IDPS Signature Classification with a Reject Option and the Incorporation of Expert Knowledge

As the importance of intrusion detection and prevention systems (IDPSs) increases, great costs are incurred to manage the signatures that are generated by malicious communication pattern files. Experts in network security need to classify signatures by importance for an IDPS to work. We propose and evaluate a machine learning signature classification model with a reject option (RO) to reduce the cost of setting up an IDPS. To train the proposed model, it is essential to design features that are effective for signature classification. Experts classify signatures with predefined if-then rules. An if-then rule returns a label of low, medium, high, or unknown importance based on keyword matching of the elements in the signature. Therefore, we first design two types of features, symbolic features (SFs) and keyword features (KFs), which are used in keyword matching for the if-then rules. Next, we design web information and message features (WMFs) to capture the properties of signatures that do not match the if-then rules. The WMFs are extracted as term frequency-inverse document frequency (TF-IDF) features of the message text in the signatures. The features are obtained by web scraping from the referenced external attack identification systems described in the signature. Because failure needs to be minimized in the classification of IDPS signatures, as in the medical field, we consider introducing a RO in our proposed model. The effectiveness of the proposed classification model is evaluated in experiments with two real datasets composed of signatures labeled by experts: a dataset that can be classified with if-then rules and a dataset with elements that do not match an if-then rule. In the experiment, the proposed model is evaluated. In both cases, the combined SFs and WMFs performed better than the combined SFs and KFs. In addition, we also performed feature analysis.Comment: 9 pages, 5 figures, 3 table

Significant survival improvement of patients with recurrent breast cancer in the periods 2001-2008 vs. 1992-2000

<p>Abstract</p> <p>Background</p> <p>It is unclear whether individualized treatments based on biological factors have improved the prognosis of recurrent breast cancer. The purpose of this study is to evaluate the survival improvement of patients with recurrent breast cancer after the introduction of third generation aromatase inhibitors (AIs) and trastuzumab.</p> <p>Methods</p> <p>A total of 407 patients who received first diagnosis of recurrent breast cancer and treatment at National Kyushu Cancer Center between 1992 and 2008 were retrospectively evaluated. As AIs and trastuzumab were approved for clinical use in Japan in 2001, the patients were divided into two time cohorts depending on whether the cancer recurred before or after 2001. Cohort A: 170 patients who were diagnosed between 1992 and 2000. Cohort B: 237 patients who were diagnosed between 2001 and 2008. Tumor characteristics, treatments, and outcome were compared.</p> <p>Results</p> <p>Fourteen percent of cohort A and 76% of cohort B received AIs and/or trastuzumab (P < 0.001). The median overall survival (OS) times after breast cancer recurrence were 1.7 years and 4.2 years for these respective cohorts (P < 0.001). Both the time period and treatment of AIs and/or trastuzumab for recurrent disease were significant prognostic factors in multivariate analysis (cohort B vs. cohort A: HR = 0.70, P = 0.01; AIs and/or trastuzumab for recurrent disease: yes vs. no: HR = 0.46, P < 0.001). When patients were categorized into 4 subgroups by the expression of hormone receptor (HR) and HER-2 status, the median OS times of the HR-positive/HER-2-negative, HR-positive/HER-2-positive, HR-negative/HER-2-positive, and HR-negative/HER-2-negative subtypes were 2.2, 2.4, 1.6, and 1.0 years in cohort A and 4.5, 5.1, 5.0, and 1.4 years in cohort B.</p> <p>Conclusions</p> <p>The prognosis of patients with recurrent breast cancer was improved over time following the introduction of AIs and trastuzumab and the survival improvement was apparent in HR- and/or HER-2-positive tumors.</p