Visualisasi Serangan Remote to Local (R2L) Dengan Clustering K-means

Visualisasi merupakan salah satu teknik untuk meningkatkan akurasi deteksi serangan yang terjadi di jaringan. Visulisasi bertujuan untuk mempermudah dalam mengenali dan menyimpulkan serangan terjadi. Clustering k-means dapat digunakan untuk mendeteksi paket serngan dan paket normal. Serangan remote to local adalah serngan yang dilakukan oleh attacker untuk mendapatkan akses akun ke sebuah sistem yang sebelumnya tidak memiliki akun ke sistem tersebut. Pola serangan R2L pada dataset DARPA dapat dikenali dengan beberapa paramer seperti source address, destination address, flags, ip length, dan tcp length

Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)

There are several ways to increase detection accuracy result on the intrusion detection systems (IDS), one way is feature extraction. The existing original features are filtered and then converted into features with lower dimension. This paper uses the Principal Components Analysis (PCA) for features extraction on intrusion detection system with the aim to improve the accuracy and precision of the detection. The impact of features extraction to attack detection was examined. Experiments on a network traffic dataset created from an Internet of Thing (IoT) testbed network topology were conducted and the results show that the accuracy of the detection reaches 100 percent

Enhanced Deep Learning Intrusion Detection in IoT Heterogeneous Network with Feature Extraction

Heterogeneous network is one of the challenges that must be overcome in Internet of Thing Intrusion Detection System (IoT IDS). The difficulty of the IDS significantly is caused by various devices, protocols, and services, that make the network becomes complex and difficult to monitor. Deep learning is one algorithm for classifying data with high accuracy. This research work incorporated Deep Learning into IDS for IoT heterogeneous networks. There are two concerns on IDS with deep learning in heterogeneous IoT networks, i.e.: limited resources and excessive training time. Thus, this paper uses Principle Component Analysis (PCA) as features extraction method to deal with data dimensions so that resource usage and training time will be significantly reduced. The results of the evaluation show that PCA was successful reducing resource usage with less training time of the proposed IDS with deep learning in heterogeneous networks environment. Experiment results show the proposed IDS achieve overall accuracy above 99%

Tingkat Kesuksesan E-Learning Edmodo Sebagai Sistem Pembelajaran Online Selama Pandemi Covid 19 Adopsi Model DeLone&Mclean

Edmodo merupakan platform pembelajaran online yang saat ini termasuk banyak digunakan di perguruan tinggi di Indonesia selama pandemic Covid19 salah satunya di Univeristas Dinamika Bangsa (UNAMA) Jambi. Dalam penelitian ini dilakukan pengevaluasian terhadapa kualitas kesuksesan LMS Edmodo pada platform-platform edmodo tersebut dengan mengadopsi model Delone And Mclean dengan 6 variabel yaitu Information Quality, System Quality, Service Quality, Use, User Statisfaction dan Net Benefit. Untuk data analisis menggunak Structural Equation Mode (SEM). Responden di penelitian ini adalah para dosen dan mahasiswa di UNAMA Jambi yang sebagai pengguna edmodo. Adapun tujuan penelitian ini untuk membuktikan sejauh mana kesuksesan yang penerapan Edmodo di universitas dinamika bangsa jambi. Responden pada penelitian ini sebanyak 166 responden. Data dikumpulkan dengan cara metode survey. Hasil dari penelitian ini menunjukkan nilai R2 dengan variabel information quality dan system quality memiliki nilai 0.339 dikategorikan tingkat moderat/sedang. Artinya kedua variabel dependen memberikan pengaruh dan tinkat moderat/sedang terhadap variabel dependen. Untuk R2 variabel independent use dan user satisfaction memiliki substansial/kuat dengan nilai 0.707, artinya kedua variabel independen memberikan pengaruh dan tingkat Substansial/kuat terhadap variabel depende

Features extraction on iot intrusion detection system using principal components analysis (PCA)

Feature extraction solves the problem of finding the most efficient and comprehensive set of features. A Principle Component Analysis (PCA) feature extraction algorithm is applied to optimize the effectiveness of feature extraction to build an effective intrusion detection method. This paper uses the Principal Components Analysis (PCA) for features extraction on intrusion detection system with the aim to improve the accuracy and precision of the detection. The impact of features extraction to attack detection was examined. Experiments on a network traffic dataset created from an Internet of Thing (IoT) testbed network topology were conducted and the results show that the accuracy of the detection reaches 100 percent

Enhanced deep learning intrusion detection in IoT heterogeneous network with feature extraction

Heterogeneous network is one of the challenges that must be overcome in Internet of Thing Intrusion Detection System (IoT IDS). The difficulty of the IDS significantly is caused by various devices, protocols, and services, that make the network becomes complex and difficult to monitor. Deep learning is one algorithm for classifying data with high accuracy. This research work incorporated Deep Learning into IDS for IoT heterogeneous networks. There are two concerns on IDS with deep learning in heterogeneous IoT networks, i.e.: limited resources and excessive training time. Thus, this paper uses Principle Component Analysis (PCA) as features extraction method to deal with data dimensions so that resource usage and training time will be significantly reduced. The results of the evaluation show that PCA was successful reducing resource usage with less training time of the proposed IDS with deep learning in heterogeneous networks environment. Experiment results show the proposed IDS achieve overall accuracy above 99%