A survey of human-in-the-loop for machine learning

X Wu, L **ao, Y Sun, J Zhang, T Ma, L He - Future Generation Computer …, 2022 - Elsevier
Abstract Machine learning has become the state-of-the-art technique for many tasks
including computer vision, natural language processing, speech processing tasks, etc …

A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system

A Gaurav, BB Gupta, PK Panigrahi - Enterprise Information …, 2023 - Taylor & Francis
ABSTRACT The Internet of Things (IoT) is a relatively new technology that has piqued
academics' and business information systems' attention in recent years. The Internet of …

[HTML][HTML] DL-Droid: Deep learning based android malware detection using real devices

MK Alzaylaee, SY Yerima, S Sezer - Computers & Security, 2020 - Elsevier
The Android operating system has been the most popular for smartphones and tablets since
2012. This popularity has led to a rapid raise of Android malware in recent years. The …

Sapienz: Multi-objective automated testing for android applications

K Mao, M Harman, Y Jia - … of the 25th international symposium on …, 2016 - dl.acm.org
We introduce Sapienz, an approach to Android testing that uses multi-objective search-
based testing to automatically explore and optimise test sequences, minimising length, while …

Guided, stochastic model-based GUI testing of Android apps

T Su, G Meng, Y Chen, K Wu, W Yang, Y Yao… - Proceedings of the …, 2017 - dl.acm.org
Mobile apps are ubiquitous, operate in complex environments and are developed under the
time-to-market pressure. Ensuring their correctness and reliability thus becomes an …

The evolution of android malware and android analysis techniques

K Tam, A Feizollah, NB Anuar, R Salleh… - ACM Computing …, 2017 - dl.acm.org
With the integration of mobile devices into daily life, smartphones are privy to increasing
amounts of sensitive information. Sophisticated mobile malware, particularly Android …

Fill in the blank: Context-aware automated text input generation for mobile gui testing

Z Liu, C Chen, J Wang, X Che, Y Huang… - 2023 IEEE/ACM 45th …, 2023 - ieeexplore.ieee.org
Automated GUI testing is widely used to help ensure the quality of mobile apps. However,
many GUIs require appropriate text inputs to proceed to the next page, which remains a …

Robust smartphone app identification via encrypted network traffic analysis

VF Taylor, R Spolaor, M Conti… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
The apps installed on a smartphone can reveal much information about a user, such as their
medical conditions, sexual orientation, or religious beliefs. In addition, the presence or …

Mamadroid: Detecting android malware by building markov chains of behavioral models (extended version)

L Onwuzurike, E Mariconti, P Andriotis… - ACM Transactions on …, 2019 - dl.acm.org
As Android has become increasingly popular, so has malware targeting it, thus motivating
the research community to propose different detection techniques. However, the constant …

Reinforcement learning based curiosity-driven testing of Android applications

M Pan, A Huang, G Wang, T Zhang, X Li - Proceedings of the 29th ACM …, 2020 - dl.acm.org
Mobile applications play an important role in our daily life, while it still remains a challenge
to guarantee their correctness. Model-based and systematic approaches have been applied …