a well-known network infrastructure assault that prevents servers from servicing genuine customers. It is necessary to identify and block a DDoS assault before it reaches the server in order to avoid being refused services. This prompted us to develop a unique way for detecting and preventing DDoS attacks at the router level in a Software-Defined Network (SDN) environment. This study demonstrates how the method efficiently integrates the first and second signatures in SDN infrastructure domains to identify and prevent DDoS attacks. It also proposes an Early DDoS Detection and Prevention (EDDDeP)-based approach for detecting and blocking malicious traffic in an SDN context. This article covers the EDDDeP, which assists in identifying and preventing DDoS in SDN to prevent malicious traffic from reaching its intended target. As a consequence, the DDoS assault is ultimately contained inside the environment, eliminating superfluous traffic in the DDoS network architecture. This method offers a unique technique to detect a DDoS assault and notify nearby neighbours in order to avert server damage.

Stochastic Intrusion Detection Game-Based Arrangement Using Controlled Markov Chain for Prevention of DoS and DDoS Attacks in Cloud

DoS (denial of service) assault is the most prevalent assault these days. It imposes a major risk to cybersecurity. At the point when this assault is propelled by numerous conveyed machines on a solitary server machine, it is called as a DDoS (distributed denial of service) assault. Additionally, DoS bypass on DHCP (dynamic host configuration protocol) server assault is a rising and famous assault in a system. The authors have proposed a stochastic intrusion detection game-based arrangement utilizing controlled Markov chain that figures the transition probabilities starting with one state then onto the next in a state transition diagram. At first, the authors have conjectured these assaults, and after that, they proposed a methodology that uses the idea of master and slave IPS (intrusion prevention system). This approach works well when mapped to these estimated assaults and accordingly helps in the recognition and counteractive action of these assaults in a cloud domain.

A systematic review on distributed denial of service attack defense mechanisms in programmable networks

SummaryDesign flaws and vulnerabilities inherent to network protocols, devices, and services make Distributed Denial of Service (DDoS) a persisting threat in the cyberspace, despite decades of research efforts in the area. The historical vertical integration of traditional IP networks limited the solution space, forcing researchers to tweak network protocols while maintaining global compatibility and proper service to legitimate flows. The advent of Software‐Defined Networking (SDN) and advances in Programmable Data Planes (PDP) changed the state of affairs and brought novel possibilities to deal with such attacks. In summary, the ability of bringing together network intelligence to a control plane, and offloading flow processing tasks to the forwarding plane, opened up interesting opportunities for network security researchers unlike ever. In this article, we dive into recent research that relies on SDN and PDP to detect, mitigate, and prevent DDoS attacks. Our literature review takes into account the SDN layered view as defined in RFC7426 and focuses on the data, control, and application planes. We follow a systematic methodology to capture related articles and organize them into a taxonomy of DDoS defense mechanisms focusing on three facets: activity level, deployment location, and cooperation degree. From the analysis of existing work, we also highlight key research gaps that may foster future research in the field.