@inproceedings{SisLab2361,
       booktitle = {2016 3rd National Foundation for Science and Technology Development (NAFOSTED) Conference on Information and Computer Science (NICS)},
           title = {POCAD: a Novel Payload-based One-Class Classifier for Anomaly Detection},
          author = {Xuan Nam Nguyen and Dai Tho Nguyen and Hai Long Vu},
            year = {2016},
           pages = {74--79},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2361/},
        abstract = {In this paper, we propose a novel Payload-based One-class Classifier for Anomaly Detection called POCAD, which combines a generalized 2v-gram feature extractor and a one-class SVM classifier to effectively detect network intrusion attacks. We extensively evaluate POCAD with real-world datasets of HTTP-based attacks. Our experiment results show that POCAD can quickly detect malicious payload and achieves a high detection rate as well as a low false positive rate. The experiment results also show that POCAD outperforms state of the art payload-based detection schemes such as McPAD [8] and PAYL [5].}
}