7 research outputs found
Pendekatan unsupervised untuk Mendeteksi Serangan Tingkat Rendah pada Jaringan Komputer
Serangan tingkat rendah merupakan serangan yang diam-diam masuk ke dalam system tanpa mengirimkan paket-paket dalam jumlah besar. Contoh dari serangan jenis ini adalah exploit, backdoors, dan worms. Untuk mencegah serangan jenis ini, kami mengusulkan system deteksi intrusi dengan menggunakan Recurrent Neural Network dan Autoencoders.Pendekatan unsupervised yang diusulkan mampu mengidentifikasi serangan tingkat rendah dalam koneksi jaringan, mengesampingkan persyaratan untuk menyediakan sampel berbahaya untuk data pelatihan. Pendekatan yang diusulkan memberikan peningkatan detection rate setidaknya 12,04% dari penelitian sebelumnya
Darknet Traffic Analysis A Systematic Literature Review
The primary objective of an anonymity tool is to protect the anonymity of its
users through the implementation of strong encryption and obfuscation
techniques. As a result, it becomes very difficult to monitor and identify
users activities on these networks. Moreover, such systems have strong
defensive mechanisms to protect users against potential risks, including the
extraction of traffic characteristics and website fingerprinting. However, the
strong anonymity feature also functions as a refuge for those involved in
illicit activities who aim to avoid being traced on the network. As a result, a
substantial body of research has been undertaken to examine and classify
encrypted traffic using machine learning techniques. This paper presents a
comprehensive examination of the existing approaches utilized for the
categorization of anonymous traffic as well as encrypted network traffic inside
the darknet. Also, this paper presents a comprehensive analysis of methods of
darknet traffic using machine learning techniques to monitor and identify the
traffic attacks inside the darknet.Comment: 35 Pages, 13 Figure