Android Botnets: A proof-of-concept using hybrid analysis approach

Mobile botnets are gaining popularity with the expressive demand of smartphone technologies. Similarly, the majority of mobile botnets are built on a popular open source OS, e.g., Android. A mobile botnet is a network of interconnected smartphone devices intended to expand malicious activities, for example; spam generation, remote access, information theft, etc., on a wide scale. To avoid this growing hazard, various approaches are proposed to detect, highlight and mark mobile malware applications using either static or dynamic analysis. However, few approaches in the literature are discussing mobile botnet in particular. In this article, the authors have proposed a hybrid analysis framework combining static and dynamic analysis as a proof of concept, to highlight and confirm botnet phenomena in Android-based mobile applications. The validation results affirm that machine learning approaches can classify the hybrid analysis model with high accuracy rate (98%) than classifying static or dynamic individually

Random forest age estimation model based on length of left hand bone for Asian population

In forensic anthropology, age estimation is used to ease the process of identifying the age of a living being or the body of a deceased person. Nonetheless, the specialty of the estimation models is solely suitable to a specific people. Commonly, the models are inter and intra-observer variability as the qualitative set of data is being used which results the estimation of age to rely on forensic experts. This study proposes an age estimation model by using length of bone in left hand of Asian subjects range from newborn up to 18-year-old. One soft computing model, which is Random Forest (RF) is used to develop the estimation model and the results are compared with Artificial Neural Network (ANN) and Support Vector Machine (SVM), developed in the previous case studies. The performance measurement used in this study and the previous case study are R-square and Mean Square Error (MSE) value. Based on the results produced, the RF model shows comparable results with the ANN and SVM model. For male subjects, the performance of the RF model is better than ANN, however less ideal than SVM model. As for female subjects, the RF model overperfoms both ANN and SVM model. Overall, the RF model is the most suitable model in estimating age for female subjects compared to ANN and SVM model, however for male subjects, RF model is the second best model compared to the both models. Yet, the application of this model is restricted only to experimental purpose or forensic practice

Malware visualizer: A web apps malware family classification with machine learning

Within the past few years, malware has been a serious threat to the security and privacy of all mobile phone users. Due to the popularity of smartphones, primarily Android, this makes them a very viable target for spreading malware. Many solutions in the past have proven to be ineffective and result many false positives. Other than that, most of the solution focuses on the android apk file, instead of visualizing the apk into image-based form. The objective of this project is to build a web apps to classify malware by transforming the apk file into image-based representation. This project uses three classification algorithm which are Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The web apps is developed using Python with help of Streamlit with is a Python library for building datadriven web apps. The dataset contains 25 malware classes ranging from Trojan Horses to Spyware and 1 legitimate application class

Deep learning based hybrid analysis of malware detection and classification: A recent review

Globally extensive digital revolutions involved with every process related to human progress can easily create the critical issues in security aspects. This is promoted due to the important factors like financial crises and geographical connectivity in worse condition of the nations. By this fact, the authors are well motivated to present a precise literature on malware detection with deep learning approach. In this literature, the basic overview includes the nature of nature of malware detection i.e., static, dynamic, and hybrid approach. Another major component of this articles is the investigation of the backgrounds from recently published and highly cited state-of-the-arts on malware detection, prevention and prediction with deep learning frameworks. The technologies engaged in providing solutions are utilized from AI based frameworks like machine learning, deep learning, and hybrid frameworks. The main motivations to produce this article is to portrait clear pictures of the option challenging issues and corresponding solution for developing robust malware-free devices. In the lack of a robust malware-free devices, highly growing geographical and financial disputes at wide globes can be extensively provoked by malicious groups. Therefore, exceptionally high demand of the malware detection devices requires a very strong recommendation to ensure the security of a nation. In terms preventing and recovery, Zero-day threats can be handled by recent methodology used in deep learning. In the conclusion, we also explored and investigated the future patterns of malware and how deals with in upcoming years. Such review may extend towards the development of IoT based applications used many fields such as medical devices, home appliances, academic systems

Augmented reality: effect on conceptual change of scientific

In recent years, Augmented Reality (AR) has received increasing emphasis and researchers gradually promote it Over the worlds. With the unique abilities to generate virtual objects over the real-world environment, it can enhance user perception. Although AR recognised for their enormous positive impacts, there are still a ton of matters waiting to be discovered. Research studies on AR toward conceptual change, specifically in scientific concept, are particularly limited. Therefore, this research aims to investigate the effect of integrating AR on conceptual change in scientific concepts. Thirty-four primary school students participated in the study. A pre-test and post-test were used to assess participants’ understanding of the scientific concepts before and after learning through AR. The findings demonstrated that 82% among them had misconceptions about the scientific concepts before learning through AR. However, most of them (around 88%) able to correct their misconceptions and shifted to have a scientific conceptual understanding after learning through AR. These findings indicate that AR was effective to be integrated into education to facilitate conceptual change

