Application Programming Interface (API) call feature analysis is the prominent method for dynamic android malware detection. Standard benchmark android malware API dataset includes features with high dimensionality. Not all features of the data are relevant, filtering unwanted features improves efficiency. This paper proposes fuzzy and meta-heuristic optimization hybrid to eliminate insignificant features and improve the performance. In 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 rest. Five ML methods viz., Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB) & Nearest Centroid (NC) are compared as wrappers. Several experiments are conducted and among them, the best post reduction accuracy of 98.34% is recorded with 95% elimination of features. The proposed novel method outperformed among the existing works on the same dataset.
Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats. Application Programming Interfaces (API) calls contain valuable information that can help with malware identification. The malware analysis with reduced feature space helps for the efficient identification of malware. The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy. Three 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 features. The 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) & Support Vector Machine (SVM). A hybrid Artificial Neuronal Classifier (ANC) is proposed for improving the classification of android malware. The 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.
The main purpose of the paper is to increase the funds from the foreign investors in the agricultural sector. This directly improves the economy of India giving the fruitful results. So the complete database about the crops and its yield are provided for the foreign investors to set up FPI's in India. The specific feature of the dashboard is that the paper also provides the single desk portal system (policy) which aims to create the conductive ecosystem to provide all clearances required to set up industry within a stipulated amount of time. In addition to these facilities paper also provides information about the transportation and export data source for every required crop. Alongside these arrangements, venture likewise incorporates the Hyper Food Malls which interface agriculture creation to the market by uniting farmers, processors and retailers.
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