The document summarizes an evaluation of anti-malware solutions for Android. It reports that certain parts of their previous paper and testing methodology were considered flawed by vendors, so additional testing is being done. It provides details on the testing methodology used, which involved evaluating products' abilities to detect a collection of over 600 malware samples both during on-demand scans and when the samples were installed. Products were grouped into categories based on their detection rates, with the top category detecting over 90% of samples.
The document is a test report that evaluated 41 Android anti-malware solutions and grouped them into categories based on their average detection rates of malware families. The top category detected over 90% of malware and included solutions from Avast, Dr.Web, F-Secure, Ikarus, Kaspersky, Lookout, McAfee, MYAndroid Protection, NQ Mobile, and Zoner. The next category detected between 65-90% and included solutions from 13 companies. The third category detected between 40-65% and included BluePoint, G Data, and Kinetoo. The fourth category detected less than 40% and did not include major security companies.
This document presents a proposed machine learning-based Android malware detection system. It discusses how Android devices are increasingly being targeted by malware due to the open nature of the Android app marketplace. The proposed system would use machine learning classifiers to analyze permission-based features and events from Android apps to classify them as goodware or malware. It would monitor apps and detect malware to enhance security and privacy for smartphone users. The system design uses k-means clustering and naive Bayes classification on XML and DEX file features to detect malware in two layers if needed.
Tech Report: On the Effectiveness of Malware Protection on AndroidFraunhofer AISEC
This document evaluates the effectiveness of malware protection on Android devices. It conducts tests on several Android antivirus apps using known malware samples and a newly developed proof of concept malware. The tests find that most antivirus apps can be easily evaded by making only trivial alterations to malware package files. The document aims to provide a more realistic assessment of the malware risk and the level of protection offered by antivirus software compared to traditional antivirus tests.
Android Malware: Study and analysis of malware for privacy leak in ad-hoc net...IOSR Journals
This document discusses analyzing Android malware that can leak privacy information in ad-hoc networks. It proposes using static and dynamic analysis methods to detect malware. In static analysis, reverse engineering is used to detect malicious code by decompiling Android app install files. In dynamic analysis, apps are run in an emulator to monitor their network behavior using tools like Snort. Destinations are then white-listed or blacklisted based on safety. The approach is compared to third party apps and is shown to also be effective at detecting malware that uses internet permissions to leak privacy data in small datasets.
Android is a Linux based operating system used for smart phone devices. Since 2008, Android devices gained huge market share due to its open architecture and popularity. Increased popularity of the Android devices and associated primary benefits attracted the malware developers. Rate of Android malware applications increased between 2008 and 2016. In this paper, we proposed dynamic malware detection approach for Android applications. In dynamic analysis, system calls are recorded to calculate the density of the system calls. For density calculation, we used two different lengths of system calls that are 3 gram and 5 gram. Furthermore, Naive Bayes algorithm is applied to classify applications as benign or malicious. The proposed algorithm detects malware using 100 real world samples of benign and malware applications. We observe that proposed method gives effective and accurate results. The 3 gram Naive Bayes algorithm detects 84 malware application correctly and 14 benign application incorrectly. The 5 gram Naive Bayes algorithm detects 88 malware application correctly and 10 benign application incorrectly. Mr. Tushar Patil | Prof. Bharti Dhote "Malware Detection in Android Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26449.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26449/malware-detection-in-android-applications/mr-tushar-patil
Avtest Kasım 2011 Bedava Android Antivirüs AraştırmasıErol Dizdar
The document evaluates several free Android antivirus apps and their ability to detect malware. It finds that most free apps detected few or none of the malware samples tested, with detection rates from 0-10%. The best free app, Zoner AntiVirus Free, detected 32% of samples in scans and blocked the installation of 8 out of 10 malware apps. Commercial products from Kaspersky and F-Secure detected over 50% of samples in scans and blocked all malware installations. Most popular free apps like Antivirus Free provided no reliable malware protection despite millions of users trusting them.
Android mobile platform security and malware surveyeSAT Journals
Abstract As mobile devices become ubiquitous, more people and companies are readily adopting the technology to conduct day-to-day business, and are increasing the amount of personal data transmitted and stored on these devices. These devices are now part of a global infrastructure powering communication and how we do business around the world. In turn, the inherent vulnerabilities are becoming an ever more critical topic of interest and challenge as we continue to see a rapid rate of malware development. This paper is a comprehensive survey on a broad view of the growing Android community, its rapidly growing malware attacks, and security concerns. Serving to aid in the continuous challenge of identifying current and future vulnerabilities as well as incorporating security strategies against them, this survey will focus primarily on mobile devices (also known as smart phones) running the Android mobile operating system between the years of 2007 and 2013. Index Terms: mobile, Android, malware, security
Hii assessing the_effectiveness_of_antivirus_solutionsAnatoliy Tkachev
The document summarizes a study that assessed the effectiveness of antivirus software in detecting newly created malware. Some key findings include:
- The initial detection rate of new viruses by antivirus software is less than 5%, and for some vendors it can take up to 4 weeks to detect a new virus.
- Free antivirus software from Avast and Emisoft had among the best detection capabilities, though they also had high false positive rates.
- Given the low effectiveness of antivirus software, the document suggests that enterprises and consumers should consider alternative security approaches and that compliance requirements around antivirus could be eased to allow budgets to be used more effectively.
The document is a test report that evaluated 41 Android anti-malware solutions and grouped them into categories based on their average detection rates of malware families. The top category detected over 90% of malware and included solutions from Avast, Dr.Web, F-Secure, Ikarus, Kaspersky, Lookout, McAfee, MYAndroid Protection, NQ Mobile, and Zoner. The next category detected between 65-90% and included solutions from 13 companies. The third category detected between 40-65% and included BluePoint, G Data, and Kinetoo. The fourth category detected less than 40% and did not include major security companies.
This document presents a proposed machine learning-based Android malware detection system. It discusses how Android devices are increasingly being targeted by malware due to the open nature of the Android app marketplace. The proposed system would use machine learning classifiers to analyze permission-based features and events from Android apps to classify them as goodware or malware. It would monitor apps and detect malware to enhance security and privacy for smartphone users. The system design uses k-means clustering and naive Bayes classification on XML and DEX file features to detect malware in two layers if needed.
Tech Report: On the Effectiveness of Malware Protection on AndroidFraunhofer AISEC
This document evaluates the effectiveness of malware protection on Android devices. It conducts tests on several Android antivirus apps using known malware samples and a newly developed proof of concept malware. The tests find that most antivirus apps can be easily evaded by making only trivial alterations to malware package files. The document aims to provide a more realistic assessment of the malware risk and the level of protection offered by antivirus software compared to traditional antivirus tests.
Android Malware: Study and analysis of malware for privacy leak in ad-hoc net...IOSR Journals
This document discusses analyzing Android malware that can leak privacy information in ad-hoc networks. It proposes using static and dynamic analysis methods to detect malware. In static analysis, reverse engineering is used to detect malicious code by decompiling Android app install files. In dynamic analysis, apps are run in an emulator to monitor their network behavior using tools like Snort. Destinations are then white-listed or blacklisted based on safety. The approach is compared to third party apps and is shown to also be effective at detecting malware that uses internet permissions to leak privacy data in small datasets.
