Distributed Denial Of Service Prevention Research Articles

Intrusion Detection System

Our project introduces an Integrated Intrusion Detection System (IDS) designed to bolster network and system security through a multi-faceted approach. This comprehensive IDS seamlessly combined Machine Learning, Anomaly Detection, File Scanning, and DDoS Prevention mechanisms to offer robust defense against diverse cyber threats. At the heart of the system lies the Machine Learning component, which continuously adapts to the evolving threat landscape. By analyzing network traffic and system behavior, it identifies both known and emerging threats in real-time, reducing reliance on traditional signature-based detection methods and allowing organizations to proactively stay ahead of cybercriminals. Anomaly Detection constantly monitors network and system activity, comparing it against established baselines. Any deviations from these baselines trigger alerts, facilitating swift responses to unusual activities that may indicate security breaches. File Scanning is another vital component ensuring data integrity and preventing malware infiltration or data exfiltration. It conducts thorough file analysis, checking for suspicious code, behavior, or unauthorized access, and offers continuous monitoring to detect anomalies in real-time. Additionally, our IDS includes Distributed Denial of Service (DDoS) Prevention mechanisms. By detecting and mitigating DDoS attacks, the system ensures the continuous availability of network resources and services even under intense traffic loads. This integrated approach to intrusion detection and prevention leverages Machine Learning, Honeypots, Anomaly Detection, File Scanning, and DDoS Prevention, empowering organizations to safeguard critical assets, maintain data integrity, and ensure network security in today's dynamic and perilous digital landscape. Our IDS solution serves as a valuable addition to the cybersecurity toolkit for organizations seeking comprehensive security against a wide range of threats.

Securing Heterogeneous IoT With Intelligent DDoS Attack Behavior Learning

The rapid increase of diverse Internet of Things (IoT) services and devices has raised numerous challenges in terms of connectivity, interoperability, and security. The heterogeneity of the networks, devices, and services introduces serious vulnerabilities to security, especially distributed denial-of-service (DDoS) attacks, which exploit massive IoT devices to exhaust both network and victim resources. As such, this article proposes FOGshield, which is a localized DDoS prevention framework leveraging the federated computing power of the fog computing-based access networks to deploy multiple smart endpoint defenders at the border of relevant attack-source/destination networks. Cooperation among the smart endpoint defenders is supervised by a central orchestrator. The central orchestrator localizes each smart endpoint defender by feeding appropriate training parameters into its self-organizing map component, based on the attacking behavior. Performance of the FOGshield framework is verified using three typical IoT traffic scenarios. Numerical results reveal that the FOGshield outperforms existing solutions.

Machine Learning Enabled Techniques for Protecting Wireless Sensor Networks by Estimating Attack Prevalence and Device Deployment Strategy for 5G Networks

A number of disadvantages of traditional networks may be attributed to the close relationship that exists between the control plane and the data plane inside proprietary hardware designs, as described above. The problem of security is one of the most difficult to deal with. There are a plethora of network hazards and attacks that might be encountered these days. DDoS attacks are one of the most popular and disruptive attacks on the internet today, and they affect a wide range of organisations. Despite a large number of traditional mitigation solutions now available, the frequency, volume, and intensity of distributed denial-of-service (DDoS) attacks continue to rise. According to the findings of this paper, a new network paradigm is necessary to satisfy the requirements of today’s complex security concerns. It was necessary to develop a software-defined network (SDN) in order to meet the real-time needs of the massive network that was expanding at an exponential rate. Many advantages of SDN exist, including simplicity of administration, scalability, and agility, but one of the most critical is security, which is one of the most important considerations when implementing SDN. SDS may be seen as a paradigm in which the implementation of new security regulations in the computer environment is performed via the use of protected software, which is described further below. The goal is to provide a flexible and extensible architecture for DDoS detection and prevention that is both flexible and extendable; the suggested clustering approach, which is based on the Open Day Light (ODL) Controller, is employed to carry out the experimental findings. In this section, we emphasise DDoS penetration techniques from a range of tools, and we evaluate the vulnerability against various tactics. It is necessary to use a Mininet emulation tool to construct a detection and prevention system against distributed denial of service (DDoS) attacks in order to achieve success. There is a range of other simulation tools that are utilised in conjunction with this research in order to bring it to a conclusion. Integration of industry standards such as SNORT and Flow has been accomplished in a variety of situations and parameter settings. During the creation of a framework capable of detecting and mitigating DDoS attacks at an early stage in both the control and application levels, the implementation of this framework has been shown to be crucial in the development of a framework.

