The source code for the paper Fostering Cyber Threat Detection Through Federated Learning.
Despite achieving good performance and wide adoption, machine learning based security detection models (e.g., malware classifiers) are subject to concept drift and evasive evolution of attackers, which renders up-to-date threat data as a necessity. However, due to enforcement of various privacy protection regulations (e.g., GDPR), it is becoming increasingly challenging or even prohibitive for security vendors to collect individual-relevant and privacy-sensitive threat datasets, e.g., SMS spam/non-spam messages from mobile devices. To address such obstacles, this study systematically profiles the (in)feasibility of federated learning for privacy-preserving cyber threat detection in terms of effectiveness, byzantine resilience, and efficiency. This is made possible by the build-up of multiple threat datasets and threat detection models, and more importantly, the design of realistic and security-specific experiments.
We evaluate FL on two representative threat detection tasks, namely SMS spam detection and Android malware detection. It shows that FL-trained detection models can achieve a performance that is comparable to centrally trained counterparts. Also, most non-IID data distributions have either minor or negligible impact on the model performance, while a label-based non-IID distribution of a high extent can incur non-negligible fluctuation and delay in FL training. Then, under a realistic threat model, FL turns out to be adversary-resistant to attacks of both data poisoning and model poisoning. Particularly, the attacking impact of a practical data poisoning attack is no more than 0.14% loss in model accuracy. Regarding FL efficiency, a bootstrapping strategy turns out to be effective to mitigate the training delay as observed in label-based non-IID scenarios.
The datasets in our work are all open source as described in our Project site.
| Datasets | Spam | Non-Spam | Languages | Period |
|---|---|---|---|---|
| UCI | 747 | 4827 | English | 2012 |
| ExAIS | 2,350 | 2,890 | English | 2015 |
| SpamHunter | 23,249 | 0 | Multilingual | 2018-2022 |
| 0 | 18,629 | Multilingual | 2018-2022 | |
| Total | 26,346 | 26,346 | Multilingual | 2012-2022 |
| Datasets | Malware | Benign | Period |
|---|---|---|---|
| Drebin | 4,255 | 0 | 2010-2012 |
| CIC-AndMal2017 | 0 | 1,645 | 2015-2017 |
| Androzoo | 0 | 2,610 | 2022 |
| Total | 4,255 | 4,255 | 2010-2022 |
Our work evaluates FL on two threat detection tasks: SMS spam detection and Android malware detection. And the experiment code can be easily extended to other binary classification tasks.
To try this code, you need to 1. get your dataset ready 2. preprocess your data 3. try the central or federated learning. More READMEs can be found in subfolders.
Now choose a threat detection task SMS spam detection or Android malware detection to get started.
.
├── AndroidMalware
│ ├── central_learning
│ ├── data
│ ├── data_preprocessing
│ ├── federated_learning
│ └── requirements.txt
├── SMSSpam
│ ├── central_learning
│ ├── data
│ ├── data_preprocessing
│ ├── federated_learning
│ └── requirments.txt
└── strategy