scholarly journals New Features of User’s Behavior to Distributed Denial of Service Attacks Detection in Application Layer

2018 ◽  
Vol 14 (12) ◽  
pp. 164
Author(s):  
Silvia Bravo ◽  
David Mauricio
Keyword(s):  
Detection Rate ◽  
Detection Method ◽  
Denial Of Service ◽  
Classification Algorithm ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Application Layer ◽  
Data Set ◽  
The Web

Distributed Denial of Service (DDoS) attacks are a threat to the security of red. In recent years, these attacks have been directed especially towards the application layer. This phenomenon is mainly due to the large number of existing tools for the generation of this type of attack. The highest detection rate achieved by a method in the application capacity is 98.5%. Therefore, the problem of detecting DDoS attacks persists. In this work an alternative of detection based on the dynamism of the web user is proposed. To do this, evaluate the user's characteristics, mouse functions and right click. For the evaluation, a data set of 11055 requests was used, from which the characteristics were extracted and entered into a classification algorithm. To that end, it can be applied once in Java for the classification of real users and DDoS attacks. The results showed that the evaluated characteristics achieved an efficiency of 100%. Therefore, it is concluded that these characteristics show the dynamism of the user and can be used in a detection method of DDoS attacks.

HULK and DDoS Attacks in Web Applications with Detection Mechanism

2018 ◽  
Vol 6 (6) ◽  
pp. 192
Author(s):  
Amit Sharma
Keyword(s):  
Web Applications ◽  
Denial Of Service ◽  
Single Server ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Application Layer ◽  
Detection Mechanism ◽  
Plan Execution ◽  
Social Affair ◽  
Server Application

Distributed Denial of Service attacks are significant dangers these days over web applications and web administrations. These assaults pushing ahead towards application layer to procure furthermore, squander most extreme CPU cycles. By asking for assets from web benefits in gigantic sum utilizing quick fire of solicitations, assailant robotized programs use all the capacity of handling of single server application or circulated environment application. The periods of the plan execution is client conduct checking and identification. In to beginning with stage by social affair the data of client conduct and computing individual user’s trust score will happen and Entropy of a similar client will be ascertained. HTTP Unbearable Load King (HULK) attacks are also evaluated. In light of first stage, in recognition stage, variety in entropy will be watched and malevolent clients will be recognized. Rate limiter is additionally acquainted with stop or downsize serving the noxious clients. This paper introduces the FAÇADE layer for discovery also, hindering the unapproved client from assaulting the framework.

scholarly journals A Classification Detection Algorithm Based on Joint Entropy Vector against Application-Layer DDoS Attack

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yuntao Zhao ◽  
Wenbo Zhang ◽  
Yongxin Feng ◽  
Bo Yu
Keyword(s):  
Denial Of Service ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Training Data ◽  
Joint Entropy ◽  
Application Layer ◽  
Accurate Identification ◽  
Data Set ◽  
Ddos Attack ◽  
The Web

The application-layer distributed denial of service (AL-DDoS) attack makes a great threat against cyberspace security. The attack detection is an important part of the security protection, which provides effective support for defense system through the rapid and accurate identification of attacks. According to the attacker’s different URL of the Web service, the AL-DDoS attack is divided into three categories, including a random URL attack and a fixed and a traverse one. In order to realize identification of attacks, a mapping matrix of the joint entropy vector is constructed. By defining and computing the value of EUPI and jEIPU, a visual coordinate discrimination diagram of entropy vector is proposed, which also realizes data dimension reduction from N to two. In terms of boundary discrimination and the region where the entropy vectors fall in, the class of AL-DDoS attack can be distinguished. Through the study of training data set and classification, the results show that the novel algorithm can effectively distinguish the web server DDoS attack from normal burst traffic.

DeepDetect: Detection of Distributed Denial of Service Attacks Using Deep Learning

2019 ◽  
Vol 63 (7) ◽  
pp. 983-994 ◽  
Author(s):  
Muhammad Asad ◽  
Muhammad Asim ◽  
Talha Javed ◽  
Mirza O Beg ◽  
Hasan Mujtaba ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Denial Of Service ◽  
Smart Devices ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Application Layer ◽  
Tangible Computing ◽  
Network Bandwidth ◽  
Feed Forward Back Propagation

