Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection

Muhammad, Arif Wirawan; Foozy, Cik Feresa Mohd; Azhari, Ahmad

doi:10.29099/ijair.v4i1.156

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Although there are many algorithm choices available in the artificial neural network training process [19] [20], this research uses Quasi-Newtonian (matlab: trainlm) algorithm because the Quasi-Newtonian algorithm can produce an optimal artificial neural network learning process and faster to achieve generalization of output values compared to training algorithms such as Scaled-Conjugate or Resilient-Propagation [21]. The parameters of the artificial neural network (ANN) training process are presented in Table 3.…”

Section: Resultsmentioning

confidence: 99%

Human Intestinal Condition Identification based-on Blended Spatial and Morphological Feature using Artificial Neural Network Classifier

Athiyah¹,

Muhammad

Azhari

2020

KEDS

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I. Introduction Colorectal cancer is a type of cancer that attacks the intestinal walls cell of humans. Wisconsin Reporting System (WRS), states that the type of colorectal cancer is the third highest cause of death after types of lung cancer and breast cancer, with a case of death of 9.5% of the total world population [1]. In Indonesia, colorectal cancer itself is ranked as the third cause of death after breast cancer and cervical cancer. Therefore, the abnormalities in the human intestinal wall need to be identified early to minimize the growth of cancerous cells that are more virulent, which can cause death. Health screening, such as endoscopy, is a simple step to detect abnormal growths in human intestinal cells wall [2][3]. Also, the process of early detection plays an essential role in health practitioners/gynecologists to determine the prognosis and type of treatment that patients must receive [4]. The right prognosis is accompanied by the right dosage of the drug to help speed the recovery of patients from colorectal cancer outbreaks. Endoscopic screening technique is a common step carried out by the health experts/gynecologist to determine the condition of the human intestine by inserting a camera through the rectum to get an intestine picture [5]. The result of endoscopic screening is a digital image of the area around the intestinal wall [6]. During this time, the interpretation of endoscopic images is carried out manually or taken by the naked eye, thus requires quite a long time to interpret and produce results. This ARTICLE INFO

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Section: Resultsmentioning

confidence: 99%

Human Intestinal Condition Identification based-on Blended Spatial and Morphological Feature using Artificial Neural Network Classifier

Athiyah¹,

Muhammad

Azhari

2020

KEDS

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“…For features selection, we use Information Gain technique where the amount of mutual information gained from a combination of monitored variables [56]. With respect to ML, Information Gain method is beneficial for selecting various remarkable features based on theories that measure the significance of information concluded from a specific feature.…”

Section: B Featuresmentioning

confidence: 99%

Gradient Monitored Reinforcement Learning for Jamming Attack Detection in FANETs

Ghelani,

Gharia,

El-Ocla

2024

IEEE Access

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Unmanned Aerial Vehicles (UAVs) have several military and civilian applications to perform tasks that do not require a central processing unit or human involvement. There are several vulnerable characteristics, alternatively limitations, in UAV systems such as data loss, signals interference, disabling sensors, misleading weapons, cyber attacks, disrupting services, etc. Jamming attack is one of the cyber threats that likely lead to denial of service that often occurs in wireless communication systems like Flying ad hoc networks (FANETs) and Internet of Drones (IoD). Over years, there are several approaches proposed by researchers to detect jamming attacks such as rule-based jamming attack detection mechanism, Bayesian game-theoretic mechanism, IoD-based protection mechanism, communication channel techniques (channel hopping, spectrum spreading, MIMO-based jamming mitigation, coding, etc), delay tolerant networking technique, and cryptographic algorithms, however, these methods were not suitable for jamming detection in UAV environment. The major challenges are on the delivery efficiency, processing time, accuracy, energy consumption, flight distance, and flight autonomy. In this paper, we introduce a method to detect the Jamming Attack using Reinforcement Learning-based Gradient Monitored (RLGM) mechanism. RLGM maintains safe regions and reduces gradient variance for intended training and this provides a better accuracy of the learning goal. In addition, RLGM achieves prompt training progress and selects preciously the series of parameters required by the network during the training phase. RLGM produces spontaneous derivation of the essential deep network scale over the training process drawing on automatically unvarying trained weights. Our proposed approach outperforms other reinforcement learning methods such as Federated RL, Deep Q Learning (DQL), and GA-AOMDV.

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Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection

Cited by 2 publications

References 13 publications

Human Intestinal Condition Identification based-on Blended Spatial and Morphological Feature using Artificial Neural Network Classifier

Human Intestinal Condition Identification based-on Blended Spatial and Morphological Feature using Artificial Neural Network Classifier

Gradient Monitored Reinforcement Learning for Jamming Attack Detection in FANETs

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