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A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutions [article]

Harun Oz, Ahmet Aris, Albert Levi, A. Selcuk Uluagac
2021 arXiv   pre-print
domain very soon, understanding ransomware and analyzing defense mechanisms with respect to target platforms is becoming more imperative.  ...  It has become a very profitable business for cybercriminals with revenues of millions of dollars, and a very serious threat to organizations with financial loss of billions of dollars.  ...  ACKNOWLEDGEMENTS This work is partially supported by the US National Science Foundation Awards: NSF-CAREER-CNS-1453647 and NSF-1718116.  ... 
arXiv:2102.06249v1 fatcat:3n62zwlo2be47m3sinht5ts6bu

A Survey of Stealth Malware: Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions [article]

Ethan M. Rudd, Andras Rozsa, Manuel Günther, Terrance E. Boult
2016 arXiv   pre-print
and more lucrative targets for malware.  ...  In this paper, we survey malicious stealth technologies as well as existing solutions for detecting and categorizing these countermeasures autonomously.  ...  O N , z 1 , .., z N )) N , 2 HMMs are used for many sequential learning problems and have several different notations.  ... 
arXiv:1603.06028v2 fatcat:dyyemahzjze6bltxlwnnqgeyzy

EC2: Ensemble Clustering and Classification for Predicting Android Malware Families

Tanmoy Chakraborty, Fabio Pierazzi, V. S. Subrahmanian
2017 IEEE Transactions on Dependable and Secure Computing  
As the most widely used mobile platform, Android is also the biggest target for mobile malware.  ...  We use the output of both supervised classifiers and unsupervised clustering to design EC2.  ...  Part of this work was funded by ONR Grants N000141612739, N000141512007, N000141612896, and N000141512742, ARO grant W911NF1410358 and Maryland Procurement Office grant H9823014C0137.  ... 
doi:10.1109/tdsc.2017.2739145 fatcat:gfjfyb2mevcqnjvrrkffdp4dgq

Machine Learning Based Cyber Attacks Targeting on Controlled Information: A Survey [article]

Yuantian Miao, Chao Chen, Lei Pan, Qing-Long Han, Jun Zhang, Yang Xiang
2021 arXiv   pre-print
Detecting and defending against such attacks is challenging and urgent so that governments, organizations, and individuals should attach great importance to the ML-based stealing attacks.  ...  Recent publications are summarized to generalize an overarching attack methodology and to derive the limitations and future directions of ML-based stealing attacks.  ...  With N-gram probabilities representing the frequency of use, the tagged POS was used to select the most suitable segmentation for passwords.  ... 
arXiv:2102.07969v1 fatcat:h4br22tpjre2lisc4zbzpy2iee

International Research Conference on Smart Computing and Systems Engineering SCSE 2020 Proceedings [Full Conference Proceedings]

2020 2020 International Research Conference on Smart Computing and Systems Engineering (SCSE)  
ACKNOWLEDGMENT The authors would like to thank the Department of Census and Department of Irrigation, Sri Lanka for providing the paddy yield and climate data for this study.  ...  in the University of Kelaniya for their immense support and encouragement they gave throughout the development phase of the data sets.  ...  The mobile application was implemented using Android Studio for native Android development. The application was built for API level 26, and above and Gradle version 3.5.1 was used.  ... 
doi:10.1109/scse49731.2020.9313027 fatcat:gjk5az2mprgvrpallwh6uhvlfi

Applications in Security and Evasions in Machine Learning: A Survey

Ramani Sagar, Rutvij Jhaveri, Carlos Borrego
2020 Electronics  
By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area.  ...  ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more.  ...  All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/electronics9010097 fatcat:ttmpehdctjhbdk7arxgczl6224

Machine Learning for Security and the Internet of Things: the Good, the Bad, and the Ugly

Fan Liang, William G. Hatcher, Weixian Liao, Weichao Gao, Wei Yu
2019 IEEE Access  
In this paper, we consider the good, the bad, and the ugly use of machine learning for cybersecurity and CPS/IoT.  ...  Moreover, such technologies can be used by malicious actors, and the potential for cyber threats, attacks, intrusions, and obfuscation that are only just being considered, applied, and countered.  ...  For instance, Zhang et al. [185] used n-gram and LSTM models to predict the end of bidding in online penny auctions, such as DealDash, with high accuracy, and clustered bidders into groups.  ... 
doi:10.1109/access.2019.2948912 fatcat:wxd6imn62fgufdmfh3gtaijeru

Mobile Behaviometrics: Behavior Modeling from Heterogeneous Sensor Time-Series

Jiang Zhu
Over the decades, we have seen tremendous success in biometrics technologies being used in all types of applications based on the physical attributes of the individual such as face, fingerprints, voice  ...  Inspired by this, we introduce a new concept Mobile Behaviometrics, which uses algorithms and models to measure and quantify unique human behavioral patterns in place of human bio-attributes.  ...  Behavioral n-gram Model An n-gram model is an n-order Markov model for predicting the next item in a sequence.  ... 
doi:10.1184/r1/6720647 fatcat:lnk44fetsrcjbkaqibbh5jvzda

