Machine Learning Model for Multiclass Lesion Diagnoses

Ismail, Karim E.; AbouRizka, Mohamed A.; Maghraby, Fahima A.

doi:10.1109/niles50944.2020.9257976

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…The study to confirm the presence or absence of intrusion prevention was conducted by introducing three well-known classification techniques, MLP, NB and Random Forest, among which MLP detected invasion with the highest accuracy [42]. Random Forest is a machine learning classifier made up of a number of decision trees that operate as a group, where the most voted prediction is accepted [43]. While the Multi Class Classifier and Random Forest algorithms detected 100% of all web-based attacks, the Naïve Bayes and Naïve Bayes Updatable algorithms detected only HTTP Flood among the four attacks, and a 96% rate was detected [44].…”

Section: Machine Learningmentioning

confidence: 99%

Efficient Autonomous Defense System Using Machine Learning on Edge Device

Cho¹

2022

Computers, Materials &Amp; Continua

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As a large amount of data needs to be processed and speed needs to be improved, edge computing with ultra-low latency and ultra-connectivity is emerging as a new paradigm. These changes can lead to new cyber risks, and should therefore be considered for a security threat model. To this end, we constructed an edge system to study security in two directions, hardware and software. First, on the hardware side, we want to autonomically defend against hardware attacks such as side channel attacks by configuring field programmable gate array (FPGA) which is suitable for edge computing and identifying communication status to control the communication method according to priority. In addition, on the software side, data collected on the server performs end-to-end encryption via symmetric encryption keys. Also, we modeled autonomous defense systems on the server by using machine learning which targets to incoming and outgoing logs. Server log utilizes existing intrusion detection datasets that should be used in real-world environments. Server log was used to detect intrusion early by modeling an intrusion prevention system to identify behaviors that violate security policy, and to utilize the existing intrusion detection data set that should be used in a real environment. Through this, we designed an efficient autonomous defense system that can provide a stable system by detecting abnormal signals from the device and converting them to an effective method to control edge computing, and to detect and control abnormal intrusions on the server side.

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Section: Machine Learningmentioning

confidence: 99%

Efficient Autonomous Defense System Using Machine Learning on Edge Device

Cho¹

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

Machine Learning Model for Multiclass Lesion Diagnoses

Cited by 1 publication

References 23 publications

Efficient Autonomous Defense System Using Machine Learning on Edge Device

Efficient Autonomous Defense System Using Machine Learning on Edge Device

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