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Статті в журналах з теми "Smishing"

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Автор: Grafiati

Опубліковано: 15 вересня 2021

Оновлено: 10 лютого 2022

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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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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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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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11

Lee, Si-Young, Hee-Soo Kang, and Jong-Sub Moon. "A Study on Smishing Block of Android Platform Environment." Journal of the Korea Institute of Information Security and Cryptology 24, no. 5 (October 31, 2014): 975–85. http://dx.doi.org/10.13089/jkiisc.2014.24.5.975.

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12

Park, In-Woo, and Dea-Woo Park. "A Study of Intrusion Security Research and Smishing Hacking Attack on a Smartphone." Journal of the Korea Institute of Information and Communication Engineering 17, no. 11 (November 30, 2013): 2588–94. http://dx.doi.org/10.6109/jkiice.2013.17.11.2588.

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13

Kim, Do-Young, and Sung-Mok Cho. "A Proposal of Smart Phone App for Preventing Smishing Attack." Journal of Security Engineering 12, no. 3 (June 30, 2015): 207–20. http://dx.doi.org/10.14257/jse.2015.06.08.

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14

Ustundag Soykan, Elif, and Mustafa Bagriyanik. "The Effect of SMiShing Attack on Security of Demand Response Programs." Energies 13, no. 17 (September 2, 2020): 4542. http://dx.doi.org/10.3390/en13174542.

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Анотація:

Demand response (DR) is a vital element for a reliable and sustainable power grid. Consumer behavior is a key factor in the success of DR programs. In this study, we focus on how consumer reaction to Short Messaging Service (SMS) messages can disturb the demand response. We present a new type of threat to DR programs using SMS phishing attacks. We follow a holistic approach starting from a risk assessment focusing on DR programs’ notification message security following the Smart Grid Information Security (SGIS) risk methodology. We identify threats, conduct impact analysis, and estimate the likelihood of the attacks for various attacker types and motivations. We implemented deterministic and randomized attack scenarios to demonstrate the success of the attack using a state-of-the-art simulator on the IEEE European Low Voltage Feeder Test System. Simulations show that the attack results in local outages, which may lead to large-scale blackouts with the cascading effect on the power system. We conclude that this is a new type of threat that has been overlooked, and it deserves more attention as mobile devices will continually be part of our lives.

15

Jain, Ankit Kumar, and B. B. Gupta. "Rule-Based Framework for Detection of Smishing Messages in Mobile Environment." Procedia Computer Science 125 (2018): 617–23. http://dx.doi.org/10.1016/j.procs.2017.12.079.

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16

Choi, Hark-Kyu, and Hwang-Rae Kim. "Design and Implementation of a Malicious SMS Training System for Preventing Smishing." Journal of Korean Institute of Information Technology 17, no. 10 (October 31, 2019): 93–99. http://dx.doi.org/10.14801/jkiit.2019.17.10.93.

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17

Joo, Jae Woong, Seo Yeon Moon, Saurabh Singh, and Jong Hyuk Park. "S-Detector: an enhanced security model for detecting Smishing attack for mobile computing." Telecommunication Systems 66, no. 1 (January 11, 2017): 29–38. http://dx.doi.org/10.1007/s11235-016-0269-9.

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18

Kang, Ji Won, Ae Ri Lee, and Beomsoo Kim. "Improving Security Awareness about Smishing through Experiment on the Optimistic Bias on Risk Perception." Journal of the Korea Institute of Information Security and Cryptology 26, no. 2 (April 30, 2016): 475–87. http://dx.doi.org/10.13089/jkiisc.2016.26.2.475.

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19

Joo, Choon Kyong, and Ji Won Yoon. "Discrimination of SPAM and prevention of smishing by sending personally identified SMS(For financial sector)." Journal of the Korea Institute of Information Security and Cryptology 24, no. 4 (August 31, 2014): 645–53. http://dx.doi.org/10.13089/jkiisc.2014.24.4.645.

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20

Ojugo, Arnold Adimabua, and Andrew Okonji Eboka. "Memetic algorithm for short messaging service spam filter using text normalization and semantic approach." International Journal of Informatics and Communication Technology (IJ-ICT) 9, no. 1 (April 1, 2020): 9. http://dx.doi.org/10.11591/ijict.v9i1.pp9-18.

