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Swarm Optimization and Machine Learning for Android Malware Detection

Computers, Materials & Continua 2022年第12期73卷 6327-6345页

作者： k.santosh jhansi P.Ravi kiran Varma Sujata Chakravarty Centurion University of Technology and Management ParalakhemundiOdishaIndia Maharaj Vijayaram Gajapathi Raj College of Engineering VizianagaramIndia Centurion University of Technology and Management BhubaneswarOdishaIndia

Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security *** Programming Interfaces(API)calls contain valuable information that can help with malware *** malware analysis with reduced feature space helps for the efficient identification of *** goal of this research is to find the most informative features of API calls to improve the android malware detection *** swarm optimization methods,viz.,Ant Lion Optimization(ALO),Cuckoo Search Optimization(CSO),and Firefly Optimization(FO)are applied to API calls using auto-encoders for identification of most influential *** nature-inspired wrapperbased algorithms are evaluated using well-known Machine Learning(ML)classifiers such as Linear Regression(LR),Decision Tree(DT),Random Forest(RF),k-Nearest Neighbor(kNN)&SupportVector Machine(SVM).A hybrid Artificial Neuronal Classifier(ANC)is proposed for improving the classification of android *** experimental results yielded an accuracy of 98.87%with just seven features out of hundred API call features,i.e.,a massive 93%of data optimization.

关键词： Android malware API calls auto-encoders ant lion optimization cuckoo search optimization firefly optimization artificial neural networks artificial neuronal classifier

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A Two-Tier Fuzzy Meta-Heuristic Hybrid Optimization for Dynamic Android Malware Detection

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Journal of Cyber Security 2022年第3期4卷 185-202页

作者： k.santosh jhansi Sujata Chakravarty P.Ravi kiran Varma Centurion University of Technology and Management ParalakhemundiOdishaIndia Maharaj Vijayaram Gajapathi Raj College of Engineering ViizianagaramIndia Centurion University of Technology and Management BhubaneswarOdishaIndia

Application Programming Interface(API)call feature analysis is the prominent method for dynamic android malware *** benchmark androidmalware API dataset includes featureswith high *** all features of the data are relevant,filtering unwanted features improves *** paper proposes fuzzy and meta-heuristic optimization hybrid to eliminate insignificant features and improve the *** the first phase fuzzy benchmarking is used to select the top best features,and in the second phase meta-heuristic optimization algorithms viz.,Moth Flame Optimization(MFO),Multi-Verse Optimization(MVO)&Whale Optimization(WO)are run with Machine Learning(ML)wrappers to select the best from the *** ML methods viz.,Decision Tree(DT),Random Forest(RF),k-NearestNeighbors(kNN),Naie Bayes(NB)&NearestCentroid(NC)are compared as *** experiments are conducted and among them,the best post reduction accuracy of 98.34% is recorded with 95% elimination of *** proposed novelmethod outperformed among the existing works on the same dataset.

关键词： Wrapper feature selection multi-verse optimization moth flame optimization whale optimization malware detection classification

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Geochemical systematics of the Mauranipur-Babina greenstone belt,Bundelkhand Craton,Central India:Insights on Neoarchean mantle plume-arc accretion and crustal evolution

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Geoscience Frontiers 2018年第3期9卷 769-788页

作者： S.P.Singh k.S.V.Subramanyam C.Manikyamba M.santosh M.Rajanikanta Singh B.Chandan kumar Department of Geology Institute of Earth SciencesBundelkhand UniversityJhansiU.P284128India CSIR-National Geophysical Research Institute Uppal RoadHyderabad500007India Department of Earth Sciences University of AdelaideAustralia School of Earth Science&Resources China University of GeosciencesBeijingChina Department of Geology Central University of KeralaKasaragodKerala671316India

The Neoarchean Bundelkhand greenstone sequences at Mauranipur and Babina areas within the Bundelkhand Gneissic Complex preserve a variety of magmatic rocks such as komatiitic basalts, basalts,felsic volcanic rocks and high-Mg andesites belonging to the Baragaon, Raspahari and koti *** intrusive and extrusive komatiitic basalts are characterized by low SiO_2(39-53 wt.%), high MgO(18-25 wt.%).moderately high Fe_2O_3(7.1-11.6 wt.%), Al_2O_3(4.5-12.0 wt.%), and TiO_2(0.4-1.23 wt.%)with super to subchondritic(Gd/Yb)N ratios indicating garnet control on the melts. The intrusive komatiitic suite of Ti-enriched and Al-depleted type possesses predominant negative Eu and positive Nb, Ti and Y anomalies. The chemical composition of basalts classifies them into three types with varying SiO_2, TiO_2, MgO, Fe_2O_3, Al_2O_3 and CaO. At similar SiO_2 content of type Ⅰ and Ⅲ basalts, the type II basalts show slightly high Al_2O_3 and Fe_2O_3 contents. Significant negative anomalies of Nb, Zr, Hf and Ti, slightly enriched LREE with relatively flat HREE and low ∑REE contents are observed in type Ⅰ and Ⅱ basalts. TypeⅢ basalts show high Zr/Nb ratios(9.8-10.4), TiO_2(1.97-2.04 wt.%), but possess strikingly flat Zr, Hf, Y and Yb and are uncontaminated. Andesites from Agar and koti have high SiO_2(55-64 wt.%), moderate TiO_2(0.4-0.7 wt.%), slightly low Al_2O_3(7-11.9 wt.%), medium to high MgO(3-8 wt.%) and CaO contents(10-17 wt.%). Anomalously high Cr, Co and Ni contents are observed in the koti rhyolites. Tholeiitic to calc alkaline affinity of mafic-felsic volcanic rocks and basalt-andesite dacite-rhyolite differentiation indicate a mature arc and thickened crust during the advanced stage of the evolution of Neoarchean Bundelkhand greenstone belt in a convergent tectonic setting where the melts were derived from partial melting of thick basaltic crust metamorphosed to amphibolite-eclogite facies. The trace element systematics suggest the presence of arc-back arc association w

关键词： Bundelkhand craton Greenstone belts Mantle dynamics Plume-arc accretion Neoarchean crustal evolution

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