Android is a Linux based operating system used for smart phone devices. Since 2008, Android devices gained huge market share due to its open architecture and popularity. Increased popularity of the Android devices and associated primary benefits attracted the malware developers. Rate of Android malware applications increased between 2008 and 2016. In this paper, we proposed dynamic malware detection approach for Android applications. In dynamic analysis, system calls are recorded to calculate the density of the system calls. For density calculation, we used two different lengths of system calls that are 3 gram and 5 gram. Furthermore, Naive Bayes algorithm is applied to classify applications as benign or malicious. The proposed algorithm detects malware using 100 real world samples of benign and malware applications. We observe that proposed method gives effective and accurate results. The 3 gram Naive Bayes algorithm detects 84 malware application correctly and 14 benign application incorrectly. The 5 gram Naive Bayes algorithm detects 88 malware application correctly and 10 benign application incorrectly. Mr. Tushar Patil | Prof. Bharti Dhote "Malware Detection in Android Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26449.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26449/malware-detection-in-android-applications/mr-tushar-patil
Avtest Kasım 2011 Bedava Android Antivirüs AraştırmasıErol Dizdar
The document evaluates several free Android antivirus apps and their ability to detect malware. It finds that most free apps detected few or none of the malware samples tested, with detection rates from 0-10%. The best free app, Zoner AntiVirus Free, detected 32% of samples in scans and blocked the installation of 8 out of 10 malware apps. Commercial products from Kaspersky and F-Secure detected over 50% of samples in scans and blocked all malware installations. Most popular free apps like Antivirus Free provided no reliable malware protection despite millions of users trusting them.
Android mobile platform security and malware surveyeSAT Journals
Abstract As mobile devices become ubiquitous, more people and companies are readily adopting the technology to conduct day-to-day business, and are increasing the amount of personal data transmitted and stored on these devices. These devices are now part of a global infrastructure powering communication and how we do business around the world. In turn, the inherent vulnerabilities are becoming an ever more critical topic of interest and challenge as we continue to see a rapid rate of malware development. This paper is a comprehensive survey on a broad view of the growing Android community, its rapidly growing malware attacks, and security concerns. Serving to aid in the continuous challenge of identifying current and future vulnerabilities as well as incorporating security strategies against them, this survey will focus primarily on mobile devices (also known as smart phones) running the Android mobile operating system between the years of 2007 and 2013. Index Terms: mobile, Android, malware, security
Hii assessing the_effectiveness_of_antivirus_solutionsAnatoliy Tkachev
The document summarizes a study that assessed the effectiveness of antivirus software in detecting newly created malware. Some key findings include:
- The initial detection rate of new viruses by antivirus software is less than 5%, and for some vendors it can take up to 4 weeks to detect a new virus.
- Free antivirus software from Avast and Emisoft had among the best detection capabilities, though they also had high false positive rates.
- Given the low effectiveness of antivirus software, the document suggests that enterprises and consumers should consider alternative security approaches and that compliance requirements around antivirus could be eased to allow budgets to be used more effectively.
Technology auto protection_from_exploitКомсс Файквэе
This document provides an introduction, methodology, and results of a comparative assessment of Kaspersky Internet Security 2013 conducted by MRG Effitas in August 2012. The assessment tested Kaspersky and nine other leading antivirus/internet security applications to evaluate the effectiveness of Kaspersky's new Automatic Exploit Prevention technology at detecting exploits and protecting against zero-day vulnerabilities. The methodology used both in-the-wild exploits and samples generated by the Metasploit framework to bypass traditional detection methods and test protection against unknown threats. The full report contains the security applications tested, details of the vulnerabilities and payloads used, and conclusions about the test results.
This document summarizes a presentation on hijacking attacks on Android devices. It discusses various types of attacks such as visual spoofing, UI redressing through techniques like clickjacking and tapjacking, and the Chrome to Phone attack. It provides examples of these attacks and outlines some countermeasures to help protect against them, such as frame busters and setting filters to block obscured touch gestures. The presentation concludes by noting that UI redressing and clickjacking attacks pose serious dangers, and that more attacks are likely to emerge in the future.
Android is an extensively used mobile platform and with evolution it has also witnessed an increased influx of malicious applications in its market place. The availability of multiple sources for downloading applications has also contributed to users falling prey to malicious applications. A major hindrance in blocking the entry of malicious applications into the Android market place is scarcity of effective mechanisms to identify malicious applications. This paper presents AndroInspector, a system for comprehensive analysis of an Android application using both static and dynamic analysis techniques. And roInspector derives, extracts and analyses crucial features of Android applications using static analysis and subsequently classifies the application using machine learning techniques. Dynamic analysis includes automated execution of Android application to identify a set of pre-defined malicious actions performed by application at run-time.
WHAT IS APP SECURITY – THE COMPLETE PROCESS AND THE TOOLS & TESTS TO RUN ITTekRevol LLC
As the #tech industry continues to grow, #security concerns have become integral not only to software developers but also to consumers and entrepreneurs stepping into the field. Here's a basic guide that will help keep users, information, and programs safe.
Keyloggers are a invasive software often used to harvest secret information. One of the main reasons for
this fast growth is the possibility for unprivileged programs running in the user space to secretly steal and record all the
keystrokes typed by the users on a system. The ability to run in unprivileged mode makes possible their implementation
and distribution. but, at the same time, allows one to understand and imitate their behavior in detail.
COVERT is a tool that analyzes Android applications in a compositional manner to detect security vulnerabilities that occur due to the interaction of apps. It extracts models of individual apps and the Android framework and uses the Alloy analyzer to check the models for vulnerabilities. An evaluation on over 500 real-world apps found that COVERT can effectively detect inter-app vulnerabilities in minutes and does not require source code. It was implemented with desktop, mobile, and web-based front-ends to facilitate end-user analysis of apps.
The document discusses analyzing Android malware. It describes setting up a lab with an Android SDK virtual machine. Tools for static and dynamic analysis are outlined. The document then demonstrates analyzing a malware sample that sends SMS messages to a premium rate number, extracting the APK, decompiling the code, and identifying the malicious behavior. By reversing the malware, the author was able to determine the phone number and text messages it was sending, thus "having" the malware and being able to control it.
100 effective software testing tools that boost your TestingBugRaptors
Bugraptors always remains up to date with ongoing trends, technological changes and latest tools used in Manual Testing as well as in Automation Testing.
CopperDroid - On the Reconstruction of Android Apps BehaviorsFACE
The document discusses CopperDroid, a project that aims to reconstruct the behaviors of Android apps through dynamic analysis. It provides background on the speaker, Lorenzo Cavallaro, and his affiliations. It also discusses the MobSec project, which uses techniques like CopperDroid to analyze mobile threats and develop mitigations.
This android app testing checklist is particularly intended to test the attributes of an Android application. In this android mobile app testing checklist clearly, we tests all the important attributes of the application. For this, a different test approach and test script should make for the security testing, performance testing, usability testing and other testing types vital for your mobile app testing checklist.
This testing checklist for mobile application divided into five main elements that are:
• Device specific checks. These are attributes that identified with the gadget on which the application installed.
• Network specific checks.
• App checks. These are things to watch that need to do with functionality that usually utilized as a part of an android app testing checklist.
• App User interface checks.
• Store particular checks.
The checks don't need to executed in the request they are given.
The below given checklist contain all the testcases used to made various checklists such as mobile app security testing checklist, mobile app performance testing checklist, usability testing checklist for mobile application, mobile compatibility testing checklist etc. that helps you in understanding of how to do mobile application testing and what is your approach while testing mobile applications.
In any case, you have any inquiries or proposals; please get in touch with us at www.testorigen.com
The document discusses the results of a survey on open source software usage and security practices. Some key findings include:
- Over half of organizations have an open source policy but only two-thirds follow the policies. Top challenges are lack of enforcement and unclear expectations.
- Most organizations do not have meaningful controls over the components used in applications and many have an incomplete view of license risks.
