A Honeypot with Machine Learning based Detection Framework for defending IoT based Botnet DDoS Attacks
| Title | A Honeypot with Machine Learning based Detection Framework for defending IoT based Botnet DDoS Attacks |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Vishwakarma, Ruchi, Jain, Ankit Kumar |
| Conference Name | 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) |
| Date Published | apr |
| Keywords | composability, Computer crime, computer network security, Data models, DDoS attack detection, detection framework, honey pots, honeypot-based approach, Human Behavior, human factors, Internet of Things, invasive software, IoT botnet DDoS attacks, IoT Botnets, IoT honeypot, IoT Honeypots, IoT malware, IoT security, learning (artificial intelligence), machine learning, machine learning model, machine learning techniques, Malware, malware detection, Metrics, Network security, Protocols, pubcrawl, Resiliency, Scalability, Training, Zero-day attacks, Zero-Day DDoS Attack, zero-day DDoS attacks |
| Abstract | With the tremendous growth of IoT botnet DDoS attacks in recent years, IoT security has now become one of the most concerned topics in the field of network security. A lot of security approaches have been proposed in the area, but they still lack in terms of dealing with newer emerging variants of IoT malware, known as Zero-Day Attacks. In this paper, we present a honeypot-based approach which uses machine learning techniques for malware detection. The IoT honeypot generated data is used as a dataset for the effective and dynamic training of a machine learning model. The approach can be taken as a productive outset towards combatting Zero-Day DDoS Attacks which now has emerged as an open challenge in defending IoT against DDoS Attacks. |
| DOI | 10.1109/ICOEI.2019.8862720 |
| Citation Key | vishwakarma_honeypot_2019 |
- Protocols
- learning (artificial intelligence)
- machine learning
- machine learning model
- machine learning techniques
- malware
- malware detection
- Metrics
- network security
- IoT security
- pubcrawl
- Resiliency
- Scalability
- Training
- Zero-day attacks
- Zero-Day DDoS Attack
- zero-day DDoS attacks
- Human Factors
- Computer crime
- computer network security
- Data models
- DDoS attack detection
- detection framework
- honey pots
- honeypot-based approach
- Human behavior
- composability
- Internet of Things
- invasive software
- IoT botnet DDoS attacks
- IoT Botnets
- IoT honeypot
- IoT Honeypots
- IoT malware