ABSTRACT
In the contemporary work-from-home period and the previous Covid-19 times, fitness or, to put it another way, obesity, has emerged as a significant issue. Technology usage has suddenly increased and become ingrained in our daily lives. For the development of such individuals, we are developing the fitness application FITWORLD, which supports individuals in achieving their objectives by offering customised training and dietary regimens. Our proposal is based on research into the workout habits of many individuals with various objectives and BMIs. These guidelines are simple to follow and help boost immunity, which further guards against Covid. We are leveraging a variety of technologies and tools, including: •Android Studio •Kotlin •XML •Draw.io •Figma •Star UML •Firebase As a consequence, we are striving to create Fitworld, an app, employing the tools and technologies indicated above. By assisting individuals in maintaining a healthy lifestyle via the use of our app, we want to make our nation healthy and fit in the future. © 2022 IEEE.
ABSTRACT
In the severe COVID-19 environment, encrypted mobile malware is increasingly threatening personal privacy, especially those targeting on Android platform. Existing methods mainly focus on extracting features from Android Malware (DroidMal) by reversing the binary samples, which is sensitive to the deduction of the available samples. Thus, they fail to tackle the insufficiency of the novel DoridMal. Therefore, it is necessary to investigate an effective solution to classify large-scale DroidMal, as well as to detect the novel one. We consider few-shot DroidMal detection as DoridMal encrypted network traffic classification and propose an image-based method with meta-learning, namely AMDetector, to address the issues. By capturing network traffic produced by DroidMal, samples are augmented and thus cater to the learning algorithms. Firstly, DroidMal encrypted traffic is converted to session images. Then, session images are embedded into a high dimension metric space, in which traffic samples can be linearly separated by computing the distance with the corresponding prototype. Large-scale and novel DroidMal traffic is classified by applying different meta-learning strategies. Experimental results on public datasets have demonstrated the capability of our method to classify large-scale known DroidMal traffic as well as to detect the novel one. It is encouraging to see that, our model achieves superior performance on known and novel DroidMal traffic classification among the state-of-the-arts. Moreover, AMDetector is able to classify the unseen cross-platform malware. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.