Figure 1 - uploaded by Ahmad Firdaus
Content may be subject to copyright.
Source publication
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, informati...
Similar publications
Year after year, mobile malware attacks grow in both sophistication and diffusion. As the open source Android platform continues to dominate the market, malware writers consider it as their preferred target. Almost strictly, state-of-the-art mobile malware detection solutions in the literature capitalize on machine learning to detect pieces of malw...
Citations
... Upon an assessment of the accumulated documents, this research was able to categorize the research issue and known vulnerabilities detection methods into distinct groups. Categorization requires a comprehensive understanding of the browser-based cryptojacking detection paradigm in terms of trending research issues and detection strategies [9,10]. This study splits the data assessment into diverse research problems. ...
... This section lays down the features extracted from the 70 research papers. Table 2 tabulates the list of features of static and dynamic Static analysis is an experiment that reverse engineers the application and analyses the source code without executing the application [9], [10]. Meanwhile, dynamic analysis is an experiment that perform and execute the application in controlled environment (real hardware, virtual hardware or sandbox) [11]. ...
Various types of malwares are capable of bringing harm to users. The list of types are root exploits, botnets, trojans, spyware, worms, viruses, ransomware, and cryptojacking. Cryptojacking is a significant proportion of cyberattacks in which exploiters mine cryptocurrencies using the victim’s devices, for instance, smartphones, tablets, servers, or computers. It is also defined as the illegal utilization of victim resources (CPU, RAM, and GPU) to mine cryptocurrencies without detection. The purpose of cryptojacking, along with numerous other forms of cybercrime, is monetary gain. Furthermore, it also intended to stay concealed from the victim's viewpoint. Following this crime, to the author's knowledge, a paper focusing solely on a review of cryptojacking research is still unavailable. This paper presents cryptojacking detection information to address this deficiency, including methods, detection, analysis techniques, and features. As cryptojacking malware is a type that executes its activities using the network, most of the analysis and features fall into dynamic activities. However, static analysis is also included in the security researcher’s option. The codes that are involved are opcode and JavaScript. This demonstrates that these two languages are vital programming languages to focus on to detect cryptojacking. Moreover, the researchers also begin to adopt deep learning in their experiments to detect cryptojacking malware. This paper also examines potential future developments in the detection of cryptojacking.
... However, to develop a robust machine learning model, selecting the features for machine learning is crucial [19,20]. This is due to excessive insignificant features that will increase the dimensionality of datasets and irrelevant data, which will decrease the value of the detection's accuracy [21,22]. ...
Many smart mobile devices, including smartphones, smart televisions, smart watches, and smart vacuums, have been powered by Android devices. Therefore, mobile devices have become the prime target for malware attacks due to their rapid development and utilization. Many security practitioners have adopted different approaches to detect malware. However, its attacks continuously evolve and spread, and the number of attacks is still increasing. Hence, it is important to detect Android malware since it could expose a great threat to the users. However, in machine learning intelligence detection, too many insignificant features will decrease the percentage of the detection’s accuracy. Therefore, there is a need to discover the significant features in a minimal amount to assist with machine learning detection. Consequently, this study proposes the Pearson correlation coefficient (PMCC), a coefficient that measures the linear relationship between all features. Afterwards, this study adopts the heatmap method to visualize the PMCC value in the color of the heat version. For machine learning classification algorithms, we used a type of fuzzy logic called lattice reasoning. This experiment used real 3799 Android samples with 217 features and achieved the best accuracy rate of detection of more than 98% by using Unordered Fuzzy Rule Induction (FURIA).
... The Distributed Denial of Service (DDoS) attacks use multiple computer systems to interrupt the normal traffic of the targeted network, service, or server by flooding the systems with huge traffic. There are four components in the DDoS architecture, namely; Zombies or Bots, Botnet, Handlers, and Botmaster [24,25]. Initially, the Zombie was one of the components in the DDoS architecture which was a machine or computer that had been infected by malware. ...
Distributed Denial of Service (DDoS) is a type of attack that leverages many compromised systems or computers, as well as multiple Internet connections, to flood targeted resources simultaneously. A DDoS attack's main purpose is to disrupt website traffic and cause it to crash. As traffic grows over time, detecting a Distributed Denial of Service (DDoS) assault is a challenging task. Furthermore, a dataset containing a large number of features may degrade machine learning’s detection performance. Therefore, in machine learning, it is necessary to prepare a relevant list of features for the training phase in order to obtain good accuracy performance. With far too many possibilities, choosing the relevant feature is complicated. This study proposes the Boruta algorithm as a suitable approach to achieve accuracy in identifying the relevant features. To evaluate the Boruta algorithm, multiple classifiers (J48, random forest, naïve bayes, and multilayer perceptron) were used so as to determine the effectiveness of the features selected by the the Boruta algorithm. The outcomes obtained showed that the random forest classifier had a higher value, with a 100% true positive rate, and 99.993% in the performance measure of accuracy, when compared to other classifiers.
... An Android application is a visual record of Android programming. It includes many source files of Java objects and finally starts the apk package for fetching [16,17]. Android itself provides many basic applications such as Home, Contacts, Phone, and Browser. ...