- Few organizations actively monitor components for vulnerability changes or maintain an inventory of components used in production applications. Responsibilities for security are often unclear.
- Application security practices often lag development speeds, with security analysis rarely performed early in the process. Training availability and developer interest in security is limited.
IRJET - Research on Data Mining of Permission-Induced Risk for Android DevicesIRJET Journal
This document describes research on using data mining techniques to detect permission-induced risks in Android devices. It aims to develop a malware-free application using permission ranking, similarity-based feature selection, and association rule mining. These techniques are used to rank permissions and detect malware applications based on their permissions. The random forest algorithm is then applied to further increase the accuracy of malware detection. The proposed system detects malicious applications and notifies users, allowing them to block apps if desired. It analyzes app permissions to identify malware while improving on existing detection methods.
Evaluating android antimalware against transformation attacksIAEME Publication
This document summarizes a study that evaluated the effectiveness of 10 popular commercial Android antimalware products against common malware transformation techniques. The researchers developed a framework called DroidChameleon that applied various obfuscation techniques to known malware samples to generate new variants. They found that none of the antimalware products were resistant to these basic transformations, and many could be trivially defeated. The researchers hope their findings will motivate the security community to improve current mobile malware detection capabilities.
The document summarizes malware threats from Q1 2012. There was significant growth in PC malware, mobile malware (especially on Android), and rootkits like ZeroAccess. Signed malware and password-stealing Trojans also increased substantially. Overall, 2012 is shaping up to be a challenging year for cybersecurity as attackers continue pushing technological boundaries.
DROIDSWAN: Detecting Malicious Android Applications Based on Static Feature A...csandit
Android being a widely used mobile platform has witnessed an increase in the number of malicious samples on its market place. The availability of multiple sources for downloading
applications has also contributed to users falling prey to malicious applications. Classification of an Android application as malicious or benign remains a challenge as malicious applications maneuver to pose themselves as benign. This paper presents an approach which extracts various features from Android Application Package file (APK) using static analysis and subsequently classifies using machine learning techniques. The contribution of this work includes deriving, extracting and analyzing crucial features of Android applications that aid in efficient classification. The analysis is carried out using various machine learning algorithms
with both weighted and non-weighted approaches. It was observed that weighted approach depicts higher detection rates using fewer features. Random Forest algorithm exhibited high detection rate and shows the least false positive rate.
SYSTEM CALL DEPENDENCE GRAPH BASED BEHAVIOR DECOMPOSITION OF ANDROID APPLICAT...IJNSA Journal
This document discusses a proposed approach to map system-level behaviors of Android applications to Android APIs. The approach involves three steps: 1) obtaining an application's behavior through system-level tracking and symbolic execution, represented as System Call Dependence Graphs, 2) concurrently obtaining all Android APIs called by the application, and 3) mapping the System Call Dependence Graphs to the Android APIs based on system call entries and timestamps. This mapping could help identify potentially malicious applications trying to evade detection by avoiding direct use of Android APIs. The study shows this approach can effectively identify potential permission abuse with negligible performance impact.
This employment law update from Lander Associates provides information on recent cases and legal issues. It summarizes a case where using the nickname "Borat" for a Polish employee was found to be race discrimination. It also discusses cases related to employee accompaniment during investigations, fairness in dismissal for attending work drunk, determining employment status despite contract terms, and implications of discussing an employee's sexual orientation. Reminders are provided on the removal of default retirement ages in the UK and obligations for notifying employers of employee arrests or convictions.
St. Lawrence College is a 4-year degree and 2-3 year diploma granting institution established in 1967 with over 7,000 students enrolled in more than 150 programs, 20% of whom are first generation students and 16% have a disability. The college works closely with industry, offers work placements and internships, and boasts an 88% graduate employment rate due to its smaller class sizes, world-class teaching focused on employment skills.
The document discusses a student who is both the best and amazing. It uses repetitive language to emphasize the student's positive qualities and accomplishments. In a very concise manner, the document highlights this student's skills and achievements.
Technology auto protection_from_exploitКомсс Файквэе
This document provides an introduction, methodology, and results of a comparative assessment of Kaspersky Internet Security 2013 conducted by MRG Effitas in August 2012. The assessment tested Kaspersky and nine other leading antivirus/internet security applications to evaluate the effectiveness of Kaspersky's new Automatic Exploit Prevention technology at detecting exploits and protecting against zero-day vulnerabilities. The methodology used both in-the-wild exploits and samples generated by the Metasploit framework to bypass traditional detection methods and test protection against unknown threats. The full report contains the security applications tested, details of the vulnerabilities and payloads used, and conclusions about the test results.
This document summarizes a presentation on hijacking attacks on Android devices. It discusses various types of attacks such as visual spoofing, UI redressing through techniques like clickjacking and tapjacking, and the Chrome to Phone attack. It provides examples of these attacks and outlines some countermeasures to help protect against them, such as frame busters and setting filters to block obscured touch gestures. The presentation concludes by noting that UI redressing and clickjacking attacks pose serious dangers, and that more attacks are likely to emerge in the future.
Android is an extensively used mobile platform and with evolution it has also witnessed an increased influx of malicious applications in its market place. The availability of multiple sources for downloading applications has also contributed to users falling prey to malicious applications. A major hindrance in blocking the entry of malicious applications into the Android market place is scarcity of effective mechanisms to identify malicious applications. This paper presents AndroInspector, a system for comprehensive analysis of an Android application using both static and dynamic analysis techniques. And roInspector derives, extracts and analyses crucial features of Android applications using static analysis and subsequently classifies the application using machine learning techniques. Dynamic analysis includes automated execution of Android application to identify a set of pre-defined malicious actions performed by application at run-time.
WHAT IS APP SECURITY – THE COMPLETE PROCESS AND THE TOOLS & TESTS TO RUN ITTekRevol LLC
As the #tech industry continues to grow, #security concerns have become integral not only to software developers but also to consumers and entrepreneurs stepping into the field. Here's a basic guide that will help keep users, information, and programs safe.
Keyloggers are a invasive software often used to harvest secret information. One of the main reasons for
this fast growth is the possibility for unprivileged programs running in the user space to secretly steal and record all the
keystrokes typed by the users on a system. The ability to run in unprivileged mode makes possible their implementation
and distribution. but, at the same time, allows one to understand and imitate their behavior in detail.
COVERT is a tool that analyzes Android applications in a compositional manner to detect security vulnerabilities that occur due to the interaction of apps. It extracts models of individual apps and the Android framework and uses the Alloy analyzer to check the models for vulnerabilities. An evaluation on over 500 real-world apps found that COVERT can effectively detect inter-app vulnerabilities in minutes and does not require source code. It was implemented with desktop, mobile, and web-based front-ends to facilitate end-user analysis of apps.
The document discusses analyzing Android malware. It describes setting up a lab with an Android SDK virtual machine. Tools for static and dynamic analysis are outlined. The document then demonstrates analyzing a malware sample that sends SMS messages to a premium rate number, extracting the APK, decompiling the code, and identifying the malicious behavior. By reversing the malware, the author was able to determine the phone number and text messages it was sending, thus "having" the malware and being able to control it.
100 effective software testing tools that boost your TestingBugRaptors
Bugraptors always remains up to date with ongoing trends, technological changes and latest tools used in Manual Testing as well as in Automation Testing.
CopperDroid - On the Reconstruction of Android Apps BehaviorsFACE
The document discusses CopperDroid, a project that aims to reconstruct the behaviors of Android apps through dynamic analysis. It provides background on the speaker, Lorenzo Cavallaro, and his affiliations. It also discusses the MobSec project, which uses techniques like CopperDroid to analyze mobile threats and develop mitigations.