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.

Detection and Characterization of DDoS Attacks Using Time-Based Features

In today’s evolving cybersecurity landscape, distributed denial-of-service (DDoS) attacks have become one of the most prolific and costly threats. Their capability to incapacitate network services while causing millions of dollars in damages has made effective DDoS detection and prevention imperative for businesses and government entities alike. Prior research has found shallow and deep learning classifiers to be invaluable in detecting DDoS attacks; however, there is an absence of research concerning time-based features and classification among many DDoS attack types. In this article, we propose and study the efficacy of 25 time-based features to detect and classify 12 types of DDoS attacks using binary and multiclass classification. Furthermore, we ran experiments to compare the performance of eight traditional machine learning classifiers and one deep learning classifier using two different scenarios. Our findings show that the majority of models provided ~99% accuracy on both the control and time-based experiments in detecting DDoS attacks while yielding ~70% accuracy in classifying specific DDoS attack types. Training on the proposed time-based feature subset was found to be effective at reducing training time without compromising test accuracy; thus, the smaller time-based feature subset alone is beneficial for near-real time applications that incorporate continuous learning.

DDoS prevention architecture using anomaly detection in fog-empowered networks

The world of computation has shown wide variety of wonders in the past decade with Internet of Things (IoT) being one of the most promising technology. Emergence of IoT brings a lot of good to the technology pool with its capability to provide intelligent services to the users. With ease to use, IoT is backed by a strong Cloud based infrastructure which allows the sensory IoT devices to perform specific functions. Important features of cloud are its reliability and security where the latter must be dealt with proper care. Cloud centric systems are susceptible to Denial of Service (DoS) attacks wherein the cloud server is subjected to an overwhelming number of incoming requests by a malicious device. If the same attack is carried out by a network of devices such as IoT devices then it becomes a Distributed DoS (DDoS) attack. A DDoS attack may render the server useless for a long period of time causing the services to crash due to extensive load. This paper proposes a lightweight, efficient and robust method for DDoS attack by detecting the compromised node connected to the Fog node or edge devices before it reaches the cloud by taking advantage of the Fog layer and prevent it from harming any information recorded or from increasing the unnecessary traffic in a network. The chosen technology stack consists of languages and frameworks which allow proposed approach to works in real time complexity for faster execution and is flexible enough to work on low level systems such as the Fog nodes. The proposed approach uses mathematical models for forecasting data points and therefore does not rely on a computationally heavy approach such as neural networks for predicting the expected values. This approach can be easily modelled into the firmware of the system and can help make cloud services more reliable by cutting off rogue nodes that try to attack the cloud at any given point of time.

DDoS detection and prevention based on artificial intelligence techniques

Distributed Denial of Service (DDoS) attacks have been the major threats for the Internet and can bring great loss to companies and governments. With the development of emerging technologies, such as cloud computing, Internet of Things (IoT), artificial intelligence techniques, attackers can launch a huge volume of DDoS attacks with a lower cost, and it is much harder to detect and prevent DDoS attacks, because DDoS traffic is similar to normal traffic. Some artificial intelligence techniques like machine learning algorithms have been used to classify DDoS attack traffic and detect DDoS attacks, such as Naive Bayes and Random forest tree. In the paper, we survey on the latest progress on the DDoS attack detection using artificial intelligence techniques and give recommendations on artificial intelligence techniques to be used in DDoS attack detection and prevention.

DDoS prevention using third party auditor in cloud computing

Distributed denial of service (DDoS) attack is one of the prominent risk factors for the development of cloud service. It is a very hard task for novice cloud users to identify the real source of DDoS attack because the attacker spoofs the Internet Protocol and Media Access Control addresses. To address this problem, we propose a third party auditor-based packet traceback approach. The method uses Weibull distribution for analyzing the source of the DDoS attack. The approach provides an efficient and fruitful solution because of its strong identification factor. The identification factor depends on the weaknesses left by the intruder. We analyze the traffic pattern to generate attack alert for different cloud users. The advantage of this approach is that it reduces the overhead on the cloud user. With the help of Weibull distribution, we can easily obtain the availability, reliability and median life of DDoS defense in the cloud environment. To demonstrate our approach, we implement an application based on Hadoop and MapReduce framework. We tested this application based on various parameters. Our method has shown the tremendous improvement over the other state of the art methods. The experimental results are included to show the effectiveness of the proposed method for DDoS attack prevention and mitigation.

