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Intelligent Hybrid Approach for Android Malware Detection based on Permissions and API Calls

Altyeb Altaher, Omar Mohammed
2017 International Journal of Advanced Computer Science and Applications  
This paper proposes an intelligent hybrid approach for Android malware detection using the permissions and API calls in the Android application. The proposed approach consists of two steps.  ...  Using a dataset consists of 250 goodware and 250 malware apps collected from different recourse, the conducted experiments show that the suggested method for Android malware detection is effective and  ...  Several research efforts have been presented for malware detection depending on the Android permissions used in the app.  ... 
doi:10.14569/ijacsa.2017.080608 fatcat:6ex4vkpabrhgbm4bda2vlv7rzi

Android Application Security Scanning Process [chapter]

Iman Almomani, Mamdouh Alenezi
2019 Android [Working Title]  
; (c) app feature extraction, considering both static and dynamic analysis; (d) dataset creation and/or utilization; and (e) data analysis and data mining that result in producing detection systems, classification  ...  The scanning process is comprehensive, explaining the main phases and how they are conducted including (a) the download of the apps themselves; (b) Android application package (APK) reverse engineering  ...  Samah Alsoghyer and Ms. Aala Khayer.  ... 
doi:10.5772/intechopen.86661 fatcat:cjjre3lkfbcwxhlwxk744af5xq

How dangerous is your Android app? An evaluation methodology

Andrea Atzeni, Tao Su, Madalina Baltatu, Rosalia D'Alessandro, Giovanni Pessiva
2014 Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services  
It examines the correlations between app required permissions and the invoked APIs, as well as the contents in the package, and subsequently it uses a dynamic analysis module to confirm the suspicions  ...  Moreover, due to the platform openness and the plethora of available software, dangerous apps (even if not necessarily malware) are now also very common for Android devices.  ...  In [25] , the authors recast ICC analysis to infer the locations and substance of all inter-and intra-app communication in an Android environment.  ... 
doi:10.4108/icst.mobiquitous.2014.257832 dblp:conf/mobiquitous/AtzeniSBDP14 fatcat:2345oeo32bdbtbfnqwzmdf3phe

A Survey of Android Malware Static Detection Technology Based on Machine Learning

Qing Wu, Xueling Zhu, Bo Liu
2021 Mobile Information Systems  
With the rapid growth of Android devices and applications, the Android environment faces more security threats.  ...  To detect Android malware, researchers have proposed various techniques, among which the machine learning-based methods with static features of apps as input vectors have apparent advantages in code coverage  ...  files in a monitored environment.  ... 
doi:10.1155/2021/8896013 doaj:9dc548d197fd404fbcd4ee962f374bde fatcat:mbuavifbmzfmjm3shzm4wcbm4a

Android Malware Detection Using Category-Based Machine Learning Classifiers

Huda Ali Alatwi, Tae Oh, Ernest Fokoue, Bill Stackpole
2016 Proceedings of the 17th Annual Conference on Information Technology Education - SIGITE '16  
Android Malware Detection Using Category-Based Machine Learning Classifiers by Huda Ali Alatwi Android malware growth has been increasing dramatically along with increasing the diversity and complicity  ...  We utilize the features of the top rated apps in a specific category to train a malware detection classifier for that given category.  ...  Chapter 3 presents related work has been done in static and dynamic malware detection in Android environment.  ... 
doi:10.1145/2978192.2978218 dblp:conf/sigite/AlatwiOFS16 fatcat:azwq25k5nbgbtavz7amuffjzue

A Structure Similarity-based Approach to Malicious Android App Detection

Gu Hsin Lai, Yen-Hsien Lee, Tsai-Hsin Chu, Tsang-Hsiang Cheng
2015 Pacific Asia Conference on Information Systems  
In this study, we proposed the structure similarity-based malicious app detection approach to address the need of malicious Android app detection.  ...  As a result, the needs for real-time malware detection and classification become critical for Android users and official market as the number of Android apps increases sharply.  ...  However, the permission information is not sufficient for detecting malicious apps because some permission requests are not defined explicitly in Android platform; for example, the "INTERNET" permission  ... 
dblp:conf/pacis/LaiLCC15 fatcat:v3o4tn3csrastmlcsomrjet3y4

Review on Mobile Threats and Detection Techniques

Lovi Dua, Divya Bansal
2014 International Journal of Distributed and Parallel systems  
In this research work, we have done a systematic review of the terms related to malware detection algorithms and have also summarized behavioral description of some known mobile malwares in tabular form  ...  computational complexity and detection ration of mobile malwares.  ...  Second detection engine includes some heuristics to detect suspicious apps and zero-day malwares.  ... 
doi:10.5121/ijdps.2014.5403 fatcat:fagpq6bx2vbsdbf6o6wvaaldge

Research on Android Malware Detection Technology Based on Improved SVM Algorithm

Lu CHEN, Mu CHEN, Ni-ge LI, Zao-jian DAI
2017 DEStech Transactions on Engineering and Technology Research  
, and with the increase of the training sample set greatly reduces the training time, this Android malware detection technology is more suitable for Android malware detection environment.  ...  Experiments show that the Android malware detection system based on improved SVM algorithm is superior to the traditional SVM algorithm in the classification accuracy and detection rate of false positives  ...  It can be seen that the improved SVM algorithm has a better application effect in a large number of Android application detection environment.  ... 
doi:10.12783/dtetr/mcae2017/15962 fatcat:2cbne7gxk5e3bmqz4lqrzmmnru