This android app testing checklist is particularly intended to test the attributes of an Android application. In this android mobile app testing checklist clearly, we tests all the important attributes of the application. For this, a different test approach and test script should make for the security testing, performance testing, usability testing and other testing types vital for your mobile app testing checklist.
This testing checklist for mobile application divided into five main elements that are:
• Device specific checks. These are attributes that identified with the gadget on which the application installed.
• Network specific checks.
• App checks. These are things to watch that need to do with functionality that usually utilized as a part of an android app testing checklist.
• App User interface checks.
• Store particular checks.
The checks don't need to executed in the request they are given.
The below given checklist contain all the testcases used to made various checklists such as mobile app security testing checklist, mobile app performance testing checklist, usability testing checklist for mobile application, mobile compatibility testing checklist etc. that helps you in understanding of how to do mobile application testing and what is your approach while testing mobile applications.
In any case, you have any inquiries or proposals; please get in touch with us at www.testorigen.com
The document discusses the results of a survey on open source software usage and security practices. Some key findings include:
- Over half of organizations have an open source policy but only two-thirds follow the policies. Top challenges are lack of enforcement and unclear expectations.
- Most organizations do not have meaningful controls over the components used in applications and many have an incomplete view of license risks.
- Few organizations actively monitor components for vulnerability changes or maintain an inventory of components used in production applications. Responsibilities for security are often unclear.
- Application security practices often lag development speeds, with security analysis rarely performed early in the process. Training availability and developer interest in security is limited.
IRJET - Research on Data Mining of Permission-Induced Risk for Android DevicesIRJET Journal
This document describes research on using data mining techniques to detect permission-induced risks in Android devices. It aims to develop a malware-free application using permission ranking, similarity-based feature selection, and association rule mining. These techniques are used to rank permissions and detect malware applications based on their permissions. The random forest algorithm is then applied to further increase the accuracy of malware detection. The proposed system detects malicious applications and notifies users, allowing them to block apps if desired. It analyzes app permissions to identify malware while improving on existing detection methods.
Evaluating android antimalware against transformation attacksIAEME Publication
This document summarizes a study that evaluated the effectiveness of 10 popular commercial Android antimalware products against common malware transformation techniques. The researchers developed a framework called DroidChameleon that applied various obfuscation techniques to known malware samples to generate new variants. They found that none of the antimalware products were resistant to these basic transformations, and many could be trivially defeated. The researchers hope their findings will motivate the security community to improve current mobile malware detection capabilities.
The document summarizes malware threats from Q1 2012. There was significant growth in PC malware, mobile malware (especially on Android), and rootkits like ZeroAccess. Signed malware and password-stealing Trojans also increased substantially. Overall, 2012 is shaping up to be a challenging year for cybersecurity as attackers continue pushing technological boundaries.
DROIDSWAN: Detecting Malicious Android Applications Based on Static Feature A...csandit
Android being a widely used mobile platform has witnessed an increase in the number of malicious samples on its market place. The availability of multiple sources for downloading
applications has also contributed to users falling prey to malicious applications. Classification of an Android application as malicious or benign remains a challenge as malicious applications maneuver to pose themselves as benign. This paper presents an approach which extracts various features from Android Application Package file (APK) using static analysis and subsequently classifies using machine learning techniques. The contribution of this work includes deriving, extracting and analyzing crucial features of Android applications that aid in efficient classification. The analysis is carried out using various machine learning algorithms
with both weighted and non-weighted approaches. It was observed that weighted approach depicts higher detection rates using fewer features. Random Forest algorithm exhibited high detection rate and shows the least false positive rate.
SYSTEM CALL DEPENDENCE GRAPH BASED BEHAVIOR DECOMPOSITION OF ANDROID APPLICAT...IJNSA Journal
This document discusses a proposed approach to map system-level behaviors of Android applications to Android APIs. The approach involves three steps: 1) obtaining an application's behavior through system-level tracking and symbolic execution, represented as System Call Dependence Graphs, 2) concurrently obtaining all Android APIs called by the application, and 3) mapping the System Call Dependence Graphs to the Android APIs based on system call entries and timestamps. This mapping could help identify potentially malicious applications trying to evade detection by avoiding direct use of Android APIs. The study shows this approach can effectively identify potential permission abuse with negligible performance impact.
This employment law update from Lander Associates provides information on recent cases and legal issues. It summarizes a case where using the nickname "Borat" for a Polish employee was found to be race discrimination. It also discusses cases related to employee accompaniment during investigations, fairness in dismissal for attending work drunk, determining employment status despite contract terms, and implications of discussing an employee's sexual orientation. Reminders are provided on the removal of default retirement ages in the UK and obligations for notifying employers of employee arrests or convictions.
St. Lawrence College is a 4-year degree and 2-3 year diploma granting institution established in 1967 with over 7,000 students enrolled in more than 150 programs, 20% of whom are first generation students and 16% have a disability. The college works closely with industry, offers work placements and internships, and boasts an 88% graduate employment rate due to its smaller class sizes, world-class teaching focused on employment skills.
The document discusses a student who is both the best and amazing. It uses repetitive language to emphasize the student's positive qualities and accomplishments. In a very concise manner, the document highlights this student's skills and achievements.
The document provides an employment law update from Lander Associates' HR division. It summarizes new guidance from Acas on managing staff during the Olympics, changes to agency worker regulations and minimum wage rates coming into effect next month. It also summarizes two court cases, one where a sick employee was entitled to carry over statutory holiday pay and one where dismissals for driving without licenses were deemed fair.
This document provides 10 tips for brands using WeChat official accounts to build audiences. The tips include making headlines count, segmenting audiences, increasing relevance of content, being more compelling, providing incentives and rewards, using more visual storytelling, linking to other social media, inviting guest editors, turning questions into content, and creating content on location. It emphasizes the importance of high-quality, relevant, visual content that engages audiences and drives action. It also recommends tools like CMS/CRM systems to better segment and target audiences with customized content.
This document summarizes the results of testing various anti-malware solutions for Android. It tested the solutions using 618 malicious Android applications and reported the detection rates. Some solutions were able to scan the entire device storage for malware, while others could only scan installed applications and files. The testing was performed on both emulators and real Android devices to verify the results. The document analyzes the detection rates of each solution at the family level to provide more insight than just an overall detection percentage. This allows identifying weaknesses in detecting specific malware families.
The document is a test report that evaluated 41 Android anti-malware solutions and grouped them into categories based on their average detection rates of malware families. The top category detected over 90% of malware and included solutions from Avast, Dr.Web, F-Secure, Ikarus, Kaspersky, Lookout, McAfee, MYAndroid Protection, NQ Mobile, and Zoner. The next category detected between 65-90% and included solutions from 13 companies. The third category detected between 40-65% and included BluePoint, G Data, and Kinetoo. The fourth category detected less than 40% and the final category did not detect anything.
The rise of android malware and efficiency of Anti-VirusDaniel Adenew
This document summarizes research into the rise of Android malware and the effectiveness of antivirus software. The research found a 472% increase in identified Android malware between June and November 2011. Two studies tested antivirus software's ability to detect malware installed before and after the antivirus. In the first study, two of six antivirus programs could detect and disable malware. In the second study, only two could detect malware installed after. A larger second study of 41 antivirus programs against 618 malware packages found detection rates varied greatly, with some detecting over 90% and others less than 40% or nothing. The conclusion is that not all antivirus software effectively prevents or removes Android malware.