Umbrella: Enabling ISPs to Offer Readily Deployable and Privacy-Preserving DDoS Prevention Services

Defending against distributed denial of service (DDoS) attacks on the Internet is a fundamental problem. However, recent industrial interviews with over 100 security experts from more than ten industry segments indicate that DDoS problems have not been fully addressed. The reasons are twofold. On one hand, many academic proposals that are provably secure witness little real-world deployment. On the other hand, the operation model for existing DDoS-prevention service providers (e.g., Cloudflare, Akamai) is privacy invasive for large organizations (e.g., government). In this paper, we present Umbrella, a new DDoS defense mechanism enabling Internet service providers to offer readily deployable and privacy-preserving DDoS prevention services to their customers. At its core, Umbrella develops a multi-layered defense architecture to defend against a wide spectrum of DDoS attacks. In particular, the flood throttling layer stops amplification-based DDoS attacks; the congestion resolving layer, aiming to prevent sophisticated attacks that cannot be easily filtered, enforces congestion accountability to ensure that legitimate flows are guaranteed to receive their fair shares regardless of attackers’ strategies; and finally the user-specific layer allows DDoS victims to enforce self-desired traffic control policies that best satisfy their business requirements. Based on Linux implementation, we demonstrate that Umbrella is capable to deal with large-scale attacks involving millions of attack flows, meanwhile imposing negligible packet processing overhead. Further, our physical test bed experiments and large-scale simulations prove that Umbrella is effective to mitigate various DDoS attacks.

Practical Proactive DDoS-Attack Mitigation via Endpoint-Driven In-Network Traffic Control

Volumetric attacks, which overwhelm the bandwidth of a destination, are among the most common distributed denial-of-service (DDoS) attacks today. Despite considerable effort made by both research and industry, our recent interviews with over 100 potential DDoS victims in over 10 industry segments indicate that today’s DDoS prevention is far from perfect. On one hand, few academical proposals have ever been deployed in the Internet; on the other hand, solutions offered by existing DDoS prevention vendors are not silver bullet to defend against the entire attack spectrum. Guided by such large-scale study of today’s DDoS defense, in this paper, we present MiddlePolice, the first readily deployable and proactive DDoS prevention mechanism. We carefully architect MiddlePolice such that it requires no changes from both the Internet core and the network stack of clients, yielding instant deployability in the current Internet architecture. Further, relying on our novel capability feedback mechanism, MiddlePolice is able to enforce destination-driven traffic control so that it guarantees to deliver victim-desired traffic regardless of the attacker strategies. We implement a prototype of MiddlePolice and demonstrate its feasibility via extensive evaluations in the Internet, hardware testbed, and large-scale simulations.

HTTP flood attack detection in application layer using machine learning metrics and bio inspired bat algorithm

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.

A Novel Model based on Group Controlled Observation for DDOS Attack Detection and Prevention in VANET

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.

Qualified Vector Match and Merge Algorithm (QVMMA) for DDoS Prevention and Mitigation

Distributed Denial of Service (DDoS) attacks continue to instigate intense wars against popular ecommerce and content websites. One in five companies worldwide become a DDoS attack victim. Such attacks remain active causing prolonged damage from a few hours to several weeks. Deccan Chronicle34,35, dated April 29, 2015, reported above statement as conclusion of Kaspersky Lab's and B2B's international survey with categorizing two types of DDoS attacks: “a powerful short term attack or persistent long running campaign”. Both the above types of popular DDoS attacks can be detected, prevented and mitigated using the proposed novel Qualified Vector Match and Merge Algorithm (QVMMA) in real time. 14 feature components are used to generate an attack signature in real time and stored in dynamically updated DDoS Captured Attack Pattern (DCAP)30database. It's effective in detecting new and old attacks. Persistent DDoS attacks cause financial damage or reputation loss by loss of the company's valuable clients. The server's availability is heavily compromised. Popular websites Github and BBC UK faced DDoS attacks in 2015. Long term DDoS attack directed on Github continued for over 118 hours34,35. Short term DDoS attack experienced by BBC36 website caused its patchy response. The main crux of the problem is the absence of a way to differentiate between attack records and legitimate records while the attack is occurring in real time. Several methods1-31,37-42 are listed in the paper. Post mortem solutions are not applicable in real time. Available real time solutions are slow. QVMMA is an ideal faster real time solution to prevent DDoS attacks using Statistical Feature Vector Generation. Matlab is used for DDoS real time simulation where the topologies (bus, star, abilene network) are created using OMNET++33. QVMMA generates and uses Statistical Feature Vector for Attack Signature Generation, Matching and Identification only for qualifier satisfied records. The web server's log files used as input to QVMMA are according to W3C log format standard34. Experimentation is completed with exhaustive 336 cases. Four networks are tested with 5, 8, 10, 13 nodes. Performance evaluation of QVMMA concludes EER is 11.8% when threshold is 1.6. Abilene network achieves best result. As the number of attackers, nodes and intermediate routers increase, detection time increases. If threshold is increased, the accuracy reduces. If the number of nodes increases, accuracy increases. Thus it is concluded that QVMMA can be used for effective layer 3 DDoS Prevention and Mitigation in real time based on results generated in Matlab simulation.

