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A Novel Framework to Classify Malware in MIPS Architecture-Based IoT Devices

Tran Nghi Phu, Kien Hoang Dang, Dung Ngo Quoc, Nguyen Tho Dai, Nguyen Ngoc Binh
2019 Security and Communication Networks  
The F-Sandbox is a new type for IoT sandbox, automatically created from the real firmware of the specialized IoT devices, inheriting the specialized environment in the real firmware, therefore creating  ...  We proposed a framework to classify malware in IoT devices by using MIPS-based system behavior (system call—syscall) obtained from our F-Sandbox passive process and machine learning techniques.  ...  Many firmware programs cannot run in traditional malware analysis sandboxes based on basic environments.  ... 
doi:10.1155/2019/4073940 fatcat:llefjvtxc5be3h4yc5vemkfcga

Demanding Requirement of Security for Wireless Mobile Devices: A Survey

K. Muthumanickam, E. Ilavarasan
2014 Research Journal of Applied Sciences Engineering and Technology  
Specifically Android Smartphone which can access the Internet may now signify an ultimate option for malware authors.  ...  So in this study, we present an organized and widespread overview of the research on the security elucidation for wireless portable devices.  ...  Shabtai et al. (2011) presented a hostbased general IDS framework for discovering malware in Android mobile phones based on supervised anomaly detection approach.  ... 
doi:10.19026/rjaset.8.1244 fatcat:hu2ehdgmgzharhi3fzpenmmf5e

Mobile Malware Threats and Defenses for Homeland Security [chapter]

Seung-Hyun Seo, Kangbin Yim, Ilsun You
2012 Lecture Notes in Computer Science  
In this paper, we survey the threats and malicious behaviors of current mobile malwares.  ...  Then, we study the defense mechanisms of mobile malware and introduce a cooperative system for mobile security in South Korea.  ...  [12] proposed virusMeter that detects mobile malware based on abnormal power consumption caused by mobile malware. Burguera et al.  ... 
doi:10.1007/978-3-642-32498-7_39 fatcat:iwiwk46jirei3nz44fet2ibe3u

The Circle Of Life: A Large-Scale Study of The IoT Malware Lifecycle

Omar Alrawi, Charles Lever, Kevin Valakuzhy, Ryan Court, Kevin Z. Snow, Fabian Monrose, Manos Antonakakis
2021 USENIX Security Symposium  
Based on our findings, we deduce that the required technology to defend against IoT malware is available, but we conclude that there are insufficient efforts in place to deal with a large-scale IoT malware  ...  We present a large-scale measurement of more than 166K Linux-based IoT malware samples collected over a year.  ...  Brendan Saltaformaggio for their help in improving this work. We thank Bad Packets LLC for sharing their data. This work is supported in part by the US Department of Defense grant no.  ... 
dblp:conf/uss/AlrawiLVCSMA21 fatcat:jxmg7honx5gq7mre6wnpeiyysm

Detection, Traceability, and Propagation of Mobile Malware Threats

Long Chen, Chunhe Xia, Shengwei Lei, Tianbo Wang
2021 IEEE Access  
To study the traceability, propagation, and detection of the threats, we perform research on all aspects of the end-to-end environment.  ...  INDEX TERMS Android mobile malware, threat traceability, family chronology, propagation models, detection analysis, infected system environment, knowledge map construction, architecture of mobile malware  ...  BEHAVIOR DETECTION Based on building the kernel objects Android behavior diagram (or KOABG), a suspicious program can be intercepted in the virtual environment mobile malware in the system call parameters  ... 
doi:10.1109/access.2021.3049819 fatcat:nxvrztpgazhedojsgottybzjga

Internet of Things Malware : A Survey

Evanson Mwangi Karanja, Shedden Masupe, Jeffrey Mandu
2017 International Journal of Computer Science & Engineering Survey  
In this paper we review literature on internet of things malware categories, support technologies, propagation and tools KEYWORDS Internet of Things (IoT), Malware, Malware synthesis, Machine to Machine  ...  Internet of Things environments poses unique challenges such as device latency, scalability, lack of antimalware tools and heterogeneity of device architectures that makes malware synthesis complex.  ...  ACKNOWLEDGEMENTS This work was supported by EU-Intra-ACP Mobility under Mobility to Enhance Training of Engineering Graduates in Africa (METEGA) grant.  ... 
doi:10.5121/ijcses.2017.8301 fatcat:dqsotxtph5cm5gmagwdxd6rmju

Security for smart mobile networks: The NEMESYS approach

Erol Gelenbe, Gokce Gorbil, Dimitrios Tzovaras, Steffen Liebergeld, David Garcia, Madalina Baltatu, George Lyberopoulos
2013 2013 International Conference on Privacy and Security in Mobile Systems (PRISMS)  
bypass the user and install malware.  ...  With the emergence of the first large-scale mobile botnets, the core network has also become vulnerable to distributed denial-of-service attacks such as the signaling attack.  ...  Figure 2 shows the distribution of malware types for the top ten Android malware families in 2012, based on data from TrendLabs [25] .  ... 
doi:10.1109/prisms.2013.6927181 dblp:conf/prisms/GelenbeGTLGBL13 fatcat:kay7rpsm5veajkayjcep5cdboe

