Distributed denial of service: Attack techniques and mitigation

Vanitha, K.; Uma, S.; Mahidhar, S.K.

doi:10.1109/ccube.2017.8394146

Cited by 18 publications

(4 citation statements)

References 2 publications

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“…These features include the rate of entropy of IP source flow, packet size and number of unreachable packets of ICMP destination. Another analysis [3] devises new techniques for causing DDoS attacks and its mitigation along with the classification of those DDoS attacks In [4] the analysis of five popular machine learning algorithms, the decision tree algorithm performed better than the others in terms of overall performance.…”

Section: Literature Surveymentioning

confidence: 99%

Real-time Visualization and Classification of DDoS Attack using Supervised Learning Algorithms

R A,

G K

et al. 2024

Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security,

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With increased reliance on the Internet for various services, Distributed Denial of Service (DDoS) attacks are a major concern for organizations. DDoS attacks causes significant damage to the target system, leading to service downtime, data loss, and financial losses. To mitigate the impact of DDoS attacks, effective detection mechanisms are necessary. In this paper, a machine learning-based approach for DDoS attack detection is proposed. NSL-KDD Dataset of network traffic data containing both normal and attack traffic were used and various visualization techniques and machine learning models, including Random Forest Classifier, K-Nearest Neighbours Classifier and Logistic Regression were applied. The performance of these models was evaluated using crossvalidation and their accuracies were measured. The experimental results show that Random Forest Classifier outperforms other models with an accuracy of 99.33% before and after applying post pruning method. The experiment also focused on the false positives and false negatives and the implications of the results were discussed.

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Section: Literature Surveymentioning

confidence: 99%

Real-time Visualization and Classification of DDoS Attack using Supervised Learning Algorithms

R A,

G K

et al. 2024

Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security,

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show abstract

“…It opens connections, initiates processes, and performs transactions that would deplete finite resources like disk space and available memory. Application-level DDoS attacks are categorized into (Vanitha et al, 2017;Zargar et al, 2013): a) Flooding attacks with Reflection/Amplification: It is similar to network/transport level attack b) HTTP flooding attacks: Four varieties of this type of attacks are as follows…”

Section: Application-level Ddos Flooding Attacksmentioning

confidence: 99%

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Manjula¹,

Venkatesh²,

Venugopal³

2023

Journal of Computer Science

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Recent advancements in e-business, e-healthcare, e-governance, and online digital transactions have brought valuable benefits. Unfortunately, it raises severe cyber-attacks. Cyberattacks disrupt normal operations, try to retrieve confidential information and defense secrets, and subvert the nation's defense systems and Internet-connected devices. Cyber security solutions are required to detect, analyze, defend against threats and protect sensitive data from unauthorized access. This study gives a detailed survey of different cybersecurity attacks, like Denial-of-service attacks, Botnet Evasion Attacks, Malware invasions, Spam and phishing invasion, Spoofing, Domain Generation algorithms, Probing attacks, R2L, and U2R attacks. This research review emphasizes Machine Learning and Deep Learning-based approaches to Cybersecurity problems. This study's key highlights are the research challenges, cybersecurity issues, cyber security domains, and tools for the Intrusion detection system. Data sets play a vital role in cybersecurity research; hence, Private and Publically available datasets are reviewed in this study. Various performance matrices are discussed in this survey which can be used to evaluate the effectiveness of cybersecurity solutions.

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“…Current issues of antivirus companies, devoted to the identification of polymorphic code, have been analysed in this research. A new detection method has been developed using an artificial neural network based on heuristic analysis [23]. The efficacy of the experimental testing approach developed has been checked.…”

Section: Related Workmentioning

confidence: 99%

Supervised Deep Learning Vector Quantization to Detect MemCached DDOS Malware Attack on Cloud

Arul¹,

Punidha²

2021

SN COMPUT. SCI.

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Weak defence makes it vulnerable to multiple cloud devices, and victims often do not even know it has been compromised. Cloud assaults were planned, and the weakening of home automation and home safety technologies was addressed over a long period of time. The threats, however, were of a different kind. The target appears to be less interested in attackers, and everyone else is attempting to hack the system and connect it to the DDoS network to strike. A DDoS attack is a cyber-attack targeting MemCached, a caching mechanism intended to speed up websites and networks up to the MemCached Database. It helps to crash the database by overwhelming a website or application. Internet hackers submitted spoof applications to the insecure UDP MemCached site, which conceals the true identity of the sender by dressing up the IP address. The UDP is especially vulnerable since the data must be exchanged by the receiver before the contact ceases. The proposed version of Supervised SD-LVQ was used to detect MemCached attacks by malicious firmware on different Cloud computers. LVQ classifies a number of application service calls associated with multiple types of destructive attacks on cloud computers that have been MemCached for DDoS attacks. Test findings reveal that 97.23% is actually positive and 0.03% is unfairly negative.

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Distributed denial of service: Attack techniques and mitigation

Cited by 18 publications

References 2 publications

Real-time Visualization and Classification of DDoS Attack using Supervised Learning Algorithms

Real-time Visualization and Classification of DDoS Attack using Supervised Learning Algorithms

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Supervised Deep Learning Vector Quantization to Detect MemCached DDOS Malware Attack on Cloud

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