Botnets represent a serious threat to cybersecurity, enabling distributed denial-of-service attacks, malware dissemination, and phishing. This survey paper by Riya Parmar classifies detection techniques into four categories: signature-based, anomaly-based, DNS-based, and mining-based. The paper is available via Open Access on the Papers with Code platform.
Use Cases
- Compare signature-based and anomaly-based detection methods based on the survey's classification.
- Design new DNS-based botnet detection algorithms based on the discussed techniques.
- Benchmark mining-based detection approaches against other categories described in the paper.
Strengths
- Provides a structured classification of four major botnet detection technique families.
- The paper is available under an Open Access (green) license, facilitating distribution and use.
Limitations
- The description metadata is limited; actual data quality requires manual inspection after download.
- Row count and column-level documentation are absent; field semantics must be inferred after download.
Provenance
- Source
- Riya Parmar via Papers with Code
- Collection Method
- Survey paper compilation.