E-Had: A distributed and collaborative detection framework for early detection of DDoS attacks

Patil, Nilesh Vishwasrao; Krishna, C. Rama; Kumar, Krishan; Behal, Sunny

doi:10.1016/j.jksuci.2019.06.016

Cited by 16 publications

(19 citation statements)

References 18 publications

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“…Sachdeva et al 30 recommended the distributed internet service provider (ISP) domain DDoS deployment because of several reasons: (i) Distributed environment, (ii) Practically feasible solution, (iii) Robust implementation, (v) Autonomous control, etc. Further, few researchers 31‐43 proposed distributed processing framework‐based DDoS detection systems. This type of deployment not only quickly recognizes attacks but also efficiently explores a huge volume of traffic by employing a cluster of slaves in a coordinated manner.…”

Section: A Ddos Defense Deployment Taxonomymentioning

confidence: 99%

“…Each class of traditional frameworks‐based DDoS defense deployment summarized in Table 2. There are several challenges in the traditional framework‐based DDoS deployment model: 5,43,54,91,92 The traditional framework‐based DDoS defense system can deploy on single or multiple machines. However, no communication between them while analyzing network traffic flows. Each machine performs its task separately.…”

Section: A Ddos Defense Deployment Taxonomymentioning

confidence: 99%

“…Few authors 31‐43,91,92,95,96 proposed distributed framework‐based mechanisms and most of them deployed their mechanism on the Apache Hadoop. Apache Hadoop distributed framework follows a two‐step process to analyze data efficiently.…”

Section: Review Of Existing Distributed Processing Framework‐based Ddos Detection Systemmentioning

confidence: 99%

See 2 more Smart Citations

Distributed frameworks for detecting distributed denial of service attacks: A comprehensive review, challenges and future directions

Patil

Krishna

Kumar

2021

Concurrency and Computation

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A distributed denial of service (DDoS) attack is a significant threat to web‐based applications and hindering legitimate traffic (denies access to benign users) by overwhelming the victim system or its infrastructure (service, bandwidth, networking devices, etc.) with a large volume of attack traffic. It leads to a delay in responses or sometimes a crash victim system. Even a few moments of pause in web‐based applications lead to a huge monetary loss and a bad reputation in the market. Several approaches available in the literature to protect websites from different types of DDoS attacks. However, incidents and volume sizes of DDoS attacks are growing quarter by quarter. Further, various challenges in the traditional framework based defense mechanisms: itself becoming a victim of attacks while analyzing a massive amount of traffic, require more time for detection process, no coordination among the modules, etc. This paper presents a comprehensive DDoS defense deployment taxonomy and critically reviewed existing distributed frameworks based DDoS attack detection systems. Further, characterized several existing distributed processing frameworks to select an appropriate one for deploying DDoS attack detection mechanisms. Finally, several evaluation metrics, open issues, discussion on available datasets including their limitations, and future directions are presented.

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Section: A Ddos Defense Deployment Taxonomymentioning

confidence: 99%

Section: A Ddos Defense Deployment Taxonomymentioning

confidence: 99%

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Distributed frameworks for detecting distributed denial of service attacks: A comprehensive review, challenges and future directions

Patil

Krishna

Kumar

2021

Concurrency and Computation

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“…Rodrigues et al [28] Javaid et al [27] Rodrigues et al [26] François et al [35] Chen et al [36] Chin et al [37] Patil et al [38] Sagirlar et al [39] Our approach…”

Section: Responsiveness Scalabilitymentioning

confidence: 99%

Collaborative Blockchain-Based Detection of Distributed Denial of Service Attacks Based on Internet of Things Botnets

et al. 2019

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Internet of Things is one of the most significant latest developments in computer science. It is common for modern computing infrastructures to partially consist of numerous low power devices that are characterized by high diversity in both hardware and software. Existing security models, approaches and solutions are not able to sufficiently protect such systems. In this paper we propose the use of lightweight agents installed at multiple internet of things (IoT) installations (e.g., smart-homes), in order to collaboratively detect distributed denial of service (DDoS) attacks conducted by the use of IoT devices botnets. Specifically, agents exchange outbound traffic information in order to identify possible victims of DDoS attacks. This information exchange is governed by a blockchain smart contract, that ensures the integrity of both the procedure and the information. A simulation of the operation of the proposed methodology has been conducted in order to evaluate both its detection efficiency and its resilience against malicious agents that aim to falsify results.

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“…et al and Behal et al worked on DDoS just for early detection[21,22]. Previous studies have almost exclusively focused on a specific traffic type or have only attack detection mechanism.Today, the industry-standard mitigation infrastructures which target DDoS attacks work with the BGP Protocol.…”

mentioning

confidence: 99%

An Inter-Domain Attack Mitigating Solution

Akin¹,

Bük²,

Uçar³

2020

Turk J Elec Eng & Comp Sci

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Online services on the Internet are increasing day by day, and in parallel, the number of cyber-attacks is rapidly increasing. These attacks are not always about data theft, but they can cause severe damage by denial of service attacks. Intrusion Prevention System products that many organizations use at the border of their enterprise networks are not strong enough to protect against DoS attacks. The typical way to mitigate such attacks is to get support from a service provider. However, a service provider only provides solutions for the traffic originating from itself. If the source of attack is in another ISP domain, it is possible to inform that ISP via phone or e-mail. As a result, the source of the attack is blocked by the manual intervention of the service provider whose domain hosts it. Border Gateway Protocol (BGP) based solutions are also available for automating a blocking system, but not all enterprise networks support BGP. In this research, we have developed a centralized automation solution for software defined network (SDN) environments that is capable of preventing cyber-attacks at the source of attack. This solution does not require any BGP support. Non-SDN environments can also use this attack mitigation and notification system. In the long run, we may use this system to create a national protection shield in order to mitigate Cybersecurity attacks.

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E-Had: A distributed and collaborative detection framework for early detection of DDoS attacks

Cited by 16 publications

References 18 publications

Distributed frameworks for detecting distributed denial of service attacks: A comprehensive review, challenges and future directions

Distributed frameworks for detecting distributed denial of service attacks: A comprehensive review, challenges and future directions

Collaborative Blockchain-Based Detection of Distributed Denial of Service Attacks Based on Internet of Things Botnets

An Inter-Domain Attack Mitigating Solution

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