Machine Learning Approach for Malware Detection by Using APKs

Rubata RIASAT, Muntaha SAKEENA, Abdul Hannan SADIQ, Chong WANG, Chang-you ZHANG, Yong-ji WANG
2017 DEStech Transactions on Computer Science and Engineering  
Although understanding the android malware using dynamic analysis can provide a compressive view and it is still subjected to high cost in environment development and manual effort in investigation.  ...  In this study our proposed approach provides a static and dynamic analyst paradigm for detecting android malware.  ...  Teams and especially to CAS-TWAS.  ... 
doi:10.12783/dtcse/cnsce2017/8883 fatcat:5kh34qysyfhx7f7zev6vne672u

DNA-Droid: A Real-Time Android Ransomware Detection Framework [chapter]

Amirhossein Gharib, Ali Ghorbani
2017 Lecture Notes in Computer Science  
Ransomware has become one of the main cyber-threats for mobile platforms and in particular for Android.  ...  The main reason is that ransomware and  ...  Offline Detection The offline detection methods are used to detect samples in isolated environments, statically or dynamically, and are not designed to detect and prevent ransomware in real-time on the  ... 
doi:10.1007/978-3-319-64701-2_14 fatcat:vaa6dmi6r5blrglpvtp6j76t6i

Constructing Features for Detecting Android Malicious Applications: Issues, Taxonomy and Directions

Wei Wang, Meichen Zhao, Zhenzhen Gao, Guangquan Xu, Hequn Xian, Yuanyuan Li, Xiangliang Zhang
2019 IEEE Access  
To ensure the quality and security of the apps in the markets, many approaches have been proposed in recent years to discriminate malapps from benign ones.  ...  INDEX TERMS Android system, IoT, security and privacy, machine learning, malware analysis, malapp detection, survey. 67602 2169-3536  ...  Therefore, some work inspects a given Android app and acquires dynamic loading code when the app is running in a virtual environment or on a real device.  ... 
doi:10.1109/access.2019.2918139 fatcat:iifcbw3v4nbb3efq4eutkttcn4

Detecting Malicious Collusion Between Mobile Software Applications: The Android TM Case [chapter]

Irina Măriuca Asăvoae, Jorge Blasco, Thomas M. Chen, Harsha Kumara Kalutarage, Igor Muttik, Hoang Nga Nguyen, Markus Roggenbach, Siraj Ahmed Shaikh
2017 Data Analytics and Decision Support for Cybersecurity  
The authors would like to thank the anonymous reviewers for their helpful comments, and Erwin R. Catesbeiana (Jr) for pointing out the importance of intention in malware analysis.  ...  Acknowledgment This work has been supported by UK Engineering and Physical Sciences Research Council (EPSRC) grant EP/L022699/1.  ...  We first review previous approaches to detect and identify Android malware (single apps) in general. Then, we address previous work on detection and identification of colluding apps.  ... 
doi:10.1007/978-3-319-59439-2_3 fatcat:fd6dz47f7bbtpm5nz4bdu6q5ou

Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach [article]

Sen Chen, Minhui Xue, Lingling Fan, Shuang Hao, Lihua Xu, Haojin Zhu, Bo Li
2017 arXiv   pre-print
We finally show that KuafuDet can significantly reduce false negatives and boost the detection accuracy by at least 15%.  ...  KuafuDet includes an offline training phase that selects and extracts features from the training set, and an online detection phase that utilizes the classifier trained by the first phase.  ...  To address this threat, we therefore proposed our detection system, KuafuDet, and showed it significantly reduces false negatives and boosts the detection accuracy by at least 15%.  ... 
arXiv:1706.04146v3 fatcat:f7yzifuahff6dfyaihlrnn3gfa

An LSTM-Based Malware Detection Using Transfer Learning

Zhangjie Fu, Yongjie Ding, Musaazi Godfrey
2021 Journal of Cyber Security  
In this paper, we propose a new approach to mobile malware detection that is able to detect newly-emerged malware instances.  ...  At last, we use the augmented examples to retrain the 4th and 5th layers of the LSTM network and the last fully connected layer so that it can discriminate against newly-emerged malware.  ...  At first, we build, train, and test the model (SmartAMD1, Smart Android Malware Detection 1) on the original dataset (benign apps and malware apps), then we build a GAN and generate augmented examples.  ... 
doi:10.32604/jcs.2021.016632 fatcat:ulfntsglobdzdnpahgm7zfchyi

Android Malware Detection Method Based on Frequent Pattern and Weighted Naive Bayes [chapter]

Jingwei Li, Bozhi Wu, Weiping Wen
2019 Communications in Computer and Information Science  
How to effectively detect Android malware has become a significant problem. Permissions and API calls in Android applications can effectively reflect the behavior patterns of an Android application.  ...  With the market share of Android system becoming the first in the world, the security problem of Android system is becoming more and more serious.  ...  Firstly, filtering the feature feature and API call feature by the feature differentiation degree defined in the text, and then mine frequent pattern of malicious application and Benign App based on the  ... 
doi:10.1007/978-981-13-6621-5_4 fatcat:4xjnydkm65c7vp5aclfqggugdy
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