CALD: Surviving Various Application-Layer DDoS Attacks That Mimic Flash Crowd

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.

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DETECTION OF ANOMALY BASED APPLICATION LAYER DDoS ATTACKS USING MACHINE LEARNING APPROACHES

i-manager s Journal on Computer Science ◽

10.26634/jcom.4.2.8120 ◽

2016 ◽

Vol 4 (2) ◽

pp. 6

Author(s):

VANI NIDHI M.S.P.S. ◽

PRASAD K. MUNIVARA ◽

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Keyword(s):

Machine Learning ◽

Learning Approaches ◽

Ddos Attacks ◽

Application Layer

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Handling System Overload Resulting from DDoS Attacks and Flash Crowd Events

2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing ◽

10.1109/cscloud.2015.66 ◽

2015 ◽

Cited By ~ 3

Author(s):

Zaid Al-Ali ◽

Basheer Al-Duwairi ◽

Ahmad T. Al-Hammouri

Keyword(s):

Ddos Attacks ◽

Handling System ◽

Flash Crowd

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A Deep Learning Approach for Detection of Application Layer Attacks in Internet

Handling Priority Inversion in Time-Constrained Distributed Databases - Advances in Data Mining and Database Management ◽

10.4018/978-1-7998-2491-6.ch010 ◽

2020 ◽

pp. 175-188

Author(s):

V. Punitha ◽

C. Mala

Keyword(s):

Deep Learning ◽

Transport Layer ◽

Classification Model ◽

Learning Approach ◽

Ddos Attacks ◽

Application Layer ◽

Learning Models ◽

Technological Transformation ◽

Application Deployment ◽

Machine Learning Models

The recent technological transformation in application deployment, with the enriched availability of applications, induces the attackers to shift the target of the attack to the services provided by the application layer. Application layer DoS or DDoS attacks are launched only after establishing the connection to the server. They are stealthier than network or transport layer attacks. The existing defence mechanisms are unproductive in detecting application layer DoS or DDoS attacks. Hence, this chapter proposes a novel deep learning classification model using an autoencoder to detect application layer DDoS attacks by measuring the deviations in the incoming network traffic. The experimental results show that the proposed deep autoencoder model detects application layer attacks in HTTP traffic more proficiently than existing machine learning models.

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Characterizing the Impacts of Application Layer DDoS Attacks

2017 IEEE International Conference on Web Services (ICWS) ◽

10.1109/icws.2017.58 ◽

2017 ◽

Cited By ~ 4

Author(s):

Muhui Jiang ◽

Chenxu Wang ◽

Xiapu Luo ◽

MiuTung Miu ◽

Ting Chen

Keyword(s):

Ddos Attacks ◽

Application Layer

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Study on Auto Detecting Defence Mechanisms against Application Layer Ddos Attacks in SIP Server

Journal of Networks ◽

10.4304/jnw.10.6.344-352 ◽

2015 ◽

Vol 10 (6) ◽

Author(s):

Muhammad Morshed Alam ◽

Muhammad Yeasir Arafat ◽

Feroz Ahmed

Keyword(s):

Ddos Attacks ◽

Application Layer ◽

Defence Mechanisms ◽

Sip Server

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User Activity Based Application-Layer DoS/DDoS Attack Defense Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.742.693 ◽

2015 ◽

Vol 742 ◽

pp. 693-697

Author(s):

Yu Lei Tsien ◽

Rong Li Gai

Keyword(s):

Intrusion Detection ◽

System Performance ◽

Intrusion Detection Systems ◽

Ddos Attacks ◽

Application Layer ◽

Network Layer ◽

Detection Systems ◽

Ddos Attack ◽

User Activity ◽

Bottleneck Resources

In application-layer DoS/DDoS attacks, malicious users attack the victim server by sending lots of legitimate requesting packages, which overwhelm the server bottleneck resources. Normal user’s request thus may not be satisfied. The traditional intrusion detection systems for network-layer cannot effectively identify this attack, and recent researches on this kind of attack are mainly for Web servers. This paper proposed a new defense algorithm based on user activity for topic-based Pub/Sub communication servers in mobile push notification systems. Users consuming system bottleneck resources the most can get high scores and thus are considered overactive. With some resource retaken strategy, overactive users’ connections will be dropped according to system performance level. Therefore, the system can get rid of latent threatens. Experiments indicated that this algorithm can identify normal and abnormal users well.

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