Hybrid Consensus Pruning of Ensemble Classifiers for Big Data Malware Detection

Abawajy, Jemal; Chowdhury, Mihir; Kelarev, AV

doi:10.1109/tcc.2015.2481378

Cited by 17 publications

(12 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Ensemble methods are machine-learning algorithms that utilize multiple classifiers to determine the predicted outcome by taking a (weighted) vote of their predictions. These methods often perform better than any single classifier 38 , 39 . There are several different ensemble methods, such as voting, bagging, stacking, and boosting.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare

et al. 2021

View full text Add to dashboard Cite

Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis, which is critical in reducing mortality, is challenging as many of its signs and symptoms are similar to other less critical conditions. We develop an artificial intelligence algorithm, SERA algorithm, which uses both structured data and unstructured clinical notes to predict and diagnose sepsis. We test this algorithm with independent, clinical notes and achieve high predictive accuracy 12 hours before the onset of sepsis (AUC 0.94, sensitivity 0.87 and specificity 0.87). We compare the SERA algorithm against physician predictions and show the algorithm’s potential to increase the early detection of sepsis by up to 32% and reduce false positives by up to 17%. Mining unstructured clinical notes is shown to improve the algorithm’s accuracy compared to using only clinical measures for early warning 12 to 48 hours before the onset of sepsis.

show abstract

Section: Methodsmentioning

confidence: 99%

Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare

et al. 2021

View full text Add to dashboard Cite

show abstract

“…As a result, machine learning algorithms are taking the centre stage to malware detection [ 2 , 7 , 8 ]. Machine learning based solutions rely heavily on extracting meaningful features from the Android apps for training the models [ 9 ]. Generally, static and dynamic analysis methods are utilized to extract typical malware descriptive behaviour (i.e., features) from the raw data.…”

Section: Introductionmentioning

confidence: 99%

Feature Subset Selection for Malware Detection in Smart IoT Platforms

Abawajy

Darem

Alhashmi

2021

Sensors

Self Cite

View full text Add to dashboard Cite

Malicious software (“malware”) has become one of the serious cybersecurity issues in Android ecosystem. Given the fast evolution of Android malware releases, it is practically not feasible to manually detect malware apps in the Android ecosystem. As a result, machine learning has become a fledgling approach for malware detection. Since machine learning performance is largely influenced by the availability of high quality and relevant features, feature selection approaches play key role in machine learning based detection of malware. In this paper, we formulate the feature selection problem as a quadratic programming problem and analyse how commonly used filter-based feature selection methods work with emphases on Android malware detection. We compare and contrast several feature selection methods along several factors including the composition of relevant features selected. We empirically evaluate the predictive accuracy of the feature subset selection algorithms and compare their predictive accuracy and the execution time using several learning algorithms. The results of the experiments confirm that feature selection is necessary for improving accuracy of the learning models as well decreasing the run time. The results also show that the performance of the feature selection algorithms vary from one learning algorithm to another and no one feature selection approach performs better than the other approaches all the time.

show abstract

“…Although the effectiveness of malware detection using ensemble classifiers is very promising, several researchers note that the memory and processing requirements make large ensemble classifiers unsuitable for malware detection in big data environments [178]. To address this problem, a pruning method has been recently proposed [179], as well as a novel method of selecting optimal classifiers based on weighted voting [180].…”

Section: E Ensemble Classifiersmentioning

confidence: 99%