A Review of Techniques to Detect and Prevent Distributed Denial of Service (DDoS) Attack in Cloud Computing Environment

Cloud computing has become one of the most demanding services over the internet. It has gained remarkable fame for past few years.But it is under the severe threats of internet security. One of the severe threats is Distributed Denial of Service (DDoS). DDoS occurs when a huge amount of packets are sent to a server from various computers. Botnet is one of the major causes that launch DDoS attack. Botnet is actually a network of bots or zombie computers that are under the control of attacker. Several techniques are available in the literature that provides solution to avoid DDoS attack.A survey on DDoS detection and prevention techniques and their comparative analysishave been done in this paper.

A Novel Protective Framework for Defeating HTTP-Based Denial of Service and Distributed Denial of Service Attacks.

The growth of web technology has brought convenience to our life, since it has become the most important communication channel. However, now this merit is threatened by complicated network-based attacks, such as denial of service (DoS) and distributed denial of service (DDoS) attacks. Despite many researchers' efforts, no optimal solution that addresses all sorts of HTTP DoS/DDoS attacks is on offer. Therefore, this research aims to fix this gap by designing an alternative solution called a flexible, collaborative, multilayer, DDoS prevention framework (FCMDPF). The innovative design of the FCMDPF framework handles all aspects of HTTP-based DoS/DDoS attacks through the following three subsequent framework's schemes (layers). Firstly, an outer blocking (OB) scheme blocks attacking IP source if it is listed on the black list table. Secondly, the service traceback oriented architecture (STBOA) scheme is to validate whether the incoming request is launched by a human or by an automated tool. Then, it traces back the true attacking IP source. Thirdly, the flexible advanced entropy based (FAEB) scheme is to eliminate high rate DDoS (HR-DDoS) and flash crowd (FC) attacks. Compared to the previous researches, our framework's design provides an efficient protection for web applications against all sorts of DoS/DDoS attacks.

Apple’s Lion vs Microsoft’s Windows 7: Comparing Built-In Protection against ICMP Flood Attacks

With the increase in the number of computers connected to Internet, the number of Distributed Denial of Service (DDoS) attacks has also been increasing. A DDoS attack consumes the computing resources of a computer or a server, by degrading its computing performance or by preventing legitimate users from accessing its services. Recently, Operating Systems (OS) are increasingly deploying embedded DDoS prevention schemes to prevent computing exhaustion caused by such attacks. In this paper, we compare the effectiveness of two popular operating systems, namely the Apple’s Lion and Microsoft’s Windows 7, against DDoS attacks. We compare the computing performance of these operating systems under two ICMP based DDoS attacks. Since the role of the OS is to manage the computer or servers resources as efficiently as possible, in this paper we investigate which OS manages its computing resources more efficiently. In this paper, we evaluate and compare the built-in security of these two operating systems by using an iMac computer which is capable of running both Windows 7 and Lion. The DDoS attacks that are simulated for this paper are the ICMP Ping and Land Attack. For this experiment, we measure the exhaustion of the processors and the number of Echo Request and Echo Reply messages that were generated under varying attack loads for both the Ping and Land Attack. From our experiments, we found that both operating systems were able to survive the attacks however they reacted a bit differently under attack. The Operating System Lion was handling both the Ping and Land attack in the exactly the same way, whereas Windows 7 handled the two attacks a bit differently, resulting in different processor consumptions by two different operating systems.

DDoS Detection and Prevention Based on Joint Entropy and Conditional Entropy

Distributed Denial of Service (DDoS) imposes a very serious threat to the stability of the Internet. Compared with many detection approaches, detecting DDoS attacks based on entropy has advantages such as simplicity, high sensitivity and low false positive rate. But the method with single attribute entropy has high false positive rate when detecting attribute forged attacks. This paper presents a detecting method based on joint entropy and a filtering way based on conditional entropy. The efficiency of this scheme is validated with simulation on the research lab network.