Results 181 to 190 of about 156,914 (217)

Android Malware Detection: A Survey

2018
In the world today, smartphones are evolving every day and with this evolution, security becomes a big issue. Security is an important aspect of the human existence and in a world, with inadequate security, it becomes an issue for the safety of the smartphone users. One of the biggest security threats to smartphones is the issue of malware.
Modupe Odusami, Olusola Abayomi-Alli, Sanjay Misra, Olamilekan Shobayo, Robertas Damasevicius, Rytis Maskeliunas   +5 more
openaire   +2 more sources

Android malware analysis and conceptual malware mitigation approaches

2016 International Conference on Information and Communication Technology Convergence (ICTC), 2016
Mobile devices have enjoyed unprecedented growth in the last decade. As devices become more ubiquitous and users place more sensitive data on their devices, the amount of mobile malware in the wild has grown. In order to protect mobile device users and their data, mobile security solutions have been brought to market for consumer and enterprise users ...
Tae (Tom) Oh, Hwa Shin Moon, Young Ho Kim, Jeong Neyo Kim, Bill Stackpole   +4 more
openaire   +1 more source

Composition-Malware: Building Android Malware at Run Time

2015 10th International Conference on Availability, Reliability and Security, 2015
We present a novel model of malware for Android, named composition-malware, which consists of composing fragments of code hosted on different and scattered locations at run time. An key feature of the model is that the malicious behavior could dynamically change and the payload could be activated under logic or temporal conditions.
Gerardo Canfora, Francesco Mercaldo, Giovanni Moriano, Corrado Aaron Visaggio   +3 more
openaire   +2 more sources

Android Malware Detection

2021
Malicious applications pose a threat to the security of the Android platform. The growing amount and diversity of these applications render conventional defenses largely ineffective and thus Android smartphones often remain un-protected from novel malware.
S, Mrs Hamsareka, S, Krishnamoorthy, T, Prasanth, R, Purusothaman, A, Santhosh Kumar   +4 more
openaire   +1 more source

Characterization of Android Malwares and their families

ACM Computing Surveys
Nowadays, smartphones have made our lives easier and have become essential gadgets for us. Apart from calling, mobiles are used for various purposes, such as banking, chatting, data storage, connecting to the internet, and running apps, that make life easier. Therefore, attackers are developing new methods or malware to steal smartphone data. Primarily,
Tejpal Sharma, Dhavleesh Rattan
openaire   +1 more source

Android Malware Detection

2020
Smartphones and mobile tablets are rapidly becoming essential in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are intermingled with a large number of benign apps in Android markets that seriously threaten Android security.
Shymala Gowri Selvaganapathy, G. Sudha Sadasivam, Hema Priya N, Rajeshwari N, Dharani M, K. Karthik   +5 more
openaire   +1 more source

CNN-Based Android Malware Detection

2017 International Conference on Software Security and Assurance (ICSSA), 2017
The growth in mobile devices has exponentially increased, making information easy to access but at the same time vulnerable. Malicious applications can gain access to sensitive and critical user information by exploiting unsolicited permission controls.
Meenu Ganesh, Priyanka Pednekar, Pooja Prabhuswamy, Divyashri Sreedharan Nair, Younghee Park, Hyeran Jeon   +5 more
openaire   +2 more sources

Machine Learning to Identify Android Malware

2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2018
This paper investigates the use of machine learning based classification for the detection of Android malware. A dataset of benign Android applications and malware was formed, utilizing only apps that have appeared in recent years. Three machine learning classifiers were then applied to the joint data set, and the results were reviewed for accuracy ...
Geran Tam, Aaron Hunter 0001
openaire   +1 more source

Reviving Android Malware with DroidRide: And How Not To

2016 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2016
Malware has started grabbing its undeserved share long before the blossom of Android ecosystem. Injected with malware, malicious applications (apps) may threat users in various ways like financial charges and information stealing. When the severity of a deluge of malware was first noticed, malware detectors delivered unsatisfactory detection accuracy ...
Min Huang, Kai Bu, Hanlin Wang, Kaiwen Zhu   +3 more
openaire   +1 more source

A Survey of Android Malware

2013
In this chapter, we present a survey of existing Android malware. Particularly, with more than one year effort, we have collected a large dataset of existing Android malware. Based on this dataset, we are able to systematically characterize existing Android malware from various aspects, including their installation, activation methods, and malicious ...
Xuxian Jiang, Yajin Zhou
openaire   +1 more source