The healthy growth of college students is related to the future development of the country and the prosperity of the nation. Under fierce social competition, college students are faced with academic pressure and employment pressure, resulting in the failure to improve their physical and mental health and their low self-acceptance level. Faced with such a situation, it is an important subject to solve the problem of the physical and mental health development of contemporary college students. As a sport that integrates sports and art, sports dance is worthy of in-depth discussion on the physical and mental development of college students. To analyze the impact of sports dance on the physical and mental development of contemporary college students, this paper uses an intelligent health monitoring system to monitor the health status of college students before and after physical dance exercise. It analyzes the influence of sports dance on the physical and mental development of college students from the aspects of cardiorespiratory endurance, muscular endurance, flexibility, and happiness. Finally, the results are obtained by conducting experiments with 10 college students. The experimental results show that the psychological well-being of college students who take physical dance exercise increases by 7.8%. Cardiorespiratory endurance and flexibility are both improved accordingly. Physical dance exercise promotes the physical and mental development of contemporary college students.
... Thus, mobile payment apps such as Google Wallet, Apple pay, Samsung Pay, and so on, are generating a new trend not only in technological development but also in market business model innovation. Thus, the integration of apps with mobile payment has developed into a business model that features co-operation among finance, communication, ICT technology and retailing operators based on finance technology (FinTech) development (Choi & Lee, 2018;Karim et al., 2020). ...
A mobile application (App) is an application designed to run on a smartphone, tablet, or other mobile device. With the continuous change of mobile payment applications in smart phones and the support of the banking system, the global mobile payment population is increasing. This study examines the behaviors of Taiwan mobile payment and apps users, a total of 1,176 valid questionnaire data is divided into six sections with 29 items for a database design. This study develops a data mining approach, including clustering analysis and association rules, based on a relational database. Thus, this study shows that mobile payment not only can provide payment service but is also a critical mobile application platform for online business. Finally, we show that as users of mobile payment and apps gain additional demand and consumption ability, online operators can gradually put together mobile payment business models to enable future electronic commerce online recommendations.
As the use of mobile devices increases, the security risks associated with them also steadily increase. One of the most serious threats is the presence of mobile botnets, which are a group of devices controlled by cybercriminals to launch attacks or data theft. Identifying infected devices is a key step in counteracting these hazards. This article presents an analysis of the data collected in the experiment using a mobile botnet application. We focused on the analysis of the generated network traffic and events registered by mobile devices. As our results show, such data analysis and searching for patterns left by malicious software in today’s reality can no longer remain an efficient tool for the detection of such threats. The results highlight the need for further research and improvement of techniques for the detection of mobile botnet members to improve the efficiency and accuracy of their identification. This article also describes possible reasons for the lack of unambiguous results and presents proposals for further research.
Currently the whole world is going digitalization, using handheld device like smartphones and evolution of Internet, due to pandemic, all the transactions are going online. The security at end devices is an important issue to everyone. We believe that the, data is in transit is more secure, but in reality is not true. The data are in hands of bad actors for malicious activities. Android Ransomware is one of the most widely distributed assaults throughout the world. It is a type of virus that prevents users from accessing the operating system and encrypts essential data saved on their device. The majority of this work focuses on two goals: the first is to offer an introduction of ransomware and machine learning techniques, and the second part focussed on thorough assessment of detection of Android ransomware application using machine learning methods. After a thorough analysis of existing mechanisms of android ransomware detection, we found that the combination of static behaviour analysis of application and machine learning techniques gives good accuracy of android ransomware applications. In this research used, proposed a static based feature selection technique and applied machine learning algorithms for prediction of ransomware applications. For classification, the Decision Tree, Extra Tree classifier, Light Gradient Boosting Machine methods are employed in conjunction with the random forest tree. The dataset used was obtained from Kaggle and consists of 331 Android application permissions, 199 of which are Ransomware. The suggested model outperforms with a detection accuracy of 98.05 percent. Based on its best performance, we believe our suggested approach will be useful in malware and forensic investigation.
This chapter examines the relevancy of value dimensions and the trustworthiness of blockchain-based mobile phone applications (BMPAs). Users shop and pay through digital wallets in the expanding metaverse by pairing blockchain technology and artificial intelligence (AI). The relationships discussed in this discursive chapter focused on the company’s identification and customers’ repeat usage underpinned by the means-end chain theory, social identity theory, and prospect theory. A critical review of relevant literature, predominantly from 1991 to 2021, was carried out. A research framework was formed where the second-order constructs included the hedonic value, utilitarian value, and BMPA trustworthiness. BMPA identification was mapped as the antecedent variable of repeat customers’ BMPA usage. There are three proposed control variables, namely, (1) past BMPA usage, (2) gender, and (3) age. Past studies signified that an increase in perceived hedonic value, utilitarian value, and trustworthiness was having a positive impact on a company’s identification. A favorable company’s identification leads to an increase in repeat customers’ usage. However, the relationship was observed to be indirect. Perceived risk potentially moderates the relationship between company’s identification and repeat customers’ usage negatively. Using the dimension of customer value and customer behavior, this chapter recommends a Customer Outcome Framework for businesses that adopt the BMPAs. This chapter utilizes a sum of customer value and risk factors to provide suggestions for BMPA usage based on various stakeholder perceptions.