Abstract At the advent of advanced wireless technology and contemporary computing paradigms, Distributed Denial of Service (DDoS) attacks on Web-based services have not only increased exponentially in number, but also in the degree of sophistication; hence the need for detecting these attacks within the ocean of communication packets is extremely important. DDoS attacks were initially projected toward the network and transport layers. Over the years, attackers have shifted their offensive strategies toward the application layer. The application layer attacks are potentially more detrimental and stealthier because of the attack traffic and the benign traffic flows being indistinguishable. The distributed nature of these attacks is difficult to combat as they may affect tangible computing resources apart from network bandwidth consumption. In addition, smart devices connected to the Internet can be infected and used as botnets to launch DDoS attacks. In this paper, we propose a novel deep neural network-based detection mechanism that uses feed-forward back-propagation for accurately discovering multiple application layer DDoS attacks. The proposed neural network architecture can identify and use the most relevant high level features of packet flows with an accuracy of 98% on the state-of-the-art dataset containing various forms of DDoS attacks.

scholarly journals SVM Implementation for Ddos Attacks in Software Defined Networks

2020 ◽  
Vol 10 (1) ◽  
pp. 205-212
Keyword(s):  
Detection Rate ◽  
Denial Of Service ◽  
Support Vector ◽  
Software Defined Network ◽  
Software Defined Networks ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Network Resources ◽  
Elapsed Time ◽  
Classification Technique

Software Defined Network (SDN) is making software interaction with the network. SDN has made the network flexible and dynamic and also enabled the abstraction feature of applications and services. As the network is independent of any of the devices like in traditional networks there exist routers, hubs, and switches that is why it is preferable these days. Being more preferably used it has become more vulnerable in terms of security. The more common attacks that corrupt the network and hinders the efficiency are distributed denial-of-service (DDOS) attacks. DDOS is an attack that in general leads to exhaust of the network resources in turn stopping the controller. Detection of DDOS attacks requires a classification technique that provides accurate and efficient decision making. As per the analysis Support Vector Machine (SVM), the classifier technique detects more accurately and precisely the attacks. This paper produces a better approach to detecting attacks using SVM classifiers in terms of detection rate and elapsed time of the attack and it also predicts the various types of distributed denial of service attacks that have corrupted the network.

DDoS Attacks and Their Types

2016 ◽  
pp. 197-205 ◽  
Author(s):  
Dileep Kumar
Keyword(s):  
Large Scale ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Network Resource ◽  
Financial Trading ◽  
Ddos Attack ◽  
Site Access ◽  
Gain Access

Billions of people rely on internet to discover and share ideas with the world. However, the websites are vulnerable to deliver the attacks, preventing people to access them. The recent study of global surveys showed that DDoS Attacks evolved in strategy and tactics. A Distributed Denial of Service (DDoS) attack is a new emerging bigger threat that target organization's business critical services such as e-commerce transactions, financial trading, email or web site access. A DDoS attack is a large-scale, coordinated attack on the availability of services of a victim system or network resource, launched indirectly through many compromised computers on the Internet. To create attacks, attackers first discover vulnerable sites or hosts on the network. Then vulnerable hosts are exploited by attackers who use their vulnerability to gain access to these hosts. This chapter deals with the introduction, architecture and classification of DDoS Attacks.

scholarly journals Recent Analysis of Forged Request Headers Constituted by HTTP DDoS

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3820
Author(s):  
Abdul Ghafar Jaafar ◽  
Saiful Adli Ismail ◽  
Mohd Shahidan Abdullah ◽  
Nazri Kama ◽  
Azri Azmi ◽  
...  
Keyword(s):  
Critical Analysis ◽  
Denial Of Service ◽  
Web Server ◽  
Recent Analysis ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Application Layer ◽  
Ddos Attack ◽  
Client Request ◽  
Attack Patterns

Application Layer Distributed Denial of Service (DDoS) attacks are very challenging to detect. The shortfall at the application layer allows formation of HTTP DDoS as the request headers are not compulsory to be attached in an HTTP request. Furthermore, the header is editable, thus providing an attacker with the advantage to execute HTTP DDoS as it contains almost similar request header that can emulate a genuine client request. To the best of the authors’ knowledge, there are no recent studies that provide forged request headers pattern with the execution of the current HTTP DDoS attack scripts. Besides that, the current dataset for HTTP DDoS is not publicly available which leads to complexity for researchers to disclose false headers, causing them to rely on old dataset rather than more current attack patterns. Hence, this study conducted an analysis to disclose forged request headers patterns created by HTTP DDoS. The results of this study successfully disclose eight forged request headers patterns constituted by HTTP DDoS. The analysis was executed by using actual machines and eight real attack scripts which are capable of overwhelming a web server in a minimal duration. The request headers patterns were explained supported by a critical analysis to provide the outcome of this paper.

scholarly journals Application Layer DDoS Attack Defense Methods and A new Defense Mechanism against Flooding

