Cloud Computing and Data Science

DeepMetaDroid: Real-Time Android Malware Detection Using Deep Learning and Metadata Features

Hashida Haidros Rahima Manzil, Manohar Naik S — Mon, 20 May 2024 00:00:00 +0800

The increasing prevalence of Android malware poses significant risks to mobile devices and user privacy. The traditional detection methods have limitations in keeping up with the evolving landscape of malware attacks, necessitating the development of more effective solutions. In this paper, we present DeepMetaDroid, a real-time detection approach for Android malware that leverages metadata features. By analyzing crucial metadata, including APK size, download size, permissions, certificates, and DEX files, the proposed method enables effective identification of malware and enhances mobile security. Using deep learning techniques, a lightweight Android real-time monitoring system is equipped with the trained model. These methods include long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and other ensemble models. Utilizing the rectified linear unit (ReLU) as the activation function, the DNN model is constructed with 32 neurons in the input layer. A one-dimensional convolutional layer with 32 neurons and a filter size of three is used as the input layer in the CNN model. The LSTM model is designed with an input layer consisting of 16 neurons. The GRU model with 32 neurons is employed in the input layer. Additionally, ensemble models that combined several architectures were developed. The proposed method offers a faster and more scalable solution for malware detection by consuming fewer resources like memory and CPU. This work ensures device security by providing real-time monitoring on Android devices to prevent users from installing malicious applications and, thus, enhance user privacy and security.

Machine Vision for Detecting Defects in Liquid Bottles: An Industrial Application for Food and Packaging Sector

Omid Farhangi, Ehsan Sheidaee, Asma kisalaei — Tue, 25 Jun 2024 00:00:00 +0800

The quality control of liquid packaging, such as cooking oils and beverages (bottled water, soft drinks, juices, etc.), is crucial due to the inherent risk of leakage. This process involves inspecting bottles for cap and seal ring defects and addressing issues arising from the gradual degradation of filling machines, leading to variations in the surface level of liquid bottles over time. Additionally, proper label placement significantly contributes to the customer-friendliness of a product. This research aims to introduce an automated vision-based rating system designed for the online inspection of defects in liquid bottles. The system is versatile, applicable to both academic and industrial settings, and can be easily adapted for use with various types of transparent liquid bottles. The defect detection metrics include three measures of distance determination and pattern matching. The equipment used in this study includes a Complementary Metal Oxide Semiconductor (CMOS) camera with a USB connection, a laptop, and a 14-speed conveyor belt, among other components. The system demonstrated an average accuracy of 95.6%, with specific accuracies for surface level, cap, and label placement at 100%, 95%, and 92%, respectively.

Advancing Stock Market Predictions with Time Series Analysis including LSTM and ARIMA

Wed, 15 May 2024 00:00:00 +0800

Predicting stock market prices accurately is a major task for investors and traders seeking to optimize their decision-making processes. This research focuses on the comparative analysis of advanced machine learning (ML) techniques, particularly, the Long Short-Term Memory (LSTM) model and Autoregressive Integrated Moving Average (ARIMA) model for predicting stock market prices. The study enforces thorough data collection and preprocessing to ensure the quality and reliability of the historical stock price data, forming a robust foundation for the predictive models. The core contribution of this paper lies in its systematic and comparative analysis of these two models. A range of performance metrics, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), are employed to assess and contrast the predictive accuracy and efficiency of the LSTM and ARIMA models. The research findings indicate that the ARIMA model, contrary to expectations, outperforms the LSTM model in this study, achieving lower RMSE and MAE values. Specifically, the ARIMA model demonstrates a Test RMSE of 4.336 and a Test MAE of 3.45926, indicating its superior predictive accuracy compared to the LSTM model. Furthermore, the study sets its findings against the backdrop of existing literature by comparing the performance of its models with those reported in previous research. This comparison shows better results achieved by our stock market prediction models. By addressing limitations observed in prior studies and demonstrating practical applicability, this research contributes to advancing stock market prediction methodologies, offering valuable insights for investors and traders.

Analysis and Prediction of COVID-19 Using Growth Analysis Models: A Case Study

Kalimullah Lone, Shabir Ahmad Sofi — Thu, 07 Mar 2024 00:00:00 +0800

The coronavirus disease 2019 outbreak has added to the development of novel methods to study the epidemiological and predictive nature of the pandemic. Mining such data is necessary as this data is full of trends and information. Using data mining techniques allows us to extract and process such data to predict the pandemic's trends and behavior. Analysis, evaluation, and prediction are performed on Jammu and Kashmir's data during the period 09^th of March 2020 to 10^th of February 2021. The work is done on the dataset of patients provided by the Department of Information and Public Relations, Government of Jammu and Kashmir. Various mathematical models and techniques were used to predict the Virus spread and occurrence with the help of symptoms. We aim to propose a model to predict the virus occurrence based on the symptoms and epidemiological nature of the pandemic. The purpose of this study is to understand the virus occurrence and distribution. The work has helped our government to find the most infected areas and future challenges to tackle any such pandemic. The trends and behavior of the virus in Jammu and Kashmir were studied. People under observation, people tested for the virus, positive, negative, recovered, active, and deaths were keenly observed. The prediction to find an infected patient was carried out with the help of symptoms. The results obtained from the prediction model are verified with the actual results.