An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering

Hu, Zhengbing; Bodyanskiy, Yevgeniy; Tyshchenko, Oleksii K.; Boiko, Olena

doi:10.5815/ijmecs.2016.05.02

Cited by 12 publications

(2 citation statements)

References 20 publications

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“…Hu et al proposed a new method for data stream clustering with the help of adaptive neuro-fuzzy system integration. He found that the proposed ensemble is formed by adaptive neuro-fuzzy self-organizing feature maps in the parallel processing mode, and the final result is selected by the best neuro-fuzzy self-organizing feature maps [6]. Denial of service is also one of the most pressing security threats to in-vehicle network systems.…”

Section: Related Workmentioning

confidence: 99%

Application of Computer Data Mining Technology Based on AKN Algorithm in Denial of Service Attack Defense Detection

Huang¹

2022

Wireless Communications and Mobile Computing

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Denial of service attacks have become one of the most difficult network security problems because they are easy to implement, difficult to prevent, and difficult to track, and they have brought great harm to the network society. Denial of service (Dos) is a phenomenon in which a large number of useless data packets or obstructive content are maliciously transmitted to the target server, which makes the target server unable to provide users with normal services. Denial of service attack (Dos attack) is a very typical network attack method, and the main harm of Dos attack is to exhaust service resources, making the computer or network unable to provide normal services. And AKN (adaptive Kohonen network) is an adaptive neural network proposed in recent years, and an algorithm summarized by using the characteristics of the neural network is called the AKN algorithm. This algorithm can realize fast, low-consumption, and high-precision denial of service detection in complex networks. In the era of big data, network security is becoming more and more important, and in order to maintain the security of network data, this article studies the common forms and principles of Dos attacks, as well as the current corresponding defense detection methods. It also investigates several commonly used algorithms of computer data mining technology, such as clustering algorithms, classification algorithms, neural network algorithms, regression algorithms, website data mining, and association algorithms, and proposes a computer data mining model based on the AKN algorithm. In addition, the computer data mining technology based on the AKN algorithm is used to conduct defensive detection experiments under Dos attacks and compares with classic algorithms. Experimental results show that experiments based on the AKN algorithm have better defensive detection effects than classic algorithms, with a detection accuracy rate of more than 97% and a detection efficiency improvement of more than 20%.

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Section: Related Workmentioning

confidence: 99%

Application of Computer Data Mining Technology Based on AKN Algorithm in Denial of Service Attack Defense Detection

Huang¹

2022

Wireless Communications and Mobile Computing

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“…Terdapat beberapa penelitian terdahulu yang membahas mengenai pemanfaatan jaringan Kohonen. Penelitian [4] dan [5] memanfaatkan jaringan Kohonen neuro-fuzzy untuk klusterisasi stream data. Jaringan Kohonen dan Support Vector Machine dapat juga digunakan untuk pendeteksian intrusi [6].…”

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Kohonen Network Modeling for Asteroid Name Recognition

Perkasa¹,

Wijaya²,

Jamhari³

2020

JST

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Sebagian kecil asteroid sudah memiliki nama permanennya masing-masing. Akan tetapi, pelacakan nama asteroid yang sudah digunakan sebelumnya merupakan pekerjaan yang tidak praktis karena terdapat ribuan nama yang harus ditelusuri satu persatu. Penelitian ini bermaksud untuk meminimalisir beban penelusuran banyak data nama asteroid menggunakan jaringan Kohonen. Dengan menggunakan jaringan Kohonen, cukup dilakukan pelatihan terhadap data sampel yang diberikan yang jumlahnya jauh lebih sedikit dibandingkan data sesungguhnya. Hasil dari pelatihan ini selanjutnya digunakan untuk mendapatkan jumlah nama asteroid yang berhasil dikenali oleh jaringan Kohonen. Hasil tersebut juga dapat digunakan untuk pengusul nama asteroid baru sehingga nama yang diusulkan dapat ditentukan status penerimaannya. Berdasarkan hasil pelatihan terhadap data sampel, didapat hasil pelatihan yang semakin membaik seiring meningkatnya tingkat learning rate. Kendati demikian, ketika diuji dengan data sesungguhnya, diperoleh hasil keseluruhan yang belum memuaskan karena tingkat pengenalannya hanya sebesar 49,78%. Dari hasil pengujian juga didapat bahwa tidak ada hubungan linear antara tingkat learning rate dan jumlah nama yang berhasil dikenali. Penelitian selanjutnya yang dapat dilakukan adalah penyertaan data latih non-asteroid, pengubahan parameter jaringan Kohonen, atau penggunaan metode pengenalan lainnya. Kata kunci-Jaringan Kohonen, pengenalan string, pengenalan nama, asteroid

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A Data Model of the Internet Social Environment

Peleshchyshyn

Mastykash

2019

Advances in Artificial Systems for Medicine and Education II

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An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering

Cited by 12 publications

References 20 publications

Application of Computer Data Mining Technology Based on AKN Algorithm in Denial of Service Attack Defense Detection

Application of Computer Data Mining Technology Based on AKN Algorithm in Denial of Service Attack Defense Detection

Kohonen Network Modeling for Asteroid Name Recognition

A Data Model of the Internet Social Environment

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