2020 ◽  
Vol 8 (6) ◽  
pp. 208-212
Keyword(s):  
Machine Learning ◽  
Defense Mechanisms ◽  
Defense Mechanism ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Network Environment ◽  
Ddos Attacks ◽  
Application Layer ◽  
New Approach ◽  
Ddos Attack

In a network environment, Distributed Denial of Service (DDoS) attacks eemploys a network or server is unavailable to its normal users. Application-layer Distributed Denial of Service (App-DDoS) attacks are serious issues for the webserver itself. The multitude and variety of such attacks and defense approaches are overwhelming. This paper here follows, we analyze the different defense mechanisms for application-layer DDoS attacks and proposes a new approach to defend using machine learning.

scholarly journals Application Layer Distributed Denial of Service Attacks Defense Techniques : A review

2018 ◽  
Vol 7 (4) ◽  
pp. 113 ◽  
Author(s):  
Subhi R. M. Zeebaree ◽  
Karzan H. Sharif ◽  
Roshna M. Mohammed Amin
Keyword(s):  
Denial Of Service ◽  
The Internet ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Application Layer ◽  
Detection Techniques ◽  
Clear View ◽  
Methods And Techniques ◽  
Experimental Approaches ◽  
Detection Mechanisms

Currently distributed denial of service (DDoS) is the most sever attack that effect on the internet convenience. The main goal of these attacks is to prevent normal users from accessing the internet services such as web servers. However the more challenge and difficult types to detect is application layer DDoS attacks because of using legitimate client to create connection with victims. In this paper we give a review on application layer DDoS attacks defense or detection mechanisms. Furthermore, we summarize several experimental approaches on detection techniques of application layer DDoS attacks. The main goal of this paper is to get a clear view and detailed summary of the recent algorithms, methods and techniques presented to tackle these serious types of attacks.

Detecting App-DDoS Attacks Based on Marking Access and d-SVDD

2013 ◽  
Vol 347-350 ◽  
pp. 3734-3739 ◽  
Author(s):  
Jin Ling Li ◽  
Bin Qiang Wang
Keyword(s):  
Denial Of Service ◽  
Detection Algorithm ◽  
Decision Boundary ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Application Layer ◽  
Time Space

In order to enhance the extensibility of current attack feature extracted and detection means for App-DDoS(Application Layer Distributed Denial of Service, App-DDoS) attacks, a novel feature extracted method based on marking access and a new detection algorithm named d-SVDD are proposed. After expressing kinds of App-DDoS attacks as characteristic vectors by access marked strategy and feature extracted strategy, d-SVDD algorithm is used for secondary classification and detection of pre-set area around decision boundary based on SVDD. It is proved by experiments that the proposed feature extracted and detection means can realize effective detection for kinds of App-DDoS attacks, both have satisfying time, space and extensibility performance.

scholarly journals Deep learning model for distributed denial of service (DDoS) detection

2022 ◽  
Vol 9 (2) ◽  
pp. 109-118
Author(s):  
Chaminda Tennakoon ◽  
◽  
Subha Fernando ◽  
Keyword(s):  
Deep Learning ◽  
Denial Of Service ◽  
False Negative ◽  
False Negative Rate ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Application Layer ◽  
Online Services ◽  
Ddos Detection ◽  
Osi Model

Distributed denial of service (DDoS) attacks is one of the serious threats in the domain of cybersecurity where it affects the availability of online services by disrupting access to its legitimate users. The consequences of such attacks could be millions of dollars in worth since all of the online services are relying on high availability. The magnitude of DDoS attacks is ever increasing as attackers are smart enough to innovate their attacking strategies to expose vulnerabilities in the intrusion detection models or mitigation mechanisms. The history of DDoS attacks reflects that network and transport layers of the OSI model were the initial target of the attackers, but the recent history from the cybersecurity domain proves that the attacking momentum has shifted toward the application layer of the OSI model which presents a high degree of difficulty distinguishing the attack and benign traffics that make the combat against application-layer DDoS attack a sophisticated task. Striding for high accuracy with high DDoS classification recall is key for any DDoS detection mechanism to keep the reliability and trustworthiness of such a system. In this paper, a deep learning approach for application-layer DDoS detection is proposed by using an autoencoder to perform the feature selection and Deep neural networks to perform the attack classification. A popular benchmark dataset CIC DoS 2017 is selected by extracting the most appealing features from the packet flows. The proposed model has achieved an accuracy of 99.83% with a detection rate of 99.84% while maintaining the false-negative rate of 0.17%, which has the heights accuracy rate among the literature reviewed so far.

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