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Готові списки джерел за темами / Smishing

Добірка наукової літератури з теми "Smishing"

Автор: Grafiati

Опубліковано: 15 вересня 2021

Оновлено: 10 лютого 2022

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Зміст

Статті в журналах
Дисертації
Частини книг
Тези доповідей конференцій

Статті в журналах з теми "Smishing":

1

Jain, Ankit Kumar, Sumit Kumar Yadav, and 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 (January 2020): 21–38. http://dx.doi.org/10.4018/ijesma.2020010102.

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Анотація:

Smishing attack is generally performed by sending a fake short message service (SMS) that contains a link of the malicious webpage or application. Smishing messages are the subclass of spam SMS and these are more harmful compared to spam messages. There are various solutions available to detect the spam messages. However, no existing solution, filters the smishing message from the spam message. Therefore, this article presents a novel method to filter smishing message from spam message. The proposed approach is divided into two phases. The first phase filters the spam messages and ham messages. The second phase filters smishing messages from spam messages. The performance of the proposed method is evaluated on various machine learning classifiers using the dataset of ham and spam messages. The simulation results indicate that the proposed approach can detect spam messages with the accuracy of 94.9% and it can filter smishing messages with the accuracy of 96% on neural network classifier.

2

Jain, Ankit Kumar, and B. B. Gupta. "Feature Based Approach for Detection of Smishing Messages in the Mobile Environment." Journal of Information Technology Research 12, no. 2 (April 2019): 17–35. http://dx.doi.org/10.4018/jitr.2019040102.

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Анотація:

Smishing is a security attack that is performed by sending a fake message intending to steal personal credentials of mobile users. Nowadays, smishing attack becomes popular due to the massive growth of mobile users. The smishing message is very harmful since its target to financial benefits. In this article, the authors present a new feature-based approach to detect smishing messages in the mobile environment. This approach offers ten novel features that distinguish the fake messages from the ham messages. In this article, the authors have also identified the nineteen most suspicious keywords, which are used by the attacker to lure victims. This article has implemented these features on benchmarked dataset and applied numerous classification algorithms to judge the performance of the proposed approach. Experimental outcomes indicate that proposed approach can detect smishing messages with the 94.20% true positive rate and 98.74% overall accuracy. Furthermore, the proposed approach is very efficient for the detection of the zero hour attack.

3

Lee, Ji-Won, Dong-Hoon Lee, and In-Suk Kim. "Method of Detecting SmiShing using SVM." Journal of Security Engineering 10, no. 6 (December 31, 2013): 655–68. http://dx.doi.org/10.14257/jse.2013.12.01.

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4

Mishra, Sandhya, and Devpriya Soni. "Smishing Detector: A security model to detect smishing through SMS content analysis and URL behavior analysis." Future Generation Computer Systems 108 (July 2020): 803–15. http://dx.doi.org/10.1016/j.future.2020.03.021.

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5

Park, Hyo-Min, Wan-Seok Kim, So-Jeong Kang, and Sang Uk Shin. "Cloud Messaging Service for Preventing Smishing Attack." Journal of Digital Convergence 15, no. 4 (April 28, 2017): 285–93. http://dx.doi.org/10.14400/jdc.2017.15.4.285.

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6

Park, Dea-Woo. "Analysis on Mobile Forensic of Smishing Hacking Attack." Journal of the Korea Institute of Information and Communication Engineering 18, no. 12 (December 31, 2014): 2878–84. http://dx.doi.org/10.6109/jkiice.2014.18.12.2878.

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7

McVey, Tom. "Smishing uses short-lived URLs to avoid detection." Network Security 2021, no. 7 (July 2021): 6. http://dx.doi.org/10.1016/s1353-4858(21)00073-8.

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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 (November 30, 2015): 2615–22. http://dx.doi.org/10.6109/jkiice.2015.19.11.2615.

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9

Moon, Soon-ho, and Dea-woo Park. "Forensic Analysis of MERS Smishing Hacking Attacks and Prevention." International Journal of Security and Its Applications 10, no. 6 (June 30, 2016): 181–92. http://dx.doi.org/10.14257/ijsia.2016.10.6.18.

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10

Sonowal, Gunikhan, and K. S. Kuppusamy. "SmiDCA: An Anti-Smishing Model with Machine Learning Approach." Computer Journal 61, no. 8 (April 25, 2018): 1143–57. http://dx.doi.org/10.1093/comjnl/bxy039.

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Більше джерел

Дисертації з теми "Smishing":

1

Metso, Joanna, and 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.