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Анотація:

Today’s popularity of the short messages services (SMS) has created a propitious environment for spamming to thrive. Spams are unsolicited advertising, adult-themed or inappropriate content, premium fraud, smishing and malware. They are a constant reminder of the need for an effective spam filter. However, SMS limitations of 160-charcaters and 140-bytes size as well as its being rippled with slangs, emoticons and abbreviations further inhibits effective training of models to aid accurate classification. The study proposes Genetic Algorithm Trained Bayesian Network solution that seeks to normalize noisy feats, expand text via use of lexicographic and semantic dictionaries that uses word sense disambiguation technique to train the underlying learning heuristics. And in turn, effectively help to classify SMS in spam and legitimate classes. Hybrid model comprises of text preprocessing, feature selection as well as training and classification section. Study uses a hybrid Genetic Algorithm trained Bayesian model for which the GA is used for feature selection; while, the Bayesian algorithm is used as classifier.

21

Seo, Gil-Won, and Il-Young Moon. "A Study of Technical Countermeasure System for the Smishing Detection and Prevention Based on the Android Platform." Journal of Korea Navigation Institute 18, no. 6 (December 30, 2014): 569–75. http://dx.doi.org/10.12673/jant.2014.18.6.569.

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22

Ghourabi, Abdallah, Mahmood A. Mahmood, and Qusay M. Alzubi. "A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages." Future Internet 12, no. 9 (September 18, 2020): 156. http://dx.doi.org/10.3390/fi12090156.

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Despite the rapid evolution of Internet protocol-based messaging services, SMS still remains an indisputable communication service in our lives until today. For example, several businesses consider that text messages are more effective than e-mails. This is because 82% of SMSs are read within 5 min., but consumers only open one in four e-mails they receive. The importance of SMS for mobile phone users has attracted the attention of spammers. In fact, the volume of SMS spam has increased considerably in recent years with the emergence of new security threats, such as SMiShing. In this paper, we propose a hybrid deep learning model for detecting SMS spam messages. This detection model is based on the combination of two deep learning methods CNN and LSTM. It is intended to deal with mixed text messages that are written in Arabic or English. For the comparative evaluation, we also tested other well-known machine learning algorithms. The experimental results that we present in this paper show that our CNN-LSTM model outperforms the other algorithms. It achieved a very good accuracy of 98.37%.

23

Ryu, Gwonsang, Seung-Hyun Kim, and Daeseon Choi. "Implicit Secondary Authentication for Sustainable SMS Authentication." Sustainability 11, no. 1 (January 8, 2019): 279. http://dx.doi.org/10.3390/su11010279.

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Short message service (SMS) is the most widely adopted multi-factor authentication method for consumer-facing accounts. However, SMS authentication is susceptible to vulnerabilities such as man-in-the-middle attack, smishing, and device theft. This study proposes implicit authentication based on behavioral pattern of users when they check an SMS verification code and environmental information of user proximity to detect device theft. User behavioral pattern is collected by using the accelerometer and gyroscope of a smart device such as a smartphone and smart watch. User environmental information is collected using device fingerprint, wireless access point, Bluetooth, and global positioning system information. To evaluate the performance of the proposed scheme, we perform experiments using a total of 1320 behavioral and environmental data collected from 22 participants. The scheme achieves an average equal error rate of 6.27% when using both behavioral and environmental data collected from only a smartphone. Moreover, it achieves an average equal error rate of 0% when using both behavioral and environmental data collected from a smartphone and smart watch. Therefore, the proposed scheme can be employed for more secure SMS authentication.

24

Ali, Guma, Mussa Ally Dida, and Anael Elikana Sam. "Evaluation of Key Security Issues Associated with Mobile Money Systems in Uganda." Information 11, no. 6 (June 8, 2020): 309. http://dx.doi.org/10.3390/info11060309.