Panda Security provides unified malware protection technologies through products like TruPrevent host-based intrusion prevention system and Collective Intelligence. TruPrevent uses behavioral analysis and deep packet inspection to detect and block unknown threats while Collective Intelligence automates malware analysis through a global network of sensors to consistently deliver fast responses. Panda also offers security appliances and services like MalwareRadar, TrustLayer Mail, and solutions for mobile operators and enterprises to provide comprehensive protection.
Stephanie Vanroelen - Mobile Anti-Virus apps exposedNoNameCon
Talk by Stephanie Vanroelen at NoNameCon 2019.
https://nonamecon.org
https://cfp.nonamecon.org/nnc2019/talk/ZFJFW8/
This talk is about top anti-virus apps on Mobile. An in depth look on how they work and what they do. Do they add to or break the security of the mobile OS?
This talk is about top anti-virus apps on Android. An in-depth look at how they work and what they do.
The focus will be on the top 5 android apps:
Kaspersky Mobile Antivirus
Avast Mobile Security
Norton Security & Antivirus
Sophos Mobile Security
Security Master
This talk will try to answer the following questions: Do they add to or break the security of the Android sandbox system? What type of information is being shared back to the company (if any)? Are these apps well built?
Finally, I will address the following: Do I recommend any of these apps and if so which one and why?
This document summarizes a review of behavior-based malware analysis for Android. It discusses existing stochastic epidemic models for malware detection that are complex. The proposed system abstracts program behaviors and compares them to reference malware behaviors to detect suspicious activities. It analyzes apps, represents them as trace languages abstracted according to behavior patterns, and detects malware by comparing to a malware database. The system gets installed apps, running tasks, extracts permission information, and detects malware to help users identify potentially malicious apps.
Mobile apps have become integral to work and personal life, but securing them is challenging due to device and OS fragmentation, lack of standardized security tools, and shortage of experienced professionals. A comprehensive three-pronged strategy is needed to secure wireless connections, protect against threats across OS versions, and safeguard data and devices through encryption and remote access features. Outsourcing to an expert partner can help enterprises overcome these security challenges and accelerate app development.
The document discusses malware improvements on Android OS. It provides an introduction to the growth of smartphones and Android's dominance of the market. It then covers the organization of the paper and defines malware. It reviews the Android OS architecture and literature on Android security. The objectives are to increase awareness of the Android security model and analyze malware development. The findings show Android security relies on user awareness and the open source nature makes it vulnerable. Future scopes include modifying the permission model and alpha testing apps for the Play Store.
This document discusses a tool called ALTERDROID that analyzes Android applications to detect malware. ALTERDROID uses static and dynamic analysis techniques to detect obfuscated malware hidden in applications. It analyzes applications that are installed on a device to identify any malicious components. If malware is found, it asks the user for permission to uninstall the application. For unauthorized users attempting to access the device, it captures their image and sends it by email. The document compares this approach to existing static-only malware analysis techniques, which can miss hidden malicious components.
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IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
ANDROID UNTRUSTED DETECTION WITH PERMISSION BASED SCORING ANALYSISijitcs
Android smart phone is one of the fast growing mobile phones and because of these it the one of the most preferred target of malware developer. Malware apps can penetrate the device and gain privileges in which it can perform malicious activities such reading user contact, misusing of private information such as sending SMS and can harm user by exploiting the users private data which is stored in the device. The study is about implementation of detecting untrusted on android applications, which would be the basis of all future development regarding malware detection.
The smartphone users worldwide are not aware of the permissions as the basis of all malicious activities that could possibly operate in an android system and may steal personal and private information. Android operating system is an open system in which users are allowed to install application from any unsafe sites. However permission mechanism of and android system is not enough to guarantee the invulnerability of the application that can harm the user. In this paper, the permission scoring-based analysis that will scrutinized the installed permission and allows user to increase the efficiency of Android permission to inform user about the risk of the installed Android application, in this paper, the framework that would classify the level of sensitivity of the permission access by the application. The framework uses a formula that will calculate the sensitivity level of the permission and determine if the installed application is untrusted or not. Our result show that, in a collection of 26 untrusted application, the framework is able to correct and determine the application's behavior consistently and efficiently.
The document is an issue of the (IN)SECURE Magazine. It includes the following articles:
- A summary of key findings from a Trustwave report analyzing 450 data breaches.
- Details of a breach at Bit9 where attackers stole certificates and used them to sign malware.
- An announcement of new features in QualysGuard WAS 3.0 including malware detection and attack proxy support.
The number of devices running with the Android operating system has been on the rise. By the end of 2012, it will account for nearly half of the world's smartphone market. Along with its growth, the importance of security has also risen. A proportional increase in the number of vulnerabilities is also happening to the extent that there are a limited number of security applications available to protect these devices. The efficacies of these applications have not been empirically established. These slides analyzes some of the security tools written for the Android platform to gauge their effectiveness at mitigating spyware and malware
The document provides a summary of the results of a test of various home anti-virus protection programs. It tested the programs' ability to protect against internet threats from October to December 2012 and how they handled legitimate software.
The key points are:
- Paid security suites' effectiveness varied widely, but all beat Microsoft's free Security Essentials. Nearly every product was compromised by at least one threat.
- Blocking malicious sites based on reputation is effective, as products that prevented visiting malicious sites gained an advantage over those facing downloaded malware.
- Some programs were too harsh in evaluating legitimate software, with Trend Micro blocking the most legitimate apps at 21. Norton Internet Security was the most accurate overall
Top Mobile Application Penetration Testing Tools for Android and iOS.pdfElanusTechnologies
Mobile application penetration testing is used to evaluate the security of native mobile apps developed for Android and iOS. It involves testing data security both at rest and in transit, as well as identifying vulnerabilities using automated tools and manual techniques. Penetration testing can locate flaws in code, systems, applications, databases, and APIs to harden apps and prevent hackers from exploiting vulnerabilities. The document provides a list of important mobile application penetration testing tools for Android and iOS.
This document discusses machine learning approaches for Android malware detection. It begins with an abstract discussing signature-based and behavior-based malware detection techniques, and how machine learning can be used to detect unknown malware. The document then discusses related work on Android malware detection using machine learning algorithms. It describes detecting malware using file permissions and features extracted from Android applications. Various machine learning algorithms are trained on datasets of benign and malicious applications, and their performance is evaluated based on accuracy, classification reports, and confusion matrices.
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The document examines major software vulnerabilities and exploits from 2017-2018, including EternalBlue, WannaCryptor, CoinMiner, Diskcoder (aka Petya), and Meltdown/Spectre. It discusses how the number of reported vulnerabilities reached a historic peak in 2017, with the number of high severity vulnerabilities increasing by 68% from 2016. Exploits like EternalBlue were utilized by ransomware like WannaCryptor to devastating effect by taking advantage of vulnerabilities in older, unpatched systems. The risk posed by vulnerabilities underscores the need for multilayered endpoint security through timely patching and protection layers.
This document provides an overview of the five major threats to mobile security and best practices for dealing with them. The threats are: 1) system vulnerabilities, 2) root access and configuration changes, 3) repackaged and fake apps, 4) trojans and malware, and 5) man-in-the-middle attacks. For each threat, the document discusses how it can challenge security and outlines best practices such as using behavioral analysis, sandboxing apps, and validating secure connections. It emphasizes using a comprehensive mobile security solution that identifies threats across devices, networks, and apps to effectively protect data on mobile devices.
Similar to Avtest 2012 02-android_anti-malware_report_english (20)
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2. Anti-Malware solutions for Android
Update 09th March 2012:
It has been brought to our attention that certain parts in our
paper and the testing methodology are considered
imprecise and/or flawed by third parties.
