Skip ke Konten
Review Penelitian Selesai Diterima (Accept)

Proposal Penelitian: Implementasi Machine Learning untuk Deteksi Cyber Attack pada IoT Networks

Review proposal penelitian machine learning untuk cybersecurity IoT

Penulis: Dr. Rina Kartika, S.T., M.T., Dr. Ahmad Prakoso, S.Kom., M.Kom.

Afiliasi: Universitas Indonesia

Negara: Indonesia

Tanggal Review: 30 November 2024

Publikasi: BRIN - Fundamental Research Grant

Putaran: Round 1

Proposal penelitian ini mengembangkan framework machine learning untuk deteksi cyber attack pada IoT networks dengan pendekatan federated learning dan edge computing. Penelitian akan mengumpulkan data dari berbagai jenis IoT devices dan mengembangkan adaptive ML models.

Metodologi mencakup data collection dari smart home, industrial IoT, dan healthcare IoT systems. Model akan diuji pada real-world scenarios dengan fokus pada real-time detection dan minimal false positives.

Area Fokus Review

Research novelty, methodology soundness, feasibility, potential impact, team capability

Evaluasi
Kelebihan

✓ Excellent Research Proposal

1. Research Novelty

Proposal menawarkan novel approach dengan kombinasi:

  • Edge computing untuk real-time processing
  • Federated learning untuk privacy preservation
  • Multi-modal data fusion (network traffic + device behavior)
  • Adaptive ML models untuk berbagai jenis IoT devices
2. Methodology Excellence

Metodologi yang comprehensive dan sound:

Phase Activities Timeline
Phase 1 Data collection & preprocessing Months 1-4
Phase 2 Model development & training Months 5-12
Phase 3 Testing & validation Months 13-18
Phase 4 Deployment & evaluation Months 19-24
3. Team Capability

Tim peneliti dengan proven track record:

  • 15+ publications in cybersecurity and ML
  • 3 patents in IoT security
  • Previous collaboration with industry partners
  • Experience in funded research projects
4. Expected Impact
  • Scientific: Advance knowledge in IoT cybersecurity
  • Practical: Protect critical IoT infrastructure
  • Economic: Reduce cyber attack costs for industry
  • Social: Enhance cybersecurity awareness
Kelemahan

⚠ Minor Concerns:

  1. Data Availability: Access to real IoT network data perlu lebih detail
  2. Scalability: Testing pada large-scale networks perlu diperjelas

Note: These are not critical issues and can be addressed during implementation.

Rekomendasi

✓ STRONGLY RECOMMENDED FOR FUNDING

Proposal penelitian yang excellent dengan novelty tinggi dan metodologi yang sound. Tim peneliti kompeten dengan track record yang baik.

Minor clarifications needed:

  • Detail data acquisition strategy
  • Scalability testing plan
  • Risk mitigation for data privacy

Recommendation: APPROVE FUNDING

Catatan Reviewer

FUNDING RECOMMENDATION: APPROVED

Proposal ini memiliki semua elemen yang dibutuhkan untuk penelitian fundamental berkualitas tinggi. Kombinasi machine learning dan IoT cybersecurity sangat relevan dengan tantangan saat ini.

Expected Outcomes:

  • Scientific Contribution: Novel ML framework untuk IoT security
  • Publications: Target 8+ papers in Q1/Q2 journals
  • Technology: Open-source security toolkit
  • Industry Impact: Partnership dengan 3+ companies
  • Human Resources: Training 5 PhD students

Funding Priority: HIGH

Budget: Rp 2.5 billion (24 months)

Informasi Review
  • Kategori

    Review Proposal Penelitian

  • Jenis Review

    Review Penelitian

  • Level

    Nasional

  • Metode Review

    Double Blind

  • Tanggal Submit

    1 Nov 2024

  • Tanggal Selesai

    30 Nov 2024

  • Durasi Review

    29 hari

Penerbit/Institusi
Badan Riset dan Inovasi Nasional (BRIN)

Lembaga Pendanaan

Indonesia

Kunjungi Website
Beban Kerja
  • Skor Review

    8.5/10

  • Waktu Review

    9.0 jam

Kata Kunci

machine learning IoT security cyber attack detection federated learning edge computing

Aktivitas Terkait