GR-23 Machine Learning Techniques for Malware Network Traffic Detection

Persistent malware variants are a constant threat to computing infrastructure across all regions and business sectors. Traditional detection systems focus primarily on signature-based analysis but this approach cannot adequately keep pace with the velocity and volume of new malware variants that are continuously deployed onto the internet. Most network traffic detection techniques are focused on analyzing raw packets and have not deterred the surge of persistent malware. Therefore, it is important to develop new research techniques that are focused on optimized metadata from malware network traffic to effectively identify an ever-increasing expanse of malicious software. Recent research efforts by Letteri et al. have produced a quality data set (MTA-KDD’19) that is utilized for this research project. New information in the area of malware network traffic detection is pursued through this research proposal. Specifically, I seek to find a defensible answer to the following question: Can machine learning techniques produce highly accurate classification models for malicious network traffic detection based on analysis of a statistically optimized data set? I believe that an affirmative answer to this research question provides a beneficial contribution to the academic community. The principal tool utilized to analyze the optimized data set for this research project is the Waikato Environment for Knowledge Analysis (WEKA). There are 64,550 instances and 33 features in the MTA-KDD’19 data set that are analyzed along with cross-validation and percentage split alternatives. The classification experiment performed by the authors of the MTA-KDD’19 data set is used as a baseline. The following machine learning classification models have been applied for this research investigation: Multilayer Perceptron, Decision Tree, Support Vector Machine, and K-Nearest Neighbors. The preliminary settings for these machine learning models include 10-fold cross-validation and 80% train 20% test data split. The Decision Tree classifier produced the best preliminary result with 100% accuracy when set to run an 80% training 20% test split and 99.9954% accuracy when set to run 10-fold cross-validation. This preliminary result has outperformed the results observed in the experiment presented by the authors of the MTA-KDD’19 data set. Other preliminary metrics illustrate that the selected models exhibit consistent and highly accurate performance. The multilayer perceptron classifier produced a preliminary result of 99.3649% accuracy when set to run an 80% training 20% test split and 99.3416% accuracy when set to run 10-fold cross-validation. The K-Nearest Neighbor classifier (K=1) produced a preliminary result of 98.9311% accuracy when set to run an 80% training 20% test split and 99.0024% accuracy when set to run 10-fold cross-validation. The Support Vector Machine classifier produced a preliminary result of 97.8081% accuracy when set to run an 80% training 20% test split and 97.7755% accuracy when set to run 10-fold cross-validation. The final stage of this research project will include implementation of additional machine learning methodologies. These methods will include feature selection techniques and ensemble learning models.Advisors(s): Dr. Seyedamin PouriyehTopic(s): SecurityCYBR 724

Effect of a Perioperative, Cardiac Output-Guided Hemodynamic Therapy Algorithm on Outcomes Following Major Gastrointestinal Surgery A Randomized Clinical Trial and Systematic Review

Importance: small trials suggest that postoperative outcomes may be improved by the use of cardiac output monitoring to guide administration of intravenous fluid and inotropic drugs as part of a hemodynamic therapy algorithm.Objective: to evaluate the clinical effectiveness of a perioperative, cardiac output–guided hemodynamic therapy algorithm.Design, setting, and participants: OPTIMISE was a pragmatic, multicenter, randomized, observer-blinded trial of 734 high-risk patients aged 50 years or older undergoing major gastrointestinal surgery at 17 acute care hospitals in the United Kingdom. An updated systematic review and meta-analysis were also conducted including randomized trials published from 1966 to February 2014.Interventions: patients were randomly assigned to a cardiac output–guided hemodynamic therapy algorithm for intravenous fluid and inotrope (dopexamine) infusion during and 6 hours following surgery (n=368) or to usual care (n=366).Main outcomes and measures: the primary outcome was a composite of predefined 30-day moderate or major complications and mortality. Secondary outcomes were morbidity on day 7; infection, critical care–free days, and all-cause mortality at 30 days; all-cause mortality at 180 days; and length of hospital stay.Results: baseline patient characteristics, clinical care, and volumes of intravenous fluid were similar between groups. Care was nonadherent to the allocated treatment for less than 10% of patients in each group. The primary outcome occurred in 36.6% of intervention and 43.4% of usual care participants (relative risk [RR], 0.84 [95% CI, 0.71-1.01]; absolute risk reduction, 6.8% [95% CI, ?0.3% to 13.9%]; P?=?.07). There was no significant difference between groups for any secondary outcomes. Five intervention patients (1.4%) experienced cardiovascular serious adverse events within 24 hours compared with none in the usual care group. Findings of the meta-analysis of 38 trials, including data from this study, suggest that the intervention is associated with fewer complications (intervention, 488/1548 [31.5%] vs control, 614/1476 [41.6%]; RR, 0.77 [95% CI, 0.71-0.83]) and a nonsignificant reduction in hospital, 28-day, or 30-day mortality (intervention, 159/3215 deaths [4.9%] vs control, 206/3160 deaths [6.5%]; RR, 0.82 [95% CI, 0.67-1.01]) and mortality at longest follow-up (intervention, 267/3215 deaths [8.3%] vs control, 327/3160 deaths [10.3%]; RR, 0.86 [95% CI, 0.74-1.00]).Conclusions and relevance: in a randomized trial of high-risk patients undergoing major gastrointestinal surgery, use of a cardiac output–guided hemodynamic therapy algorithm compared with usual care did not reduce a composite outcome of complications and 30-day mortality. However, inclusion of these data in an updated meta-analysis indicates that the intervention was associated with a reduction in complication rate

Disruption of glycogen utilization markedly improves the efficacy of carboplatin against preclinical models of clear cell ovarian carcinoma

High stage and recurrent ovarian clear cell carcinoma (OCC) are associated with poor prognosis and resistance to chemotherapy. A distinguishing histological feature of OCC is abundant cytoplasmic stores of glucose, in the form of glycogen, that can be mobilized for cellular metabolism. Here, we report the effect on preclinical models of OCC of disrupting glycogen utilization using the glucose analogue 2-deoxy-D-glucose (2DG). At concentrations significantly lower than previously reported for other cancers, 2DG markedly improves the efficacy in vitro of carboplatin chemotherapy against chemo-sensitive TOV21G and chemo-resistant OVTOKO OCC cell lines, and this is accompanied by the depletion of glycogen. Of note, 2DG doses-of more than 10-fold lower than previously reported for other cancers-significantly improve the efficacy of carboplatin against cell line and patient-derived xenograft models in mice that mimic the chemo-responsiveness of OCC. These findings are encouraging, in that 2DG doses, which are substantially lower than previously reported to cause adverse events in cancer patients, can safely and significantly improve the efficacy of carboplatin against OCC. Our results thus justify clinical trials to evaluate whether low dose 2DG improves the efficacy of carboplatin in OCC patients