Distributed reflection denial of service attack: A critical review

Nuiaa, Riyadh Rahef; Manickam, Selvakumar; Alsaeedi, Ali Hakem

doi:10.11591/ijece.v11i6.pp5327-5341

Cited by 11 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The detection model, underpinning this research, underwent comprehensive training, testing, and evaluation phases using this dataset. Despite the absence of explicit dataset details in the referenced link, the publication is presumed to delineate the dataset's characteristics and composition [22]. The dataset is characterized by the following attributes:…”

Section: Results and Analysismentioning

confidence: 99%

Evaluating Machine Learning and Deep Learning Models for Enhanced DDoS Attack Detection

Owaid,

Hammoodi

2024

MMEP

View full text Add to dashboard Cite

In the realm of network security, distributed denial of service (DDoS) attacks pose a formidable threat, often resulting in operational disruptions and substantial financial losses. Traditional methods for DDoS detection struggle to adapt to the rapidly evolving attack methodologies, leading to compromised detection robustness and accuracy. The urgent need for more sophisticated detection mechanisms is evident. This investigation explores the effectiveness of advanced deep learning and ensemble machine learning models in identifying DDoS threats. A comprehensive approach is employed, leveraging a multitude of base classifiers to construct a robust and precise detection system. Integral to this study is the application of convolutional neural networks (CNNs), a deep learning variant, adept at discerning complex patterns and relationships within network traffic data. These models excel in autonomously extracting pertinent features, thereby enabling efficient detection of intricate DDoS attacks. A critical step in this methodology involves the collection of a comprehensive network traffic dataset, encompassing both normal and DDoS attack scenarios. This dataset undergoes a rigorous preprocessing and enhancement phase to ensure a balanced and representative training set. Subsequently, the augmented data is utilized to train the proposed models. The performance of these models is evaluated using a variety of metrics. Results from the experiments demonstrate that both machine learning and deep learning models significantly surpass existing techniques in DDoS detection. By amalgamating the strengths of various classifiers and neural networks, the method enhances detection precision and resistance to diverse attack variations. Comparative analyses reveal impressive performance metrics, with models such as CNN 1D and Alex Net achieving high levels of accuracy and precision. The outcomes of this study underscore the superiority of deep learning models in identifying both prevalent and novel DDoS attack patterns, thereby highlighting their potential in countering evolving cyber threats. The findings advocate for the enhanced precision and adaptability of the proposed approach in DDoS detection, marking a significant advancement in the field.

show abstract

Section: Results and Analysismentioning

confidence: 99%

Evaluating Machine Learning and Deep Learning Models for Enhanced DDoS Attack Detection

Owaid,

Hammoodi

2024

MMEP

View full text Add to dashboard Cite

show abstract

“…Cybersecurity researchers say DDoS attacks have become more common in recent years. DDoS attacks have also significantly impacted the functioning of companies and organizations worldwide, causing financial and technical damage due to their destructive effect while sabotaging DNS infrastructure [73]. The DNS amplification attack is more popular than other DNS attacks because the attacker prefers attacks with a high effect and a low cost.…”

Section: Resultsmentioning

confidence: 99%

“…This type of attack is classified into two types based on the protocol used in the attack: TCP-based DRDoS assault and UDP-based DRDoS assault, which consists of two parts: amplification and reflection [73]. As a result, some attacks are protocol-dependent, while others are protocol-independent.…”

Section: A the Mechanics Of Drdos Attacksmentioning

confidence: 99%

A Comprehensive Review of DNS-based Distributed Reflection Denial of Service (DRDoS) Attacks: State-of-the-Art

Nuiaa¹,

Manickam²,

ALsaeedi³

2022

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

Cyberattacks significantly impact the services based on the internet that is used in our daily lives. Any disruption will make it extremely difficult for us to carry out our daily activities. Cyberattacks will disrupt online services, exploit vulnerabilities to breach databases and servers, and so on. Various systems and services contribute to the Internet's seamless functionality. The Domain Name System (DNS) is one of the most important services. DNS is used to resolve domain names into machine-readable IP addresses. DNS, like many other Internet services, is vulnerable to cyber-attacks. While DNS faces a slew of threats, one in particular appears to stand out. DNS is vulnerable to a variety of distributed denial-of-service attacks. The distributed reflection denial of service (DRDoS) attack, a flooding attack against DNS servers that renders them unavailable, disrupting domain name resolution activities, is one of the most common variants. DRDoS attacks have been on the rise in recent years. DNS lookup outages would significantly impact our online activities in the world of ultra-connectivity because they are typically the first step in establishing a connection with a server. The purpose of this paper is to present a state-of-the-art review of DRDoS attack detection and mitigation algorithms as well as the datasets on which these algorithms operate. Finally, we discussed each of these algorithms' relative merits and demerits.

show abstract

“…This increases the attack bandwidth and the likelihood that a denial of service outage will result from the attack. DNS, NTP, SNMP, and SSDP are the protocols for UDP-based DRDoS attacks; SYN and BGP are the protocols for TCP-based DRDoS attacks [ 178 ]. Firewalls, intrusion detection systems, machine learning, etc.…”

Section: Devices’ Vulnerabilities and Requirementsmentioning

confidence: 99%

Internet of Things: Security and Solutions Survey

Sadhu

Yanambaka

Abdelgawad

2022

Sensors

128

View full text Add to dashboard Cite

The overwhelming acceptance and growing need for Internet of Things (IoT) products in each aspect of everyday living is creating a promising prospect for the involvement of humans, data, and procedures. The vast areas create opportunities from home to industry to make an automated lifecycle. Human life is involved in enormous applications such as intelligent transportation, intelligent healthcare, smart grid, smart city, etc. A thriving surface is created that can affect society, the economy, the environment, politics, and health through diverse security threats. Generally, IoT devices are susceptible to security breaches, and the development of industrial systems could pose devastating security vulnerabilities. To build a reliable security shield, the challenges encountered must be embraced. Therefore, this survey paper is primarily aimed to assist researchers by classifying attacks/vulnerabilities based on objects. The method of attacks and relevant countermeasures are provided for each kind of attack in this work. Case studies of the most important applications of the IoT are highlighted concerning security solutions. The survey of security solutions is not limited to traditional secret key-based cryptographic solutions, moreover physical unclonable functions (PUF)-based solutions and blockchain are illustrated. The pros and cons of each security solution are also discussed here. Furthermore, challenges and recommendations are presented in this work.

show abstract

Distributed reflection denial of service attack: A critical review

Cited by 11 publications

References 39 publications

Evaluating Machine Learning and Deep Learning Models for Enhanced DDoS Attack Detection

Evaluating Machine Learning and Deep Learning Models for Enhanced DDoS Attack Detection

A Comprehensive Review of DNS-based Distributed Reflection Denial of Service (DRDoS) Attacks: State-of-the-Art

Internet of Things: Security and Solutions Survey

Contact Info

Product

Resources

About