DT-MSOF Strategy and its Application to Reduce the Number of Operations in AHP

A computing strategy called Double Track"“Most Significant Operation First (DT-MSOF) is proposed. The goal of this strategy is to reduce computation time by reducing the number of operations that need to be executed, while maintaining a correct final result. Executions are conducted on a sequence of computing operations that have previously been sorted based on significance. Computation will only run until the result meets the needs of the user. In this study, the DT-MSOF strategy was used to modify the Analytic Hierarchy Process (AHP) algorithm into MD-AHP in order to reduce the number of operations that need to be done. The conventional AHP uses a run-to-completion approach, in which decisions can only be obtained after all of the operations have been completed. On the other hand, the calculations in MD-AHP are carried out iteratively only until the conditions are reached where a decision can be made. The simulation results show that MD-AHP can reduce the number of operations that need to be done to obtain the same results (decisions) as obtained by conventional AHP. It was also found that the more uneven the distribution of priority values, the more the number of operations could be reduced. 

Improving DDoS Detection Accuracy Using Six-Sigma in SDN Environment

This paper proposes the new method for improving the accuracy of detection of DDoS attacks on the SDN by utilizing control plane using Six-Sigma method. Software-Defined Networking (SDN) is a centralized network control system. This system offers flexibility on receiving, processing and forwarding packets between subnetworks. The centralized system of SDN, which separates control plane and data plan, has an immense number of advantages, but it also has the risk of becoming a single point of network failure. Distributed Denial of Service (DDoS) attack is the major issues faced in the security aspect of SDN. This attack can make network resources unreachable by the real packets. The widely known method has been implemented on SDN for avoiding a DDoS attack is Three-Sigma method. Three-Sigma method uses a threshold value to determine the existence of a DDoS attack. However, this method has drawbacks regarding accuracy in determining the DDoS attack. The main contribution of this paper is utilizing central control plane of SDN for improving accuracy on detecting the DDoS attack. Several experiments performed for proving the concept. The result shows the new method can improve the accuracy of detection of a DDoS attack, either in constant or fluctuating traffic, by reducing the false positive. The performance is about 50% more accurate than the previous method

Pelatihan Berpikir Komputasional untuk Peningkatan Kompetensi Guru Telkom Schools sebagai Bagian dari Gerakan PANDAI

Berpikir komputasional (BK) atau computational thinking (CT) merupakan salah satu keahlian esensial yang diperlukan sumber daya manusia Indonesia dalam rangka menghadapi revolusi industri 4.0 dan masyarakat 5.0. Gerakan PANDAI (Pengajar Era Digital Indonesia) merupakan suatu gerakan nasional yang merupakan kolaborasi nirlaba antara komunitas Bebras Indonesia, Kementerian Pendidikan dan Kebudayaan Indonesia, dan Google Indonesia dalam rangka meningkatkan kompetensi BK yang dimiliki oleh guru sekolah dasar dan menengah. Pada tahun 2022, Biro Bebras Universitas Telkom mengadakan pelatihan BK kepada lebih dari 60 guru Telkom Schools sebagai bagian dari gerakan ini. Pelatihan ini terdiri dari lima tahapan besar yang meliputi lokakarya luring, pembelajaran mandiri, lokakarya daring, dan dua kegiatan microteaching. Hasil analisis kuantitatif menunjukkan peningkatan kemampuan konseptual peserta terkait BK, meskipun masih banyak hal yang perlu dibenahi dari sisi kemampuan teknis dalam pengerjaan soal-soal BK

Improving Distributed Denial of Service (DDOS) Detection using Entropy Method in Software Defined Network (SDN)

This research proposed a new method to enhance Distributed Denial of Service (DDoS) detection attack on Software Defined Network (SDN) environment. This research utilized the OpenFlow controller of SDN for DDoS attack detection using modified method and regarding entropy value. The new method would check whether the traffic was a normal traffic or DDoS attack by measuring the randomness of the packets. This method consisted of two steps, detecting attack and checking the entropy. The result shows that the new method can reduce false positive when there is a temporary and sudden increase in normal traffic. The new method succeeds in not detecting this as a DDoS attack. Compared to previous methods, this proposed method can enhance DDoS attack detection on SDN environment