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Анотація:

Informationssäkerhetsutbildning om phishing krävs för att kunna bekämpa det hot som phishing utgör, då människan alltid är den svagaste länken inom en organisation. Även om förslag och krav kopplade till hur informationssäkerhetsutbildning bör genomföras finns beskrivet i litteratur, standarder och ramverk är det svårt för SMF:er att anpassa och implementera dessa rent praktiskt. Syftet med denna studie är därför att undersöka svenska SMF:ers implementation av utbildningsinsatser för att bemöta phishing-hotet. Empirin har samlats in genom semistrukturerade intervjuer samt tematisk analys. Resultaten från studien visade att utbildningsinsatserna främst är grundade på egna erfarenheter och exempel från tidigare phishing-attacker som drabbat andra organisationer. Ett par organisationer har inte utvecklat sina utbildningsinsatser själva, utan använder verktyg och andra organisationers erfarenheter som hjälpmedel. Resultaten visade också att åsikterna om den valda utbildningsinsatsen inte alltid var lika mellan ledningen och användare. Slutsatsen av studien är att SMF:er kan implementera utbildning kring det hot som phishing utgör utan att förlita sig på specifika ramverk eller standarder, men att organisationen måste vara noga med att anpassa den efter sin egen organisations storlek. För att dra mer långtgående slutsatser än de som beskrivs i rapporten hade det varit av vikt att kunna förlita sig på ett större antal organisationer än de 4 organisationer och 10 intervjudeltagare som deltog i studien. Dessutom behövs mer forskning inom området gällande smishing och vishing.
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.

Частини книг з теми "Smishing":

1

Goel, Diksha, and Ankit Kumar Jain. "Smishing-Classifier: A Novel Framework for Detection of Smishing Attack in Mobile Environment." In Communications in Computer and Information Science, 502–12. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8660-1_38.

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2

Kang, Anna, Jae Dong Lee, Won Min Kang, Leonard Barolli, and Jong Hyuk Park. "Security Considerations for Smart Phone Smishing Attacks." In 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.

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3

Kang, Anna, Jae Dong Lee, Won Min Kang, Leonard Barolli, and Jong Hyuk Park. "Erratum: Security Considerations for Smart Phone Smishing Attacks." In Lecture Notes in Electrical Engineering, E1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41674-3_202.

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4

Lee, Ayoung, Kyounghun Kim, Heeman Lee, and Moonseog Jun. "A Study on Realtime Detecting Smishing on Cloud Computing Environments." In 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.

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5

Jain, Ankit Kumar, Sumit Kumar Yadav, and Neelam Choudhary. "A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques." In 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.

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Анотація:

Smishing attack is generally performed by sending a fake short message service (SMS) that contains a link of the malicious webpage or application. Smishing messages are the subclass of spam SMS and these are more harmful compared to spam messages. There are various solutions available to detect the spam messages. However, no existing solution, filters the smishing message from the spam message. Therefore, this article presents a novel method to filter smishing message from spam message. The proposed approach is divided into two phases. The first phase filters the spam messages and ham messages. The second phase filters smishing messages from spam messages. The performance of the proposed method is evaluated on various machine learning classifiers using the dataset of ham and spam messages. The simulation results indicate that the proposed approach can detect spam messages with the accuracy of 94.9% and it can filter smishing messages with the accuracy of 96% on neural network classifier.

6

Jain, Ankit Kumar, and B. B. Gupta. "Feature Based Approach for Detection of Smishing Messages in the Mobile Environment." In 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.

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Анотація:

Smishing is a security attack that is performed by sending a fake message intending to steal personal credentials of mobile users. Nowadays, smishing attack becomes popular due to the massive growth of mobile users. The smishing message is very harmful since its target to financial benefits. In this article, the authors present a new feature-based approach to detect smishing messages in the mobile environment. This approach offers ten novel features that distinguish the fake messages from the ham messages. In this article, the authors have also identified the nineteen most suspicious keywords, which are used by the attacker to lure victims. This article has implemented these features on benchmarked dataset and applied numerous classification algorithms to judge the performance of the proposed approach. Experimental outcomes indicate that proposed approach can detect smishing messages with the 94.20% true positive rate and 98.74% overall accuracy. Furthermore, the proposed approach is very efficient for the detection of the zero hour attack.

7

"Phishing, SMishing, and Vishing." In Mobile Malware Attacks and Defense, 125–96. Elsevier, 2009. http://dx.doi.org/10.1016/b978-1-59749-298-0.00006-9.

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Тези доповідей конференцій з теми "Smishing":

1

Boukari, Badr Eddine, Akshaya Ravi, and Mounira Msahli. "Machine Learning Detection for SMiShing Frauds." In 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC). IEEE, 2021. http://dx.doi.org/10.1109/ccnc49032.2021.9369640.

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2

Balim, Caner, and Efnan Sora Gunal. "Automatic Detection of Smishing Attacks by Machine Learning Methods." In 2019 1st International Informatics and Software Engineering Conference (UBMYK). IEEE, 2019. http://dx.doi.org/10.1109/ubmyk48245.2019.8965429.

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