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Smartphone technology has improved access to mobile money services (MMS) and successful mobile money deployment has brought massive benefits to the unbanked population in both rural and urban areas of Uganda. Despite its enormous benefits, embracing the usage and acceptance of mobile money has mostly been low due to security issues and challenges associated with the system. As a result, there is a need to carry out a survey to evaluate the key security issues associated with mobile money systems in Uganda. The study employed a descriptive research design, and stratified random sampling technique to group the population. Krejcie and Morgan’s formula was used to determine the sample size for the study. The collection of data was through the administration of structured questionnaires, where 741 were filled by registered mobile money (MM) users, 447 registered MM agents, and 52 mobile network operators’ (MNOs) IT officers of the mobile money service providers (MMSPs) in Uganda. The collected data were analyzed using RStudio software. Statistical techniques like descriptive analysis and Pearson Chi-Square test was used in data analysis and mean (M) > 3.0 and p-value < 0.05 were considered statistically significant. The findings revealed that the key security issues are identity theft, authentication attack, phishing attack, vishing attack, SMiShing attack, personal identification number (PIN) sharing, and agent-driven fraud. Based on these findings, the use of better access controls, customer awareness campaigns, agent training on acceptable practices, strict measures against fraudsters, high-value transaction monitoring by the service providers, developing a comprehensive legal document to run mobile money service, were some of the proposed mitigation measures. This study, therefore, provides a baseline survey to help MNO and the government that would wish to implement secure mobile money systems.

25

Lötter, André, and Lynn Futcher. "A framework to assist email users in the identification of phishing attacks." Information & Computer Security 23, no. 4 (October 12, 2015): 370–81. http://dx.doi.org/10.1108/ics-10-2014-0070.

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Purpose – The purpose of this paper is to propose a framework to address the problem that email users are not well-informed or assisted by their email clients in identifying possible phishing attacks, thereby putting their personal information at risk. This paper therefore addresses the human weakness (i.e. the user’s lack of knowledge of phishing attacks which causes them to fall victim to such attacks) as well as the software related issue of email clients not visually assisting and guiding the users through the user interface. Design/methodology/approach – A literature study was conducted in the main field of information security with a specific focus on understanding phishing attacks and a modelling technique was used to represent the proposed framework. This paper argues that the framework can be suitably implemented for email clients to raise awareness about phishing attacks. To validate the framework as a plausible mechanism, it was reviewed by a focus group within the School of Information and Communication Technology (ICT) at the Nelson Mandela Metropolitan University (NMMU). The focus group consisted of academics and research students in the field of information security. Findings – This paper argues that email clients should make use of feedback mechanisms to present security related aspects to their users, so as to make them aware of the characteristics pertaining to phishing attacks. To support this argument, it presents a framework to assist email users in the identification of phishing attacks. Research limitations/implications – Future research would yield interesting results if the proposed framework were implemented into an existing email client to determine the effect of the framework on the user’s level of awareness of phishing attacks. Furthermore, the list of characteristics could be expanded to include all phishing types (such as clone phishing, smishing, vishing and pharming). This would make the framework more dynamic in that it could then address all forms of phishing attacks. Practical implications – The proposed framework could enable email clients to provide assistance through the user interface. Visibly relaying the security level to the users of the email client, and providing short descriptions as to why a certain email is considered suspicious, could result in raising the awareness of the average email user with regard to phishing attacks. Originality/value – This research presents a framework that email clients can use to identify common forms of normal and spear phishing attacks. The proposed framework addresses the problem that the average Internet user lacks a baseline level of online security awareness. It argues that the email client is the ideal place to raise the awareness of users regarding phishing attacks.

26

Mishra, Sandhya, and Devpriya Soni. "DSmishSMS-A System to Detect Smishing SMS." Neural Computing and Applications, July 28, 2021. http://dx.doi.org/10.1007/s00521-021-06305-y.

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27

Mishra, Sandhya, and Devpriya Soni. "A Content-Based Approach for Detecting Smishing in Mobile Environment." SSRN Electronic Journal, 2019. http://dx.doi.org/10.2139/ssrn.3356256.

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28

Ghourabi, Abdallah. "SM‐Detector: A security model based on BERT to detect SMiShing messages in mobile environments." Concurrency and Computation: Practice and Experience, June 14, 2021. http://dx.doi.org/10.1002/cpe.6452.

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