This document discusses the development of DroidSwan, a machine learning model for detecting Android malware. It begins with background on the prevalence of Android malware and need for detection techniques. It then outlines the process used to build DroidSwan, including collecting a dataset of malware and benign apps, extracting relevant features, deriving an optimal feature set, and building and testing the classifier model. Key features for detection included suspicious permissions, permission combinations, API calls, and manifest violations. The document concludes by presenting DroidSwan's performance based on metrics like ROC curve, recall rate, and detection rate.
An insider threat is a malicious threat to an organization that comes from people within the organization, such as employees, former employees, contractors or business associates, who have inside information concerning the organization's security practices, data and computer systems. The threat may involve fraud, the theft of confidential or commercially valuable information, the theft of intellectual property, or the sabotage of computer systems.
Fraud and Malware Detection in Google Play by using Search Rankijtsrd
Fraudulent behaviors in Google Play, the most popular Android app market, fuel search rank abuse and malware proliferation. To identify malware, previous work has focused on app executable and permission analysis. In this paper, we introduce FairPlay, a novel system that discovers and leverages traces left behind by fraudsters, to detect both malware and apps subjected to search rank fraud. . Fair Play discovers hundreds of fraudulent apps that currently evade Google Bouncer’s detection technology. A. Brahma Reddy | K. V. Ranga Rao | V. Vinay Kumar "Fraud and Malware Detection in Google Play by using Search Rank" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd35728.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-network/35728/fraud-and-malware-detection-in-google-play-by-using-search-rank/a-brahma-reddy
VirusTotal Threat Intelligence and DNIF Use CasesDNIF
NIF is a next gen SIEM platform with advanced security and automation capabilities, that let's machines do what they do best and allows security analysts to do activities that can actually change the game.
In this presentation, we talk about how DNIF users can build a use case on "Detecting Malicious URLs" with the help of VirusTotal Threat Intelligence.
The digital transformation of businesses is growing exponentially because enterprises are attracted by the revenue growth it brings and by the opportunities for new business it generates. Yet, the success of this digital revolution will depend on how quickly and efficiently cyber security evolves to counter increasingly complex, rapid and aggressive threats and to safeguard natively insecure digital innovations. Prescriptive Security Operations Centers (SOC) will be the next generation SOCs that the digital economy needs in order to innovate securely and steadily. With Prescriptive SOCs, organizations will be able to effectively protect their business assets including valuable business data and customer personal data. Prescriptive SOC will require a technological change, with the convergence of intelligence, big data and analytics - driven security that will scrutinize all the data generated in its environment, from IT to OT to IoT data. Cyber security will shift from a reactive and proactive model to a prescriptive model, focused on analytics patterns in order to identity emerging threats and automate the security control responses.
This presentation was showcased during Ladies in Cyber Security, an event organised by DefCamp and Cyber Security Research Center from Romania - CCSIR.
An insider threat is a malicious threat to an organization that comes from people within the organization, such as employees, former employees, contractors or business associates, who have inside information concerning the organization's security practices, data and computer systems. The threat may involve fraud, the theft of confidential or commercially valuable information, the theft of intellectual property, or the sabotage of computer systems.
Fraud and Malware Detection in Google Play by using Search Rankijtsrd
Fraudulent behaviors in Google Play, the most popular Android app market, fuel search rank abuse and malware proliferation. To identify malware, previous work has focused on app executable and permission analysis. In this paper, we introduce FairPlay, a novel system that discovers and leverages traces left behind by fraudsters, to detect both malware and apps subjected to search rank fraud. . Fair Play discovers hundreds of fraudulent apps that currently evade Google Bouncer’s detection technology. A. Brahma Reddy | K. V. Ranga Rao | V. Vinay Kumar "Fraud and Malware Detection in Google Play by using Search Rank" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd35728.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-network/35728/fraud-and-malware-detection-in-google-play-by-using-search-rank/a-brahma-reddy
VirusTotal Threat Intelligence and DNIF Use CasesDNIF
NIF is a next gen SIEM platform with advanced security and automation capabilities, that let's machines do what they do best and allows security analysts to do activities that can actually change the game.