Therefore we are now in contact with the reporting
parties/vendors and performing additional tests to sort out
any of those issues and will provide an updated version of
the report as soon as possible.
We would like to thank MYMobileSecurity
(MYAndroidProtection), NQmobile (Netqin) and Total
Defense for their feedback on this.
5. Anti-Malware solutions for Android
1. Introduction
The Smartphone market grew enormously over the last five years and the mobile malware evolved
rapidly, too. Right now there are over 450.000 apps in the Android market, where as there were less
than 100.000 in July 2010 1. This makes it the fastest growing software market overall. With the rise
of new apps, the number of malware increases as well. Figure 1 shows the growth of the AV-TEST
Android malware collection. The increasing curve is similar to what we've seen for PC malware in the
last years. The threats for Android include Phishing- and Banking-Trojans, Spyware, Bots, Root
Exploits, SMS Fraud, Premium Dialers and Fake Installers. There have also been reports about
Download-Trojans – apps that download their malicious code after installation – which means that
these apps can't be easily detected by Google's Bouncer technology 2 during publication in the Google
Android Market. Our collection used for this test contains more than 20 different Android malware
families, which cover each of the previously named threats.
Android Malware Collection Growth
14000
12000
10000
8000
6000
4000
2000
0
New Samples Total # Samples
Figure 1: Android malware collection growth since January 2011
In November 2011 we’ve revealed that many Antivirus apps, which are available for free in Google's
Android Market, don't provide a sufficient malware protection for your Android mobile. This time we
are trying to cover the good and the bad and started reviewing as many Android Anti-Malware apps
as we could find, regardless whether an app requires a specific Android version or device. These apps
include free and non-free programs, intended for personal use. This report aims to give an
impression of the malware detection rates. As an independent test institute, we aren't in the position
to recommend a specific product, but you can certainly use our report to find your personal favorite.
However please bear in mind, that malware may not the only or the most important threat to your
1
<http://en.wikipedia.org/wiki/Android_operating_system>
2
Google's Bouncer technology checks apps for malware during publication in Google's Android Market
<http://googlemobile.blogspot.com/2012/02/android-and-security.html>
3
6. Anti-Malware solutions for Android
device. Even if a product scores poorly in malware detection it may have other convenient features,
such as remote lock and wipe, backup and phone locating, that make it useful for your purposes. It is
also possible to run two or more security apps on your device at the same time, using only the best
features of the single apps.
4
7. Anti-Malware solutions for Android
2. Test report
The large number of tested apps required a scalable test environment, so we decided to use the
Android emulator supplied by the Android SDK as basis for the review. The emulator has some
advantages in contrast to a real device. There is root-access without exploiting the device and you
can easily switch between API versions and screen sizes. It has also some disadvantages. You don't
have a real phone number, which might be required to activate an app through SMS, and the
emulated 3G connection may have a too high latency for querying the cloud of some vendors. While
the advantages of the emulator make testing more comfortable, the disadvantages limit the number
of apps, which could be properly tested. To get around this limitation, the apps, which didn't work in
the emulator, were tested on a real device and all emulator results were cross checked and verified
on a real device. The emulator was set up with API level 10 (Gingerbread, Version 2.3) and for non-
emulated testing we used a Samsung GalaxyTab (GT-P1010) with Froyo (Version 2.2) and a Samsung
Galaxy Nexus (GT-I9250) with Ice Cream Sandwich (Version 4). The products were updated to their
latest available versions/signature updates and were allowed to connect to their cloud during the
test. The real devices were flashed to factory default settings after every test to provide each product
the same clean environment.
Among the tested apps we saw two different approaches for the on-demand scan. While many apps
simply scan the complete device storage, some other apps scan installed apps and important files
only. The latter were not able to scan the malware set with 618 malicious APK-files as it was stored
on the SD card. Therefore, we tested the real-time protection feature of those apps instead. That
means that all malware apps in our sample set were installed on a device or emulator one by one.
After an app has been installed, the tester waited for feedback of the real-time protection, which
should pop up if it finds a malicious app. In case of an undetected sample, it was uninstalled
manually. This is a time consuming approach and may not work in the future with larger sample sets
(see Fig. 1).
Regarding the detection rates, it makes no difference whether a malicious app is detected by an on-
demand scan or by the real-time scan, when the app is installed. From the testers' point of view, an
on-demand scan with many samples is much easier to realize than an on-access scan. However from
the user’s point of view the only criterion is protection, no matter at which point and how this takes
place.
After an on-demand scan has been completed and all detections were removed the testers saved the
remaining files, because the reporting abilities weren't consistent among all apps. The files that were
left over and have not been modified were flagged as "not detected". In case of the on-access
testing, the testers wrote their own report since the samples were tested one by one. With the
knowledge of which specific files have been detected by a scanner, we were able to analyze the scan
results based on malware families. The family based analysis can help vendors to improve the
protection for malware families with low detection rates. If the results would only provide a total,
absolute detection rate, it would be impossible to notice if an app that scored well missed an entire
malware family or not. So this way of displaying the results gives both the reader and the vendor
much more insight. Furthermore this helps to decide whether a product that doesn’t score 100% is
still a good choice, e.g. because it misses on a malware family that is no threat to a specific user
group or environment.
5
8. Anti-Malware solutions for Android
In this report no exact detection rates are given, instead the products are grouped into five different
categories, referring to different ranges of detections (Fig. 2 and Fig. 3). The first category contains
products that detected over 90%, the second category 90% to 65%, the third 65% to 40%, the fourth
everything less than 40% but above 0% and finally the last group contains the products that didn’t
detect anything.
VERYGOOD GOOD SATISFYING SUFFICIENT NULL
> 90% > 65% > 40% > 0% 0%
Figure 2: Detection rate legend
There are several reasons for doing that:
1. The number of malware samples is still fairly small
2. Determining the prevalence of malware apps is difficult
3. Malware apps are quickly removed from the market (and even remotely from the device)
This all comes down to one issue: It can happen very easily that a sample set is distorted by samples
that are not really relevant anymore or were never at all. It is impossible for us to measure the
prevalence of malware apps. It is also not possible to determine when and how long they have been
a threat to the user. Therefore we identified the most widely known malware families and primarily
used those for the test. Only malicious apps that we have discovered between August and December
2011 have been included in the test set. A few further malicious apps which don’t belong to the
listed families have been put in a category called “Other” and represent other families. Even with
those precautions it is possible that malware samples that are not suitable for this test are included.
Already 30 wrongly chosen samples could change the result by 5%. In order to avoid too heavy
effects from these issues, the results are categorized. However, by looking at the individual family
detections it is still possible to get a fairly accurate picture of the absolute detection rate.
The products were distributed over all detection ranges as shown in Figure 3.
