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Rifqy Hakimi

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Author-ID : 2891768

Control & Systems Engineering Electrical & Electronics Engineering Engineering Library & Information Science Social Sciences

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1

Studi Isu Keamanan Jaringan Pada Facebook Rifqy Hakimi
Jurnal Pustakawan Indonesia Vol. 11 No. 2 (2011): Jurnal Pustakawan Indonesia
Publisher : Perpustakaan IPB

Facebook merupakan salah satu media jejaring sosial yang terlaris di Indonesia bahkan di dunia. Jejaring sosial ini digunakan untuk berinteraksi dengan relasi, teman, berbagi foto, dan bahkan untuk mengembangkan bisnis. Pada paper penelitian ini akan dibahas isu keamanan yang bisa mengancam pada Facebook. Serangan yang bisa mengancam keamanan data pribadi pada Facebook antara lain phising, clickjacking, dan link scam. Dalam paper ini akan dianalisis cara kerja serangan dan penanganan serta pencegahan yang dapat dilakukan terhadap serangan ini.Kata Kunci : Facebook, phising, clickjacking, link scam

Studi Isu Keamanan Jaringan Pada Facebook Rifqy Hakimi
Jurnal Pustakawan Indonesia Vol. 11 No. 2 (2011): Jurnal Pustakawan Indonesia
Publisher : Perpustakaan IPB

Facebook merupakan salah satu media jejaring sosial yang terlaris di Indonesia bahkan di dunia. Jejaring sosial ini digunakan untuk berinteraksi dengan relasi, teman, berbagi foto, dan bahkan untuk mengembangkan bisnis. Pada paper penelitian ini akan dibahas isu keamanan yang bisa mengancam pada Facebook. Serangan yang bisa mengancam keamanan data pribadi pada Facebook antara lain phising, clickjacking, dan link scam. Dalam paper ini akan dianalisis cara kerja serangan dan penanganan serta pencegahan yang dapat dilakukan terhadap serangan ini.Kata Kunci : Facebook, phising, clickjacking, link scam

Hardware-Constrained Feature-Slicing Ensemble for Explainable DDoS Detection in Software-Defined Networks Rifqy Hakimi; Wervyan Shalannanda; Heriansyah
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 2 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n2.3196

Software-Defined Networking (SDN) provides centralised network management but introduces a critical structural vulnerability because the controller is highly susceptible to Distributed Denial of Service (DDoS) attacks. While Machine Learning is widely utilised for Intrusion Detection Systems, traditional monolithic models often operate as opaque black boxes, rely on easily spoofed categorical features, and ignore the severe computational latency limits of centralised controllers. This paper proposes a novel, hardware-constrained Feature-Slicing Ensemble architecture for DDoS detection. We partition network data into two domain-specific subsets, namely Header Features and Flow Statistics, while deliberately excluding evasion-prone identifiers to prevent data leakage. Specialised, depth-constrained Random Forest base learners are trained on each subset to simulate controller CPU limitations, with predictions aggregated using a soft-voting mechanism. Evaluated on the InSDN dataset using 5-fold stratified cross-validation, our proposed model achieved an F1-Score of 0.9954. While maintaining strict statistical parity with unconstrained monolithic baselines, the decoupled architecture provides critical explainability, allowing network administrators to isolate structural anomalies from volumetric floods. This demonstrates that logical feature partitioning improves model modularity and real-world evasion resilience without sacrificing predictive precision.

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