Android-manifest extraction and labeling method for malware compilation and dataset creation

Malware is a nuisance for smartphone users. The impact is detrimental to smartphone users if the smartphone is infected by malware. Malware identification is not an easy process for ordinary users due to its deeply concealed dangers in application package kit (APK) files available in the Android Play Store. In this paper, the challenges of creating malware datasets are discussed. Long before a malware classification process and model can be built, the need for datasets with representative features for most types of malwares has to be addressed systematically. Only after a quality data set is available can a quality classification model be obtained using machine learning (ML) or deep learning (DL) algorithms. The entire malware classification process is a full pipeline process and sub processes. The authors purposefully focus on the process of building quality malware datasets, not on ML itself, because implementing ML requires another effort after the reliable dataset is fully built. The overall step in creating the malware dataset starts with the extraction of the Android Manifest from the APK file set and ends with the labeling method for all the extracted APK files. The key contribution of this paper is on how to generate datasets systematically from any APK file

Blockchain-Based E-Certificate Verification and Validation Automation Architecture to Avoid Counterfeiting of Digital Assets in Order to Accelerate Digital Transformation

The security and confidentiality of data are very important for institutions. Meanwhile, data fabrication or falsification of official documents is still common. Validation of the authenticity of documents such as certificates becomes a challenge for various parties, especially those who have to make decisions based on the validity of the document. Scanning-based signatures on printed and digital documents are still relatively easy to counterfeit and yet still difficult to distinguish from the original. The traditional approach is no longer reliable. Solutions to these problems require the existence of data security techniques, seamless online verification of the authenticity of printed documents, and e-certificates quickly. The objective of the study is to model the e-certificate verification process via blockchain and proof-of-stake consensus methods and use MD5 encryption. The data or identity listed on the e-certificate is secured with an embedded digital signature in the form of a QR code and can be checked for the truth online. A combination of technologies capable of suppressing or removing counterfeiting of digital assets will accelerate digital transformation across spectrums of modern life. The resulting architectural model can be used as a starting point for implementing a blockchain-based e-certificate verification and validation automation system

A MODEL VISION OF SORTING SYSTEM APPLICATION USING ROBOTIC MANIPULATOR

Image processing in today’s world grabs massive attentions as it leads to possibilities of broaden application in many fields of high technology. The real challenge is how to improve existing sorting system in the Moduler Processing System (MPS) laboratirium which consists of four integrated stations of distribution, testing, processing and handling with a new image processing feature. Existing sorting method uses a set of inductive, capacitive and optical sensors do differentiate object color. This paper presents a mechatronics color sorting system solution with the application of image processing. Supported by OpenCV, image processing procedure senses the circular objects in an image captured in realtime by a webcam and then extracts color and position information out of it. This information is passed as a sequence of sorting commands to the manipulator (Mitsubishi Movemaster RV-M1) that does pick-and-place mechanism. Extensive testing proves that this color based object sorting system works 100% accurate under ideal condition in term of adequate illumination, circular objects’ shape and color. The circular objects tested for sorting are silver, red and black. For non-ideal condition, such as unspecified color the accuracy reduces to 80%.

Prediction Model of Production Completion Delay to Improve Service Quality Using Decision Tree Versus Multilayer Perceptron Method

Delays in the completion of pvd production can be caused by several factors. Including the actual experience in the production of the difficulty of each process and color type, even the difficulty of the product type can also be affected. In this study, the prediction of the delay in the completion of pvd production was carried out using the C4.5 decision tree and Multilayer Perceptron data mining method approach using Production Results data at PT. Surya Toto Indonesia, whose results are expected to provide information and input for the company in making production plans in the future. The data testing method was carried out with 5 (five) testing times with different amounts of data to determine the level of consistency of accuracy obtained. C4.5 gives the results of a decision tree where the root is the color type and as the leaf is the product category, type type and order period. The average value of accuracy generated in the C4.5 decision tree method is 87.15%. While the Multilayer Perceptron obtained an average accuracy of 87.98%, which is greater than the decision tree method with a difference of 0.83%