Detection rate distribution
6 7
12 10
6
> 90% > 65% > 40% > 0% 0%
Figure 3: Detection rate distribution
6
9. Anti-Malware solutions for Android
3. Test results
During February 2012 we tested 41 Product Average Family Detection
different Android Anti-Malware solutions. avast! Free Mobile Security
Dr.Web anti-virus Light
The results are shown in Figure 4. Please F-Secure Mobile Security
>90%
note that the products in a certain category IKARUS mobile.security LITE
are sorted alphabetically, so this listing is Kaspersky Mobile Security (Lite)
Lookout Security & Antivirus
not a ranking! Mostly traditional anti-virus Zoner AntiVirus Free
vendors are in the top range of the overall AegisLab Antivirus Free
detection results. Exceptions are Zoner and AVG Mobilation Anti-Virus Free
Bitdefender Mobile Security
Lookout which also make it into the top
ESET Mobile Security
group. Using these products you don’t have Norton Mobile Security Lite
>65%
to worry about your protection. Products Quick Heal Mobile Security
Super Security
with a detection rate between 90% and
Trend Micro Mobile Security
65% are still very good and could move to Vipre Mobile Security (BETA)
the top range depending on changes to the Webroot SecureAnywhere Mobile
tested malware set. Some of these products BullGuard Mobile Security
Comodo Mobile Security
just miss one or two malware families, G Data MobileSecurity
>40%
which might be not prevalent in certain McAfee Mobile Security
environments anyway. Again, there are only NQ Mobile Security
Total Defense Mobile Security
two products from specialized mobile
ALYac Android
security vendors: AegisLab and Super Antivirus Free
Security. All other products in this group BlackBelt AntiVirus
BluePoint Security Free
come from vendors well known in the
CMC Mobile Security
Desktop IT. Bullguard, Comodo, G Data, Fastscan Anti-Virus Free
>0%
McAfee, NetQin and Total Defense are in GuardX Antivirus
the third range. These vendors may not yet Kinetoo Malware Scan
MobiShield Mobile Security
have a sufficient infrastructure to collect a Privateer LITE
broad range of malware or they focus on a Snap Secure
local market. They provide reliable malware TrustGo Mobile Security
Android Antivirus
protection against a few families, but have Android Defender
trouble with some others. It can be LabMSF Antivirus beta
0%
expected that these products will improve MobileBot Antivirus
MT Antivirus
once they broaden their sample acquiring.
MYAndroid Protection Antivirus
The fourth group doesn’t contain any Figure 4: Average detection rate per malware family
traditional anti-virus vendor and include the
products which also failed in our last report.
We’ve reviewed six more products which are listed in the last category. We could not clearly
determine whether they scanned the malware set correctly or not or whether they are able to detect
anything at all. This means that we haven’t seen any detection, neither on our widely known samples
nor on the EICAR test file 3. Even in the on-access tests these products had no detections. So it is safe
to assume that these products really don’t detect anything, but we still wanted to point out the
possibility of a flaw in our testing methodology.
3
The EICAR test file can be used to determine whether an anti-malware software is operational or not and can
be obtained here <http://www.eicar.org/86-0-Intended-use.html>
7
10. Anti-Malware solutions for Android
The malware family based analysis in Figure 5 shows that some products miss the top group only due
to their low detection of one or two malware families. You can expect better signatures for these
families to be added in the near future. The detection of specific families can also depend on each
vendor’s definition of malware. Some families might only be annoying advertisement apps, while
others include real malicious code, which can lead to monetary damage or data loss. Therefore some
vendors may decide to not detect certain potentially unwanted, but not clearly malicious, apps.
Average Family Detection
Exploit.Lotoor
Glodream
BaseBrid
DorDrae
FakeInst
Geinimi
Nickspy
KungFu
Opfake
Rooter
Gonca
Xsider
SerBG
Other
Boxer
Jifake
Kmin
Adrd
Yzhc
avast! Free Mobile Security
Dr.Web anti-virus Light
F-Secure Mobile Security
IKARUS mobile.security LITE
Kaspersky Mobile Security (Lite)
Lookout Security & Antivirus
Zoner AntiVirus Free
AegisLab Antivirus Free
AVG Mobilation Anti-Virus Free
Bitdefender Mobile Security
ESET Mobile Security
Norton Mobile Security Lite
Quick Heal Mobile Security
Super Security
Trend Micro Mobile Security
Vipre Mobile Security (BETA)
Webroot SecureAnywhere Mobile
BullGuard Mobile Security
Comodo Mobile Security
G Data MobileSecurity
McAfee Mobile Security
NQ Mobile Security
Total Defense Mobile Security
ALYac Android
Antivirus Free
BlackBelt AntiVirus
BluePoint Security Free
CMC Mobile Security
Fastscan Anti-Virus Free
GuardX Antivirus
Kinetoo Malware Scan
MobiShield Mobile Security
Privateer LITE
Snap Secure
TrustGo Mobile Security
Android Antivirus
Android Defender
LabMSF Antivirus beta
MobileBot Antivirus
MT Antivirus
MYAndroid Protection Antivirus
Figure 5: Detection by malware family
8
11. Anti-Malware solutions for Android
4. Testing issues
Despite the fact that some apps weren’t able to scan our sample set on the SD card and therefore
have to be tested in a time consuming on-access test, we were also faced with apps which couldn’t
delete all detections automatically. They didn’t even provide a "Do it! And never ask me again!"
option in the case of more than one malware detection. This fact led to testers clicking a "remove"-
button several hundred times. While such options are very common in desktop applications, they
aren't in the Android world yet. Also scan reports couldn't be saved within most of the tested apps.
Some apps use SQLite databases to save their scan results and we were able to collect the
corresponding db-files from the emulators only. As accessing those files requires root privileges, they
weren't collected from the real devices. The average user shouldn't miss such features, as its device
should never be infected with hundreds of malicious apps, but those simple functions would make a
testers life much easier.
As pointed out before, there are also apps which use their cloud to detect malware. While this
worked flawlessly with most products, both in emulated environments as well as on a real device
there were a few exceptions. We have seen products that were not able to query their cloud in the
emulator at all, even if full internet access was provided. There were also products that did have
some trouble on a real device. This might be due to latency issues and could only be resolved by
repeated tests until no further problems occurred.
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12. Anti-Malware solutions for Android
5. Conclusion
Even if Google now checks all apps on its Android Market, you should consider installing a security
app, because nowadays the malware authors are able to load their malicious code after a seemingly
clean app has been installed. Regarding the detection rates, you can trustfully choose from at least
17 products to protect your Android device. What you should also have in mind when choosing your
mobile security app are additional functions such as backup and anti-theft protection (e.g. find your
lost device or wipe all data remotely).
To keep your device free of malware even without a security app, you should install apps only from
trusted sources, like the Google Android Market or the Amazon Appstore for Android. Read the
comments carefully and check whether the required permissions are reasonable (e.g. a game usually
shouldn't need the permission to read or write SMS unless its description lists the specific features
using these permissions). As it may take between two to four weeks until Google removes malicious
apps from its Android Market, you should also be careful with new apps on the market. Wait until
apps are well-established, e.g. they were downloaded several thousand times and have many good
ratings, or visit the developer’s website, which should at least provide contact information.
In most cases when there is a free (often called Lite) and a paid version, the malware detection
capabilities are the same. So if you are just looking at the detection rates, you can take the Lite result
and apply this to the paid version and vice versa. Another finding of the test is, that the well known
Desktop IT vendors perform above the average. Even the worst products from those vendors are still
better than most of the specialized mobile security software vendors.
