Load Balancing and Detection of Distributed Denial of Service Attacks Using Entropy Detection

doi:10.33103/uot.ijccce.21.4.6

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…As a result, the corresponding port is kept needlessly open. When the number of bogus SYN requests becomes significant, all the ports of the targeted machine are blocked, and it becomes unable to communicate with authorized users [13].…”

Section: Dataset Descriptionmentioning

confidence: 99%

Feature-based real-time distributed denial of service detection in SDN using machine learning and Spark

Samaan

Jeiad

2023

Bulletin EEI

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Recently, software defined networking (SDN) has been deployed extensively in diverse practical domains, providing a new direction in network management by separating the control plane from the data plane. Nevertheless, SDN is vulnerable to distributed denial of service (DDoS) attacks resulting from its centralized controller. Several studies have been suggested to address the DDoS attacks in SDN utilizing machine learning approaches. However, these approaches are resource-intensive and cause performance degradation since they cannot perform effectively in large-scale SDN networks that generate vast traffic statistics. To handle all these challenges, we build a DDoS attack detection model in SDN using Spark as a big data tool to overcome the limitations of conventional data processing methods. Four machine learning algorithms are employed. The decision tree (DT) is elected to be used for real-time deployment based on the performance results, which indicates that it has the best accuracy of 0.936. The model performance is compared with state-of-the-art and shows an overall better performance.

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Section: Dataset Descriptionmentioning

confidence: 99%

Feature-based real-time distributed denial of service detection in SDN using machine learning and Spark

Samaan

Jeiad

2023

Bulletin EEI

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“…Entropy is a measurement of how well a cryptographic technique works. Encryption algorithms are used to keep information secure [34], [35]. In order to calculate the entropy of a system, The researchers make use of Shannon's formula.…”

Section: E Entropymentioning

confidence: 99%

In-Depth Assessment of Cryptographic Algorithms Namely DES, 3DES, AES, RSA, and Blowfish

2023

IJCCCE

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Securing information is difficult in the modern internet era, as terabytes of data are generated daily online and online transactions occur virtually every second. The current world's information security relies heavily on cryptography, which makes the internet a safer environment. Making information incoherent to an unauthorized person is done through the use of cryptography. Providing legitimate users with confidentiality as a result. There are a wide variety of cryptographic algorithms suitable for this purpose. An ideal cryptography method would allow the user to do their job without breaking the bank. Unfortunately, there is no magic formula that can address every issue.Several algorithms balance cost and performance. A banking application needs robust security at a high cost, while a gaming software that sends user patterns for analytics cares more about speed and cost. Thus, choosing the appropriate encryption technique for the user. This study offers important insights in the process of selecting cryptographic algorithms in terms of each algorithm's strengths, weaknesses, cost, and performance . In order to demonstrate an entire performance analysis in this article, as opposed to just theoretical comparisons, this research developed and thoroughly examined the cost and performance of commonly used cryptographic algorithms, including DES, 3DES, AES, RSA, and blowfish. According to the findings, blowfish requires the smallest amount of time to decrypt files of various sizes (25K, 50K, 1M, 2M, 3M, and 4M), and it also consumes the smallest amount of memory. This makes it approximately three times faster than other cryptographic algorithms. Index Terms— Cryptograph Algorithm, Security attacks , Key, Cipher.

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Load Balancing and Detection of Distributed Denial of Service Attacks Using Entropy Detection

Cited by 2 publications

References 12 publications

Feature-based real-time distributed denial of service detection in SDN using machine learning and Spark

Feature-based real-time distributed denial of service detection in SDN using machine learning and Spark

In-Depth Assessment of Cryptographic Algorithms Namely DES, 3DES, AES, RSA, and Blowfish

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