Malay

The pondok is the earliest formal institution in Islamic education established in Malaysia. The state of Kelantan is one of the earliest states in Malaysia to have a pondok institution around 18th century. This institution continues in contributing to the development of Islamic education in the state of Kelantan and Malaysia. However, the globalizations give an impact to this institution to faced challenges in terms of systems, practices also in teaching and learning in tradition methodology. The changes have caused some pondok institutions to transferred the teaching in modern education and lose their traditional identity. Therefore, this study will discuss the elements of tradition that are still practiced for the survival of pondok institutions in Kelantan. The methodology of this research based on qualitative methods including document analysis, interviews and observations to obtain research data. The content of documents will be analyzed and supported by interviews and observations to achieve the findings. The results show that the traditional elements practiced have maintained and contributed to the sustainable of the pondok institution today. Therefore, the survival of the pondok as an Islamic traditional education center still relevant even though the current education system is changing to the new aspect and methodology. The tradition methodology practiced by the pondok institution have contributed to the charismatic formation of the scholars, intellectual development, da'wah and the development of Islamic knowledge in the global Muslim community. Institusi pondok merupakan pusat pengajian Islam berbentuk formal yang paling awal ditubuhkan di Malaysia. Negeri Kelantan merupakan antara negeri yang terawal di Malaysia mempunyai institusi pondok iaitu pada abad ke 18 M. Institusi ini terus berkembang pesat dalam menyumbang kepada perkembangan syiar Islam di negeri Kelantan khususnya dan di Malaysia amnya. Namun, dengan peredaran masa dan perkembangan zaman yang semakin moden menyebabkan institusi ini menghadapi cabaran dari aspek sistem serta praktis pengajaran dan pembelajaran yang sedikit sebanyak menggugat unsur tradisi yang diwarisi sejak turun temurun. Perubahan yang berlaku telah menyebabkan sesetengah institusi pondok kehilangan identiti kerana terpaksa mengubah sistem pendidikan berbentuk moden untuk disesuaikan dengan tuntutan zaman. Justeru itu, kajian ini akan membincangkan unsur tradisi yang masih dipraktikkan dalam mengekalkan survival institusi pondok di Kelantan. Bagi mencapai objektif tersebut, kajian ini menggunakan metode kualitatif merangkumi penilaian dokumen, temubual dan pemerhatian bagi mendapatkan data kajian. Data penilaian dokumen yang dikumpulkan daripada buku, jurnal dan prosiding akan dianalisis secara kandungan disertai dengan data temubual dan pemerhatian bagi merumuskan dapatan kajian. Hasil dapatan kajian menunjukkan bahawa unsur tradisi yang dipraktikkan di institusi pondok kini telah berjaya mengekalkan identiti dan menyumbang kepada keutuhan survival institusi pondok sejak zaman berzaman. Justeru itu, kelangsungan institusi pengajian pondok sebagai pusat pendidikan Islam yang mengekalkan unsur pengajian tradisi masih kekal relevan walaupun elemen dalam sistem pendidikan semasa semakin berubah. Unsur tradisi yang dipraktikkan oleh institusi pondok telah menyumbang kepada pembentukan karismatik para ulama, pengembangan intelektual, dakwah dan perkembangan ilmu Islam kepada masyarakat Islam sejagat

Smart e-laundry monitoring system by using IoT

Nowadays people tend to do laundry outside of their house due to the limitation of time, space and machine such as a dryer. Therefore, doing laundry already become a weekly routine for certain families to ensure their clothes are clean and fresh. Hence, we can’t deny that the washing machine has a great impact on our society as it has a lot of advantages that able to help people to make their life more efficient. But, there is some improvement that we can make to every washing machine especially to the company that provides self-service laundry as their business to ensure everything is run smoothly

The summer heat of cryptojacking season : Detecting cryptojacking using heatmap and fuzzy

Cryptojacking is a subset of cybercrime in which hackers use unauthorised devices (computers, smartphones, tablets, and even servers) to mine cryptocurrencies. Similar to many other forms of cybercrime, the objective of cryptojacking is achieve profit illegally. It is also designed to remain entirely concealed from the victim's view. However, its attacks continue to evolve and spread, and their number continues to rise. Therefore, it is essential to detect cryptojacking malware, as it poses a significant risk to users. However, in machine learning intelligence detection, an excessive number of insignificant features will diminish the detection's accuracy. For machine learning-based detection, it's important to find important features in a minimal amount of data. This study therefore proposes the Pearson correlation coefficient (PMCC), a measure of the linear relationship between all features. After that, this study employs the heatmap method to visualise the PMCC value as a colour version of heat. We utilised The Fuzzy Lattice Reasoning (FLR) classifier for classification algorithms in machine learning. This experiment utilised actual cryptojacking samples and achieved a 100 percent detection accuracy rate in simulation

Understanding COVID-19 halal vaccination discourse on facebook and twitter using aspect-based sentiment analysis and text emotion analysis

The COVID-19 pandemic introduced unprecedented challenges for people and governments. Vaccines are an available solution to this pandemic. Recipients of the vaccines are of different ages, gender, and religion. Muslims follow specific Islamic guidelines that prohibit them from taking a vaccine with certain ingredients. This study aims at analyzing Facebook and Twitter data to understand the discourse related to halal vaccines using aspect-based sentiment analysis and text emotion analysis. We searched for the term “halal vaccine” and limited the timeline to the period between 1 January 2020, and 30 April 2021, and collected 6037 tweets and 3918 Facebook posts. We performed data preprocessing on tweets and Facebook posts and built the Latent Dirichlet Allocation (LDA) model to identify topics. Calculating the sentiment analysis for each topic was the next step. Finally, this study further investigates emotions in the data using the National Research Council of Canada Emotion Lexicon. Our analysis identified four topics in each of the Twitter dataset and Facebook dataset. Two topics of “COVID-19 vaccine” and “halal vaccine” are shared between the two datasets. The other two topics in tweets are “halal certificate” and “must halal”, while “sinovac vaccine” and “ulema council” are two other topics in the Facebook dataset. The sentiment analysis shows that the sentiment toward halal vaccine is mostly neutral in Twitter data, whereas it is positive in Facebook data. The emotion analysis indicates that trust is the most present emotion among the top three emotions in both datasets, followed by anticipation and fear

Adaboost-multilayer perceptron to predict the student’s performance in software engineering

Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students