An Effective Mechanism to Mitigate Real-Time DDoS Attack

Bakar, Rana Abu; Aldegheishem, Abdulaziz; Majeed, Muhammad Faran; Mehmood, Amjad; Maryam, Hafsa; Alrajeh, Nabil; Maple, Carsten; Jawad, Muhammad

doi:10.1109/access.2020.2995820

Cited by 24 publications

(11 citation statements)

References 40 publications

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“…Attackers usually use many computers, which are called bots or zombies, to transfer malicious traffic. The bots are often the computers compromised by attackers, while their legitimate controllers are unaware of that [5]. Figure 1 indicates a basic diagram of how a DDoS attack occurs, in which the attacker controls a botnet to employ the attack on the target.…”

Section: Introductionmentioning

confidence: 99%

A hybrid machine learning approach for detecting unprecedented DDoS attacks

2022

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Service availability plays a vital role on computer networks, against which Distributed Denial of Service (DDoS) attacks are an increasingly growing threat each year. Machine learning (ML) is a promising approach widely used for DDoS detection, which obtains satisfactory results for pre-known attacks. However, they are almost incapable of detecting unknown malicious traffic. This paper proposes a novel method combining both supervised and unsupervised algorithms. First, a clustering algorithm separates the anomalous traffic from the normal data using several flowbased features. Then, using certain statistical measures, a classification algorithm is used to label the clusters. Employing a big data processing framework, we evaluate the proposed method by training on the CICIDS2017 dataset and testing on a different set of attacks provided in the more up-to-date CICDDoS2019. The results demonstrate that the Positive Likelihood Ratio (LR+) of our method is approximately 198% higher than the ML classification algorithms.

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Section: Introductionmentioning

confidence: 99%

A hybrid machine learning approach for detecting unprecedented DDoS attacks

2022

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“…A gap in the [14] study is that it does not consider offloading in the large-scale network, while the quality of offloading tasks in the network is not considered in [15]. Safety is not considered in [16], while task execution is difficult for the user in [17,18]. Modern facilities to use are not well-thought-out [19].…”

Section: Introductionmentioning

confidence: 99%

Secured Optimized Resource Allocation in Mobile Edge Computing

Bibi¹,

Majeed²,

Ali

et al. 2022

Mobile Information Systems

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Mobile edge computing (MEC) uses multiple mobiles to compute several complex tasks that are unable to compute on a single device. Taking advantage of all abundant mobile resources and making a mobile cloud from them will be useful. This study aims to propose and implement a novel framework to cover challenges raised by application execution on resource-constrained devices. The purpose is to overcome the waste of resources in MEC and provide time efficiency to the data packets that are sent and received between offloaded and offloading devices. The main task of MEC is to offload tasks by first selecting resources and then allocating tasks to selected resources. A multiple linear regression algorithm is used for the selection of compatible devices. Particle swarm optimization techniques are used as a benchmark technique to design an algorithm for optimized resource allocation. Multiple mobile devices acted as a major component for making edge clouds. The study finds that response time of processing tasks is reduced, ineffectual resources become beneficial, increase in demand for mobile devices, and usage of mobile resources as a replacement for mobile edge cloud servers. Abundant resource usage of two or more edge clouds, that is, interedge resource usage is the originality of this research, while others are using intraedge resources only.

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“…The compromised devices transfer a large volume of packets without any breaks over the network, tricking the victim into recognizing them as legitimate traffic. As a result, not only does the host communicate with different devices but with different types of packets as well [8]. DDoS attack has been proven as a resource battleground between the defenders and attackers; the more the resources, the higher the chance of success [9].…”

Section: Introductionmentioning

confidence: 99%

Distributed Denial of Service (DDoS) Mitigation Using Blockchain—A Comprehensive Insight

et al. 2021

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Distributed Denial of Service (DDoS) attack is a major threat impeding service to legitimate requests on any network. Although the first DDoS attack was reported in 1996, the complexity and sophistication of these attacks has been ever increasing. A 2 TBps attack was reported in mid-August 2020 directed towards critical infrastructure, such as finance, amidst the COVID-19 pandemic. It is estimated that these attacks will double, reaching over 15 million, in the next 2 years. A number of mitigation schemes have been designed and developed since its inception but the increasing complexity demands advanced solutions based on emerging technologies. Blockchain has emerged as a promising and viable technology for DDoS mitigation. The inherent and fundamental characteristics of blockchain such as decentralization, internal and external trustless attitude, immutability, integrity, anonymity and verifiability have proven to be strong candidates, in tackling this deadly cyber threat. This survey discusses different approaches for DDoS mitigation using blockchain in varied domains to date. The paper aims at providing a comprehensive review, highlighting all necessary details, strengths, challenges and limitations of different approaches. It is intended to serve as a single platform to understand the mechanics of current approaches to enhance research and development in the DDoS mitigation domain.

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An Effective Mechanism to Mitigate Real-Time DDoS Attack

Cited by 24 publications

References 40 publications

A hybrid machine learning approach for detecting unprecedented DDoS attacks

A hybrid machine learning approach for detecting unprecedented DDoS attacks

Secured Optimized Resource Allocation in Mobile Edge Computing

Distributed Denial of Service (DDoS) Mitigation Using Blockchain—A Comprehensive Insight

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