In this presentation, we talk about how DNIF users can build a use case on "Detecting Malicious URLs" with the help of VirusTotal Threat Intelligence.
The digital transformation of businesses is growing exponentially because enterprises are attracted by the revenue growth it brings and by the opportunities for new business it generates. Yet, the success of this digital revolution will depend on how quickly and efficiently cyber security evolves to counter increasingly complex, rapid and aggressive threats and to safeguard natively insecure digital innovations. Prescriptive Security Operations Centers (SOC) will be the next generation SOCs that the digital economy needs in order to innovate securely and steadily. With Prescriptive SOCs, organizations will be able to effectively protect their business assets including valuable business data and customer personal data. Prescriptive SOC will require a technological change, with the convergence of intelligence, big data and analytics - driven security that will scrutinize all the data generated in its environment, from IT to OT to IoT data. Cyber security will shift from a reactive and proactive model to a prescriptive model, focused on analytics patterns in order to identity emerging threats and automate the security control responses.
This presentation was showcased during Ladies in Cyber Security, an event organised by DefCamp and Cyber Security Research Center from Romania - CCSIR.
Destinations: Hanoi - Sa Pa – Tam Coc – Halong Bay - Hue - Hoi An - NhaTrang -Saigon - Tay Ninh - Cu Chi – Ben Tre – Cai Be – Can Tho.
From North to South, this 20 - day program covers all the highlights of vietnam travel, includes a diverse range of destinations and excursions providing travelers with an in-depth view of the history, cultures and landscapes of Vietnam. Trip to the mountainous region of Sa Pa and Ha Long Bay, from Red River to Mekong Delta, from major cities of Hanoi and Ho Chi Minh to cultural heartland Hue and Hoi An and NhaTrang with beautiful beach.
More details on: http://www.intimateasia.com/
ANDROINSPECTOR: A SYSTEM FOR COMPREHENSIVE ANALYSIS OF ANDROID APPLICATIONSIJNSA Journal
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. AndroInspector 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.
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.
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.
A FRAMEWORK FOR THE DETECTION OF BANKING TROJANS IN ANDROIDIJNSA Journal
Android is the most widely used operating system today and occupies more than 70% share of the smartphone market. It is also a popular target for attackers looking to exploit mobile operating systems for personal gains. More and more malware are targeting android operating system like Android Banking Trojans (ABTs) which are widely being discovered. To detect such malware, we propose a prediction model for ABTs that is based on hybrid analysis. The feature sets used with the machine learning algorithms are permissions, API calls, hidden application icon and device administrator. Feature selection methods based on frequency and gain ratio are used to minimize the number of features as well as to eliminate the low-impact features. The proposed system is able to achieve significant performance with selected machine learning algorithms and achieves accuracy up to 98% using random forest classifier.
Why Serverless is scary without DevSecOps and ObservabilityEficode
Mahdi Azarboon, Senior Analyst, Liquid Studio Accenture
Thanks to the unique nature of serverless, it’s not always easy to secure it properly, use it in a DevOps way, and ensure observability. Mahdi Azarboon will help you to start securing and observing your app in a DevOps way in this fascinating talk.
Destinations: Hanoi - Sa Pa – Tam Coc – Halong Bay - Hue - Hoi An - NhaTrang -Saigon - Tay Ninh - Cu Chi – Ben Tre – Cai Be – Can Tho.
From North to South, this 20 - day program covers all the highlights of vietnam travel, includes a diverse range of destinations and excursions providing travelers with an in-depth view of the history, cultures and landscapes of Vietnam. Trip to the mountainous region of Sa Pa and Ha Long Bay, from Red River to Mekong Delta, from major cities of Hanoi and Ho Chi Minh to cultural heartland Hue and Hoi An and NhaTrang with beautiful beach.