Security for smart mobile networks: The NEMESYS approach

Erol Gelenbe, Gokce Gorbil, Dimitrios Tzovaras, Steffen Liebergeld, David Garcia, Madalina Baltatu, George Lyberopoulos
2013 2013 IEEE Global High Tech Congress on Electronics  
bypass the user and install malware.  ...  With the emergence of the first large-scale mobile botnets, the core network has also become vulnerable to distributed denial-of-service attacks such as the signaling attack.  ...  Figure 2 shows the distribution of malware types for the top ten Android malware families in 2012, based on data from TrendLabs [25] .  ... 
doi:10.1109/ghtce.2013.6767242 fatcat:nd3gkngb5ve2fe23cmfc2kf5mi

Supporting Transparent Snapshot for Bare-metal Malware Analysis on Mobile Devices

Le Guan, Shijie Jia, Bo Chen, Fengwei Zhang, Bo Luo, Jingqiang Lin, Peng Liu, Xinyu Xing, Luning Xia
2017 Proceedings of the 33rd Annual Computer Security Applications Conference on - ACSAC 2017  
With the emergence of evasive malware, which is capable of detecting that it is being analyzed in virtualized environments, bare-metal analysis has become the definitive resort.  ...  In addition, all of the existing works require some in-guest components to assist the restoration. Therefore, a kernel-level malware is still able to detect the presence of the in-guest components.  ...  BareCloud [26] is an armored malware detection system; it executes malware on a bare-metal system and compares both disk-level and network-level activities of the malware with other emulation and virtualizationbased  ... 
doi:10.1145/3134600.3134647 dblp:conf/acsac/GuanJCZLLLXX17 fatcat:6sgozgloofbwvkqzawixtlkxma

Scalable and Secure Architecture for Distributed IoT Systems [article]

Najmeddine Dhieb, Hakim Ghazzai, Hichem Besbes, Yehia Massoud
2020 arXiv   pre-print
We propose a novel architecture based on permissioned blockchain technology in order to build a scalable and decentralized end-to-end secure IoT system.  ...  Furthermore, we enhance the IoT system security with an AI-component at the gateway level to detect and classify suspected activities, malware, and cyber-attacks using machine learning techniques.  ...  The malware detection module is performed on IoT gateways and nodes participating into the blockchain network.  ... 
arXiv:2005.02456v1 fatcat:culse67ylzeqlnkedwepjshq5e

FirmwareDroid: Security Analysis of the Android Firmware EcoSystem [article]

Thomas Sutter
2021 arXiv   pre-print
We demonstrate on real data that pre-installed apps are, in fact, a a threat to Android's users, and we can detect several hundred malware samples using scanners like VirusTotal, AndroGuard, and APKiD.  ...  In our study, we analyze the Android firmware eco-system in various ways.  ...  Laemk;->c()Ljava/lang/String;' This application loads a native library: 'crashreporterer' This application loads a native library: 'Lasrr;->c()Laspc;' Androwarn native code loading report for Android10 firmware  ... 
arXiv:2112.08520v1 fatcat:ofenstl53fbhphcnm3afyx5lvi

Mobile Botnet Attacks – an Emerging Threat: Classification, Review and Open Issues

2015 KSII Transactions on Internet and Information Systems  
fraud, phishing, malware distribution, spam emails, and building mobile devices for the illegitimate exchange of information and materials.  ...  In relation to our findings, research challenges are then presented in this domain.  ...  Furthermore, authors in [43] created a virtual lab environment for the purpose of analysis and detection of Android malware through emulating the environment.  ... 
doi:10.3837/tiis.2015.04.012 fatcat:hq2wtnaxlfhzjl5bd2l6q5dzky

Recent Advances in Android Mobile Malware Detection: A Systematic Literature Review

Abdulaziz Alzubaidi
2021 IEEE Access  
INDEX TERMS Smartphone, intrusion detection, mobile malware, android devices, machine learning.  ...  BACKGROUND This section provides general characteristics related to Android malware detection. A.  ...  [118] were able to detect app patterns by implementing a multi-level anomaly detector scheme for Android Malware (MADAM) at the kernel, app, user, and package levels.  ... 
doi:10.1109/access.2021.3123187 fatcat:evuzzky5izht3efupwo6ty42ea

A survey on various mobile malware attacks and security characteristics The increase of the smart devices is quickly expanding and is progressively turning out to be more modern device in the recent smart world. This expanding prominence is making the attackers have a flawless focus on it. The smart devices prepared with the advanced complicated software and hardware systems are paying way for the profit of the malware attackers. The malware authors targets the mobile devices and destruct the information in ...

2017 International Journal of Latest Trends in Engineering and Technology  
In the Android devices Network Environment assumes to play the primary role. Management Network Environment (MNE) controls and manages the basic remote and the update processes.  ...  Based on the Linux system, the security mechanism for Google Android was developed.  ... 
doi:10.21172/1.82.060 fatcat:c47qwv4lrrcqtestovkssi2cga

ANDRUBIS -- 1,000,000 Apps Later: A View on Current Android Malware Behaviors

Martina Lindorfer, Matthias Neugschwandtner, Lukas Weichselbaum, Yanick Fratantonio, Victor van der Veen, Christian Platzer
2014 2014 Third International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS)  
To deal with the increasing number of malicious Android apps in the wild, malware analysts typically rely on analysis tools to extract characteristic information about an app in an automated fashion.  ...  Android is the most popular smartphone operating system with a market share of 80%, but as a consequence, also the platform most targeted by malware.  ...  This work also has been carried out within the scope of u'smile, the Josef Ressel Center for User-Friendly Secure Mobile Environments.  ... 
doi:10.1109/badgers.2014.7 dblp:conf/badgers/LindorferNWFVP14 fatcat:7u7b3slwijbpzddoyn7sndlhky
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