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13. Anti-Malware solutions for Android
6. Product details
Product Vendor Android Package 4 Version
AegisLab Antivirus Free AegisLab com.aegislab.sd3prj.antivirus.free 1.0.4
ALYac Android ESTsoft com.estsoft.alyac 1.2.5.0
Android Antivirus Android Antivirus and.anti 1.6
Antivirus Free Creative Apps com.zrgiu.antivirus 1.3.1
Android Defender AndroidAppTools com.virusshield.android 1.1
avast! Free Mobile Security AVAST com.avast.android.mobilesecurity 1.0.1282
AVG Mobilation Anti-Virus Free AVG Mobilation com.antivirus 2.10
Bitdefender Mobile Security BitDefender com.bitdefender.security 1.1.483
BlackBelt AntiVirus BlackBelt SmartPhone Defence com.blackbelt.antivirus 2.2.0002
BluePoint Security Free BluePoint Security bluepointfree.ad 4.0.17
BullGuard Mobile Security BullGuard com.smobile.securityshield.android.bullgard 10.0.22.14023
CMC Mobile Security CMC InfoSec com.cmcinfosec.mobilesec 2.1
Comodo Mobile Security Comodo Security Solutions com.comodo.pimsecure 1.1.16984.2
Dr.Web anti-virus Light Doctor Web com.drweb 6.01.5
ESET Mobile Security ESET com.eset.emsw 1.0.288.223
Fastscan Anti-Virus Free K-TEC jp.ktinc.fastscan 1.1.5
F-Secure Mobile Security F-Secure com.fsecure.browser 7.6.08787
G Data MobileSecurity G Data de.gdata.mobilesecurity 23.2.17613
GuardX Antivirus QStar org.qstar.guardx 2.3
IKARUS mobile.security LITE IKARUS Security Software com.ikarus.mobile.security 0.9.8.9008
Kaspersky Mobile Security (Lite) Kaspersky Lab com.kms 9.10.106
Kinetoo Malware Scan CPU Media SARL com.cpumedia.android.kinetoo 1.7.1
LabMSF Antivirus beta LabMSF com.ReSync.RNGN 1.0
Lookout Security & Antivirus Lookout Mobile Security com.lookout 7.1
McAfee Mobile Security McAfee com.wsandroid.suite 1.2.0.141
MobileBot Antivirus Desktop Shark avm.defender 1.05
MobiShield Mobile Security trustmobi com.trustmobi.MobiShield 3.1.5
MT Antivirus KissDroid com.hot.free.defence.main 1.0.8
MYAndroid Protection Antivirus MYMobileSecurity com.mymobileprotection20 4.2.18.36
Norton Mobile Security Lite NortonMobile com.symantec.mobilesecurity 2.5.0.392
NQ Mobile Security NetQin Mobile com.nqmobile.antivirus20 6.0.06.08
Privateer LITE Privateer Labs com.privateer.lite 2.1.4
Quick Heal Mobile Security Quick Heal Technologies com.quickheal.platform 1.01.017
Snap Secure Exclaim Mobility com.exclaim.snapsecure.app 6.45
Super Security Superdroid.net com.superdroid.security2 1.04
Trend Micro Mobile Security Trend Micro com.trendmicro.tmmspersonal 2.1
TrustGo Mobile Security TrustGo Mobile com.trustgo.security.beta 0.8.5
Total Defense Mobile Security Total Defense com.tdi.security 3.0.3.16256
Vipre Mobile Security (BETA) GFI Software com.ssd.vipre 1.0.231
Webroot SecureAnywhere Mobile Webroot com.webroot.security 2.2.1.1046
Zoner AntiVirus Free ZONER com.zoner.android.antivirus 1.2.10
Figure 6: Product details of all products listed in the test results
4
The Android package name is unique among all apps in the Google Android Market. You can use it as search
term if you want to install a specific program from the Android Market.
11
14. Anti-Malware solutions for Android
AegisLab Antivirus Free belongs ALYac Android is a free Mobile Android Antivirus showed no
to the second range with its Security. It has a clear user detections in our tests and
detection rate between 65% and interface but the detection rates crashed several times. The
90%. It has additional Anti-Theft need to improve. advertisements worked properly.
functions in the Elite Version.
Antivirus Free just detects a avast! Free Mobile Security is AVG Mobilation Anti-Virus Free is
handful of samples in the test set. available for free, easy to use and a good choice to secure your
It shows advertisements at the has many features to protect your phone, being in the second group
bottom of the screen. device. With its very good of detection rates. It also provides
detection rate it is one of the best Anti-Theft functions.
security products for your Android
device.
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15. Anti-Malware solutions for Android
The premium version of BlackBelt AntiVirus is simple to BluePoint Security Free uses a
Bitdefender Mobile Security use. However the poor detection clear user interface but has a low
includes a variety of other useful rate doesn’t excuse to pay for the detection rate with its cloud scan
functions in addition to the good product after the trial period has engine.
malware and privacy scanner. expired.
BullGuard Mobile Security The free CMC Mobile Security Comodo Mobile Security provides
contains Parental Control and seems to be out of date. The latest statistics at its home screen and
Backup beside its virus scanner. signatures are several months old. provides fair malware detection.
13
16. Anti-Malware solutions for Android
Dr.Web anti-virus Light has very ESET Mobile Security provides a Fastscan Anti-Virus Free covers all
good detection rates. You need good to very good malware malware families but the
the premium version to use Anti- detection and extended Anti-Theft signatures still need to enhance.
Theft and Anti-Spam features. functions.
F-Secure Mobile Security has one G Data MobileSecurity scans on- GuardX Antivirus displays
of the best test results. F-Secure demand and periodically with a advertisements. It has no real
offers a comprehensive package satisfactory detection rate. You advantage over using no virus
with Anti-Theft and Safe Browsing. can also check apps for specific scanner.
permissions.
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17. Anti-Malware solutions for Android
IKARUS mobile.security LITE is a Kaspersky Mobile Security (Lite) is Kinetoo Malware Scan offers a
plain virus scanner and got top one of the best malware marginal detection rate. The free
marks in the malware detection protection solutions and contains version contains a regularly
test. Anti-Theft, Privacy Protection, updated database of mobile
Parental Control and Data malware and spyware.
Encryption.
With LabMSF Antivirus we found Lookout Security & Antivirus McAfee Mobile Security offers
neither any malware nor the achieved very good results for comprehensive security functions
EICAR test file. malware detection. Privacy with a 1-year subscription.
Advisor, Safe Browsing, Remote
Lock and Wipe and other
functions are available in the
premium version.
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18. Anti-Malware solutions for Android
MobileBot Antivirus couldn’t find MobiShield Mobile Security The only working feature of MT
any malware sample, but it’s free contains free Antivirus, Backup, Antivirus seems to be the
of ads. System Optimization, Anti-Theft, advertisements at the bottom.
Traffic-Monitor and more. The
malware detection test ends with
moderate results.
MYAndroid Protection Antivirus Norton Mobile Security Lite NQ Mobile Security provides
looks good, but it detected achieves good test results. The Antivirus, Network Manager,
nothing. free version includes Anti- Privacy Advisor, Optimization and
Malware and Anti-Theft. Backup in its free version.
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19. Anti-Malware solutions for Android
Privateer LITE has no additional Quick Heal Mobile Security Snap Secure has a clear menu but
functions to its scan feature, includes Anti-Malware detection, it detected less than 40 percent of
which didn’t detect too many Call Blocker, Anti-Theft and our malware test set.
samples. Message Filtering.
Super Security is a free solution Total Defense Mobile Security Trend Micro Mobile Security
with a good detection rate. It has provides AntiVirus, Monitoring Personal Edition scored well in the
several other functions. malware detection test. Safe
and Backup.
Browsing, Parental Control Call
and Message Filter as well as Anti-
Theft functions are integrated.
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20. Anti-Malware solutions for Android
TrustGo Mobile Security has to Vipre Mobile Security is available Virus Shield didn’t detect anything
improve its detection rates. It for free. It’s a beta release but in our test. Every scan ended with
offers many functions for free. already shows good detection full screen advertisements.
rates.
Webroot SecureAnywhere Mobile Zoner AntiVirus Free surprises
shows good detection results in with very good test results and
the malware test. The premium many free functions such as Anti-
version offers Secure Browsing, Theft, Task Manager, Call Filter,
Lost Device Protection, Call and Parental Control and others.
SMS Filter and an App Inspector.
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