Analysis of Currently Open and Closed-source Software for the Creation of an AI Personal Assistant

Rubal Preet Singh
Analysis of currently open and closed-source software for the creation of an AI personal assistant.  ...  For example, for all HMM-based systems, an ngram language model is required, and a typical n-gram language model often takes several gigabytes of memory to make it impractical to deploy on mobile devices  ...  I could not get all the expected Alexa results because I was not using Alexa Echo, the device Alexa works efficiently, but I was only using its android application.  ... 
doi:10.7939/r3-f0jh-se38 fatcat:cw7fta23p5ffxn243r3gd6bmdm


Sidhika Varshney, Sidhika Varshney
2017 unpublished
For identification of a user, walking activity and call receiving activity are analyzed when the phone is on the table and in pocket or bag.  ...  In recent years, smartphones have become part and parcel of peoples life. Smartphones are used for all day-today critical tasks like money transfer, storing important documents and other information.  ...  For example, the way in which the user receives the phone, how often they move their hand while talking and doing other gestures.  ... 

IMMM 2014 COMMITTEE IMMM Advisory Committee

France Paris, Andreas Schmidt, Abdulrahman Yarali, Philip Davis, Andreas Schmidt, David Newell, Bournemouth University -Bournemouth, Kuan-Ching Li, Abdulrahman Yarali, Alain Casali, Christian Ernst, Paolo Garza (+118 others)
to the development of special mining techniques, mechanisms support, applications and enabling tools.  ...  a series of academic and industrial events focusing on advances in all aspects related to information mining, management, and use.  ...  We developed an application available to Android smartphones, and used it for field testing in the city of Nagasaki, Japan.  ... 

Robust behavioral malware detection [article]

Mikhail Kazdagli
We evaluate Shape-GD by emulating a large community of Windows systems using the system call traces from a few thousand malicious and benign applications; we simulate both a phishing attack in a corporate  ...  In both scenarios, Shape-GD identifies malware early on (∼100 infected nodes in a ∼100K-node system for watering hole attacks, and ∼10 of ∼1,000 for phishing attacks) and robustly (with ∼100% vi global  ...  However, we have experimentally determined that FPs and TPs are separable for other LD types as well -an n-gram-based LD [95] and an LD that uses VirusTotal [46] reports for malware detection [117  ... 
doi:10.15781/t2bn9xn0t fatcat:mw4vzzmed5cujgyxiuhcxrwbna

Ubiquitäre Systeme (Seminar) und Mobile Computing (Proseminar) SS 2016. Mobile und Verteilte Systeme. Ubiquitous Computing. Teil XIV

Martin Alexander [Hrsg.] Neumann, Anja [Hrsg.] Erler, Andrei [Hrsg.] Miclaus, Antonios [Hrsg.] Karatzoglou, Long [Hrsg.] Wang, Michael [Hrsg.] Beigl
As well as utilization rate, ratio between samples used for estimation and total samples. The HMM selection chose 4-state for 14 subjects and 2-state for the other 6.  ...  Furthermore, soft sensor data such as application usage and web browser history are also recorded.  ... 
doi:10.5445/ir/1000062232 fatcat:rrcknw54vveibidmngtnqvw4iq

Real-time autonomic decision making under uncertain environments for UAV-based search-and-rescue missions

Vidyasagar Sadhu
For this, I propose an actor-critic based Multi-Agent Deep Reinforcement Learning (MADRL) framework where the critic is trained in a centralized manner and the actor is decentralized and is used during  ...  transformation of sensor data into actionable knowledge by giving semantic meaning to the raw data) and planning (making real-time decisions using this knowledge).  ...  Mannini et al. [76] use HMMs for human user activity using HMM based on sensor measurements [76-79].  ... 
doi:10.7282/t3-ms6y-wr38 fatcat:lnh6thuktvartnxdz6wnmtpkea

and On-line Learning eLmL 2017 COMMITTEE eLmL Steering Committee

Ahmed Mohamed, Fahmy Yousef, Maiga Chang, Hironori Washizaki, Ivan Ganchev, Ben-David Yifat, Kolikant, Maiga Chang, Hironori Washizaki, Ivan Ganchev, Ben-David Yifat, Kolikant (+55 others)
2017 The Ninth International Conference on Mobile, Hybrid, The ninth edition of the International Conference on Mobile, Hybrid, and On-line Learning   unpublished
The constraints of e-learning are diminishing and options are increasing as the Web becomes increasingly easy to use and the technology becomes better and less expensive. eLmL 2017 provided a forum where  ...  We hope that eLmL 2017 was a successful international forum for the exchange of ideas and results between academia and industry and for the promotion of progress in eLearning research.  ...  Special thanks to the research team in educational technology seminar, and school of computer science and information technology at Northeast Normal University.  ... 
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