Multitask Learning for Network Traffic Classification

Traffic classification has various applications in today's Internet, from resource allocation, billing and QoS purposes in ISPs to firewall and malware detection in clients. Classical machine learning algorithms and deep learning models have been widely used to solve the traffic classification task. However, training such models requires a large amount of labeled data. Labeling data is often the most difficult and time-consuming process in building a classifier. To solve this challenge, we reformulate the traffic classification into a multi-task learning framework where bandwidth requirement and duration of a flow are predicted along with the traffic class. The motivation of this approach is twofold: First, bandwidth requirement and duration are useful in many applications, including routing, resource allocation, and QoS provisioning. Second, these two values can be obtained from each flow easily without the need for human labeling or capturing flows in a controlled and isolated environment. We show that with a large amount of easily obtainable data samples for bandwidth and duration prediction tasks, and only a few data samples for the traffic classification task, one can achieve high accuracy. We conduct two experiment with ISCX and QUIC public datasets and show the efficacy of our approach

Pemodelan Proxy Anonim Menggunakan Algoritme Expectation Maximization Dengan Data Balancing

Penggunaan internet untuk mengakses situs-situs tertentu yang tidak berhubungan dengan pekerjaan dibatasi akses nya oleh perusahaan atau organisasi. Perusahaan atau organisasi melakukan pemblokiran untuk tujuan mengamankan jaringan mereka terhadap ancaman virus, spyware, hacker dan ancaman lainnya yang dapat merugikan perusahaan dengan cara menerapkan firewall, filter URL serta sistem deteksi intrusi. Namun, pengamanan tersebut masih dapat ditembus dengan menggunakan layanan proxy anonim. Penggunaan proxy anonim memungkinkan user untuk melakukan bypass sebagian besar sistem penyaringan. Dalam penelitian ini, data proxy anonim diperoleh dengan cara menangkap (capture) paket data menggunakan aplikasi wireshark. Data tersebut dimodelkan dengan algoritme expectation maximization sehingga diperoleh akurasi model sebesar 71.22% pada pembagian data yang seimbang. Hasil ini menunjukkan bahwa model mampu mengenali penggunaan proxy anonim pada traffic internet