Littérature scientifique sur le sujet « Smishing »
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Articles de revues sur le sujet "Smishing":
Jain, Ankit Kumar, Sumit Kumar Yadav et Neelam Choudhary. « A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques ». International Journal of E-Services and Mobile Applications 12, no 1 (janvier 2020) : 21–38. http://dx.doi.org/10.4018/ijesma.2020010102.
Jain, Ankit Kumar, et B. B. Gupta. « Feature Based Approach for Detection of Smishing Messages in the Mobile Environment ». Journal of Information Technology Research 12, no 2 (avril 2019) : 17–35. http://dx.doi.org/10.4018/jitr.2019040102.
Lee, Ji-Won, Dong-Hoon Lee et In-Suk Kim. « Method of Detecting SmiShing using SVM ». Journal of Security Engineering 10, no 6 (31 décembre 2013) : 655–68. http://dx.doi.org/10.14257/jse.2013.12.01.
Mishra, Sandhya, et Devpriya Soni. « Smishing Detector : A security model to detect smishing through SMS content analysis and URL behavior analysis ». Future Generation Computer Systems 108 (juillet 2020) : 803–15. http://dx.doi.org/10.1016/j.future.2020.03.021.
Park, Hyo-Min, Wan-Seok Kim, So-Jeong Kang et Sang Uk Shin. « Cloud Messaging Service for Preventing Smishing Attack ». Journal of Digital Convergence 15, no 4 (28 avril 2017) : 285–93. http://dx.doi.org/10.14400/jdc.2017.15.4.285.
Park, Dea-Woo. « Analysis on Mobile Forensic of Smishing Hacking Attack ». Journal of the Korea Institute of Information and Communication Engineering 18, no 12 (31 décembre 2014) : 2878–84. http://dx.doi.org/10.6109/jkiice.2014.18.12.2878.
McVey, Tom. « Smishing uses short-lived URLs to avoid detection ». Network Security 2021, no 7 (juillet 2021) : 6. http://dx.doi.org/10.1016/s1353-4858(21)00073-8.
Park, Dea-Woo. « Analysis of Mobile Smishing Hacking Trends and Security Measures ». Journal of the Korea Institute of Information and Communication Engineering 19, no 11 (30 novembre 2015) : 2615–22. http://dx.doi.org/10.6109/jkiice.2015.19.11.2615.
Moon, Soon-ho, et Dea-woo Park. « Forensic Analysis of MERS Smishing Hacking Attacks and Prevention ». International Journal of Security and Its Applications 10, no 6 (30 juin 2016) : 181–92. http://dx.doi.org/10.14257/ijsia.2016.10.6.18.
Sonowal, Gunikhan, et K. S. Kuppusamy. « SmiDCA : An Anti-Smishing Model with Machine Learning Approach ». Computer Journal 61, no 8 (25 avril 2018) : 1143–57. http://dx.doi.org/10.1093/comjnl/bxy039.
Thèses sur le sujet "Smishing":
Metso, Joanna, et Rasmus Gunnarsson. « IT-säkerhetshotet phishing : Svenska små och medelstora företags utbildningsinsatser inom problemområdet ». Thesis, Jönköping University, JTH, Avdelningen för datateknik och informatik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-54185.
Information security training about phishing is required to be able to combat the threat that phishing determine, as humans are always the weakest link within an organization. Although proposals and requirements linked to how information security training should be implemented in the literature, standards, and frameworks, it is difficult for SMEs to adapt and implement these in practice. The purpose of this study is therefore to investigate Swedish SMEs' implementation of forms of education to address the phishing threat. The empirical data has been collected through semi-structured interviews and thematic analysis. The results from the study showed that the forms of education are mainly based on own experiences and examples from previous phishing attacks that have affected other organizations. A couple of organizations have not developed their forms of education themselves, instead they use tools and other companies experiences as aids. The results also showed that the opinions about the chosen form of education were not always the same between management and users. The conclusion of the study is that SMEs can implement education around the threat that phishing constitutes without specific frameworks or standards to rely on, but if the organization want to use it, they must be careful to adapt the education to their own organization's size. In order to draw more far-reaching conclusions than those described in the report, it would have been important to be able to rely on a larger number of organizations than the 4 organizations and the 10 interviewees that participated in the study. In addition, more research is needed in the field of smishing and vishing.
Chapitres de livres sur le sujet "Smishing":
Goel, Diksha, et Ankit Kumar Jain. « Smishing-Classifier : A Novel Framework for Detection of Smishing Attack in Mobile Environment ». Dans Communications in Computer and Information Science, 502–12. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8660-1_38.
Kang, Anna, Jae Dong Lee, Won Min Kang, Leonard Barolli et Jong Hyuk Park. « Security Considerations for Smart Phone Smishing Attacks ». Dans Lecture Notes in Electrical Engineering, 467–73. Berlin, Heidelberg : Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41674-3_66.
Kang, Anna, Jae Dong Lee, Won Min Kang, Leonard Barolli et Jong Hyuk Park. « Erratum : Security Considerations for Smart Phone Smishing Attacks ». Dans Lecture Notes in Electrical Engineering, E1. Berlin, Heidelberg : Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41674-3_202.
Lee, Ayoung, Kyounghun Kim, Heeman Lee et Moonseog Jun. « A Study on Realtime Detecting Smishing on Cloud Computing Environments ». Dans Lecture Notes in Electrical Engineering, 495–501. Berlin, Heidelberg : Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47895-0_60.
Jain, Ankit Kumar, Sumit Kumar Yadav et Neelam Choudhary. « A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques ». Dans Research Anthology on Securing Mobile Technologies and Applications, 267–85. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8545-0.ch014.
Jain, Ankit Kumar, et B. B. Gupta. « Feature Based Approach for Detection of Smishing Messages in the Mobile Environment ». Dans Research Anthology on Securing Mobile Technologies and Applications, 286–306. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8545-0.ch015.
« Phishing, SMishing, and Vishing ». Dans Mobile Malware Attacks and Defense, 125–96. Elsevier, 2009. http://dx.doi.org/10.1016/b978-1-59749-298-0.00006-9.
Actes de conférences sur le sujet "Smishing":
Boukari, Badr Eddine, Akshaya Ravi et Mounira Msahli. « Machine Learning Detection for SMiShing Frauds ». Dans 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC). IEEE, 2021. http://dx.doi.org/10.1109/ccnc49032.2021.9369640.
Balim, Caner, et Efnan Sora Gunal. « Automatic Detection of Smishing Attacks by Machine Learning Methods ». Dans 2019 1st International Informatics and Software Engineering Conference (UBMYK). IEEE, 2019. http://dx.doi.org/10.1109/ubmyk48245.2019.8965429.