More details on: http://www.intimateasia.com/
ANDROINSPECTOR: A SYSTEM FOR COMPREHENSIVE ANALYSIS OF ANDROID APPLICATIONSIJNSA Journal
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. AndroInspector 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.
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.
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.
A FRAMEWORK FOR THE DETECTION OF BANKING TROJANS IN ANDROIDIJNSA Journal
Android is the most widely used operating system today and occupies more than 70% share of the smartphone market. It is also a popular target for attackers looking to exploit mobile operating systems for personal gains. More and more malware are targeting android operating system like Android Banking Trojans (ABTs) which are widely being discovered. To detect such malware, we propose a prediction model for ABTs that is based on hybrid analysis. The feature sets used with the machine learning algorithms are permissions, API calls, hidden application icon and device administrator. Feature selection methods based on frequency and gain ratio are used to minimize the number of features as well as to eliminate the low-impact features. The proposed system is able to achieve significant performance with selected machine learning algorithms and achieves accuracy up to 98% using random forest classifier.
Why Serverless is scary without DevSecOps and ObservabilityEficode
Mahdi Azarboon, Senior Analyst, Liquid Studio Accenture
Thanks to the unique nature of serverless, it’s not always easy to secure it properly, use it in a DevOps way, and ensure observability. Mahdi Azarboon will help you to start securing and observing your app in a DevOps way in this fascinating talk.
This is a presentation on MOTODEV App Validator from a webinar given on January 25, 2012. For more information go to http://developer.motorola.com/testing/app-validator
Cyber Code Intelligence for Android Malware Detection.pdfOKOKPROJECTS
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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
Now in a day mobile internet user increasing. Along with this mobile banking, online purchase increasing also. So secure your mobile with bitdefender android security.
Next Generation Defense in Depth Model - Tari Schreider, CCISO, Chief Cybers...EC-Council
This session will focus on presenting a next generation defense in depth model and answer the question on many CISO’s minds - is it still relevant? A model of defense in depth will serve as a backdrop to introduce you to a wide range of solutions from across the cybersecurity-industrial complex that just may change how you view your defense in depth approach.
4. Why DroidSwan?
Of all mobile malware
applications target
Android platform
Decrease in Number of
Malware samples
removed from PlayStore
98%
60%
5. Why DroidSwan?
338%
Of all mobile malware
applications target
Android platform
Decrease in Number of
Malware samples
removed from PlayStore
Increase in Number of
Malware samples on
Google’s PlayStore
98%
60%
12. Building DroidSwan
Collecting
Malware and
Benign data set
Updating
classifier with
new data
Feature set
efficiency
analysis
Building
classifier model
Deriving feature
set
Identifying
crucial features
22. Extracting Features
APK APK Parser
Dexdump
Suspicious
Permissions
Suspicious permission
Combinations
Suspicious API
Combinations
Suspicious
Content URI
Manifest
Violation
23. Extracting Features
APK APK Parser
Dexdump
Jar
Disassembler
Executables in
resources
Suspicious
Permissions
Suspicious permission
Combinations
Suspicious API
Combinations
Suspicious
Content URI
Manifest
Violation
24. Deriving Feature Set
Three variations of feature set considered :
•Weighted feature set with ED as a feature
•Weighted feature set without ED as a feature
•Non-Weighted feature set
31. Babu Rajesh V has been
working for three years in
the field of mobile security
and malware analysis. His
areas of interests include
mobile security and
embedded security
Himanshu Pareek has around
six years of experience in
developing and design of
security solutions related to
small sized networks. He has
research papers published on
topics like malware detection
based on behaviour and
application modelling
Mahesh U Patil received
master degree in electronics
and communication. Presently
he is working as Principal
Technical Officer at CDAC. His
research interests include
Mobile Security and
Embedded Systems
Phaninder Reddy has been
working for two years in the
field of mobile security and
malware analysis. His areas
of interests include
machine learning and data
analytics
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