Alleviation of DDoS attack using advance technique

Mahale, Vishal V.; Pareek, Nikita P.; Uttarwar, Vrushali U.

doi:10.1109/icimia.2017.7975595

Cited by 10 publications

(3 citation statements)

References 16 publications

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“…In addition, the author distinguishes methods of DDoS detective work based on viewing, blocking, detecting, and minimizing comments. In [32], a step-by-step approach to DDoS attack mitigation was presented, where the entire process of mitigating DDoS attacks was forced to a single layer or multiple layers. To increase the security of DDoS attacks, the go-layer process has become a useful solution.…”

Section: Literature Reviewmentioning

confidence: 99%

Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron

et al. 2023

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Distributed denial of service (DDoS) attacks pose an increasing threat to businesses and government agencies. They harm internet businesses, limit access to information and services, and damage corporate brands. Attackers use application layer DDoS attacks that are not easily detectable because of impersonating authentic users. In this study, we address novel application layer DDoS attacks by analyzing the characteristics of incoming packets, including the size of HTTP frame packets, the number of Internet Protocol (IP) addresses sent, constant mappings of ports, and the number of IP addresses using proxy IP. We analyzed client behavior in public attacks using standard datasets, the CTU-13 dataset, real weblogs (dataset) from our organization, and experimentally created datasets from DDoS attack tools: Slow Lairs, Hulk, Golden Eyes, and Xerex. A multilayer perceptron (MLP), a deep learning algorithm, is used to evaluate the effectiveness of metrics-based attack detection. Simulation results show that the proposed MLP classification algorithm has an efficiency of 98.99% in detecting DDoS attacks. The performance of our proposed technique provided the lowest value of false positives of 2.11% compared to conventional classifiers, i.e., Naïve Bayes, Decision Stump, Logistic Model Tree, Naïve Bayes Updateable, Naïve Bayes Multinomial Text, AdaBoostM1, Attribute Selected Classifier, Iterative Classifier, and OneR.

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Section: Literature Reviewmentioning

confidence: 99%

Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron

et al. 2023

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“…More recently, Bin Fan and others proposed Cuckoo Filter [18] that supports count and deletion operations, does not introduce false negatives during deletion operation, has fewer bits per entry for optimum false positive rate (< 3%), and can maintain stable false positive rate with higher load for up to 95%. After its introduction, recently, several research works have used Cuckoo Filter to speed up the lookup process in the areas of Networking and Security [25] [26] [27] [28] [29]. However, there have not been significant researches utilizing Cuckoo Filter in the area of Big Data -only a few on semi-structured data [30], encrypted data [31] and dynamic data-sets [32].…”

Section: Related Workmentioning

confidence: 99%

Improving Lookup and Query Execution Performance in Distributed Big Data Systems using Cuckoo Filter

Mosharraf

Adnan

2021

Preprint

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Performance is a critical concern when reading and writing data from billions of records stored in Big Data warehouse. We introduce two scopes for query performance improvement. One is to improve performance of lookup queries after data deletion in Big Data systems that use Eventual Consistency. We propose a scheme to improve lookup performance after data deletion by using Cuckoo Filter. Another scope for improvement is to avoid unnecessary network round-trip for querying in remote nodes in a distributed Big Data cluster when it is known that the nodes do not have requested partition of data. We propose a scheme using probabilistic filters that are looked up before querying remote nodes, so that queries resulting in no data can be skipped from passing through the network. We evaluate our schemes with Cassandra using real dataset and show that each scheme can improve performance of lookup queries for up to 100%.

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“…In their model, the diffserv was based on network nodes applying different forwarding treatments to packets with those IP headers were marked with different Diffserv Codepoints (DSCPs). By Vishal V. Mahale et al, a co-operative cross layer mechanism for mitigation of DDoS attack was introduced [28]. In their consideration, to enhance the overall reliability against DDoS attacks, a combination of Device-Driver Packet Filter and Remote Firewall were a solution as a cross-layer approach in their model.…”

Section: Introductionmentioning

confidence: 99%

A DRDoS Detection and Defense Method Based on Deep Forest in the Big Data Environment

Cheng

Wang

et al. 2019

Symmetry

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Distributed Denial of Service (DDoS) has developed multiple variants, one of which is Distributed Reflective Denial of Service (DRDoS). With the increasing number of Internet of Things (IoT) devices, the threat of DRDoS attack is growing, and the damage of a DRDoS attack is more destructive than other types. The existing DDoS detection methods cannot be generalized in DRDoS early detection, which leads to heavy load or degradation of service when deployed at the final point. In this paper, we propose a DRDoS detection and defense method based on deep forest model (DDDF), and then we integrate differentiated service into defense model to filter out DRDoS attack flow. Firstly, from the statistics perspective on different stages of DRDoS attack flow in the big data environment, we extract a host-based DRDoS threat index (HDTI) from the network flows. Secondly, using the HDTI feature we build a DRDoS detection and defense model based on the deep forest, which consists of 1 extreme gradient boost (XGBoost) forest estimator, 2 random forest estimators, and 2 extra random forest estimators in each layer. Lastly, the differentiated service procedure applies the detection result from DDDF to drop the traffic identified in different stages and different detection points. Theoretical analysis and experiments show that the method we proposed can effectively identify DRDoS attack with higher detection rate and a lower false alarm rate, the defense model also shows distinguishing ability to effectively eliminate the DRDoS attack flows, and dramatically mitigate the damage of a DRDoS attack.Symmetry 2019, 11, 78 2 of 22 access requests, and the internal server or devices reflecting more packets, the DRDoS attack brings the danger of congestion effectively, and the high accessibility of conducting such attacks is achieved due to the easy-use tools and relatively low cost.In 26 September 2016, and 21 October 2016, the network in the United States was also attacked by DRDoS, the attackers unitized tens of millions of webcams and digital video recorders (DVR), sending packets to the Internet service provider (ISP), causing over 1200 websites, including Twitter, Amazon, Reddit, and Netflix to be inaccessible for millions of Internet users. Moreover, the DRDoS attacks witnessed this year have the trend to be more dedicated and sophisticated with higher diversities, as a consequence, a swift, smart and solid cyber-attack detection mechanism is needed for security control for the increasingly vulnerable network.The rest of the paper is organized as follows. Related work is discussed in Section 2. In Section 3, we analyze the feature of general type DRDoS attack as the base theorem for the method we proposed in Section 4, where we will introduce the algorithm of the DRDoS detection method and characterized each part of it. We will then introduce the deep forest model for classification and apply differentiated service in the defense method. And we define differentiated service as a procedure that implements a simple and scalable mechani...

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Alleviation of DDoS attack using advance technique

Cited by 10 publications

References 16 publications

Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron

Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron

Improving Lookup and Query Execution Performance in Distributed Big Data Systems using Cuckoo Filter

A DRDoS Detection and Defense Method Based on Deep Forest in the Big Data Environment

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