Composite and efficient DDoS attack detection framework for B5G networks

Amaizu, Gabriel Chukwunonso; Nwakanma, Cosmas Ifeanyi; Bhardwaj, Sanjay; Lee, J. M.; Kim, Dong-Seong

doi:10.1016/j.comnet.2021.107871

Cited by 71 publications

(41 citation statements)

References 35 publications

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“…Amaizu et al. ( 2021 ) proposed an efficient DL-based DDoS attack detection framework in 5G and B5G environments. The proposed framework is developed by concatenating two differently designed DNN models, coupled with a feature extraction algorithm, i.e.…”

Section: Methodologies Strengths and Weaknessesmentioning

confidence: 99%

“…( 2020 ) 2020 CNN model i.e., ResNet CICDDoS2019 Syn, TFTP, DNS, LDAP, UDP Lag, MSSQL, NetBIOS, SNMP, SSDP, NTP, UDP, and Normal traffic Amaizu et al. ( 2021 ) 2021 DNN CICDDoS2019 UDP LAG, SYN, DNS, MSSQL, NTP, SSDP, TFTP, NetBIOS, LDAP, UDP and Benign Cil et al. ( 2021 ) 2021 DNN CICDDoS2019 Twelve DDoS attacks on the training day and seven attacks during the testing day Supervised Sequence Learning Li et al.…”

Section: Available Ddos Benchmarked Datasets and Classes Of Attacks In Datasetsmentioning

confidence: 99%

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Deep learning approaches for detecting DDoS attacks: a systematic review

Mittal¹,

Kumar²,

Behal³

2022

Soft Comput

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In today’s world, technology has become an inevitable part of human life. In fact, during the Covid-19 pandemic, everything from the corporate world to educational institutes has shifted from offline to online. It leads to exponential increase in intrusions and attacks over the Internet-based technologies. One of the lethal threat surfacing is the Distributed Denial of Service (DDoS) attack that can cripple down Internet-based services and applications in no time. The attackers are updating their skill strategies continuously and hence elude the existing detection mechanisms. Since the volume of data generated and stored has increased manifolds, the traditional detection mechanisms are not appropriate for detecting novel DDoS attacks. This paper systematically reviews the prominent literature specifically in deep learning to detect DDoS. The authors have explored four extensively used digital libraries (IEEE, ACM, ScienceDirect, Springer) and one scholarly search engine (Google scholar) for searching the recent literature. We have analyzed the relevant studies and the results of the SLR are categorized into five main research areas: (i) the different types of DDoS attack detection deep learning approaches, (ii) the methodologies, strengths, and weaknesses of existing deep learning approaches for DDoS attacks detection (iii) benchmarked datasets and classes of attacks in datasets used in the existing literature, and (iv) the preprocessing strategies, hyperparameter values, experimental setups, and performance metrics used in the existing literature (v) the research gaps, and future directions.

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Section: Methodologies Strengths and Weaknessesmentioning

confidence: 99%

Section: Available Ddos Benchmarked Datasets and Classes Of Attacks In Datasetsmentioning

confidence: 99%

Deep learning approaches for detecting DDoS attacks: a systematic review

Mittal¹,

Kumar²,

Behal³

2022

Soft Comput

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“…As more devices respond, the attack volume rises until the target network is brought down by the cumulative volume of these SNMP responses. NetBIOS [8] is to allow applications on different computers to communicate and establish sessions to access shared resources and communicate with one another through a local area network. On a this-aware network, the NetBIOS Name Service (NBNS) allows for hostname and address mapping.…”

Section: Reflection-based Ddos Attacksmentioning

confidence: 99%

“…A denial-of-service attack occurs when the victim is unable to communicate with other NetBIOS hosts. In Lightweight Directory Access Protocol (LDAP) DDoS attack [8], the attacker sends an LDAP request to an LDAP server to produce large replies, with a spoofed sender IP address. Domain Name System (DNS) [9] amplification is a reflection-based DDoS attack, which manipulates domain name systems and makes them flood the target system with large quantities of UDP packets, which bring down the target servers.…”

Section: Reflection-based Ddos Attacksmentioning

confidence: 99%

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Detection of Different DDoS Attacks Using Machine Learning Classification Algorithms

Dasari¹,

Devarakonda²

2021

ISI

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Cyber attacks are one of the world's most serious challenges nowadays. A Distributed Denial of Service (DDoS) attack is one of the most common cyberattacks that has affected availability, which is one of the most important principles of information security. It leads to so many negative consequences in terms of business, production, reputation, data theft, etc. It shows the importance of effective DDoS detection mechanisms to reduce losses. In order to detect DDoS attacks, statistical and data mining methods have not been given good accuracy values. Researchers get good accuracy values while detecting DDoS attacks by using classification algorithms. But researchers, use individual classification algorithms on generalized DDoS attacks. This study used six machine learning classification algorithms to detect eleven different DDoS attacks on different DDoS attack datasets. We used the CICDDoS2019 dataset which is collected from the Canadian Institute of Cyber security in this study. It contains eleven different DDoS attack datasets in CSV file format. On each DDoS attack, we evaluated the effectiveness of the classification methods Logistic regression, Decision tree, Random Forest, Ada boost, KNN, and Naive Bayes, and determined the best classification algorithms for detection.

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