A Study on Realtime Detecting Smishing on Cloud Computing Environments

Lee, Ayoung; Kim, Kyounghun; Lee, Heeman; Jun, Moon-Seog

doi:10.1007/978-3-662-47895-0_60

Cited by 9 publications

(5 citation statements)

References 5 publications

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“…During the literature survey, it was observed that few research papers [ 9 , 10 , 12 – 18 ] are contributing to the study of SMS classification using Neural Network whereas other research papers [ 4 , 8 , 11 , 19 – 22 ] are related to the study of smishing detection and evaluating the detection models using various techniques. Some research papers [ 23 – 28 ] are focused on the detection of phishing.…”

Section: Related Work Studymentioning

confidence: 99%

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Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural Network

Mishra

Soni

2022

SN COMPUT. SCI.

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Neural network creates a neuron-based network similar to the human nervous system to solve classification problems efficiently. The smishing problem is a binary classification problem in which attackers target smartphone users through text messages. As smishing is a remarkable cybersecurity issue that is troubling researchers and smartphone users these days. Addressing this security issue using the most efficient algorithm is the need of the hour. This manuscript presented an algorithm for the model proposed by authors in ‘Smishing Detector’ model and implemented it using Neural Network. The result obtained proves that the neural network is much efficient in detecting smishing problem. Neural Network outperformed other machine learning algorithms with a difference of 1.11%. Neural Network performed with the final accuracy of 97.40%. In this paper, system extracted the most efficient features of smishing SMS (Short Message Service) using the Neural Network. This manuscript also reported the accuracy shown by the system for each feature selected and implemented. It is evident from the implementation that each feature selected is most effective in smishing detection and URL (Uniform Resource Locator) feature is the most effective feature with an accuracy of 94%.

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Section: Related Work Studymentioning

confidence: 99%

“…Lee et al. [ 8 ] implemented a cloud-based virtual environment to verify the downloading of a malicious executable file and in turn detects the maliciousness of the message.…”

Section: Related Work Studymentioning

confidence: 99%

Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural Network

Mishra

Soni

2022

SN COMPUT. SCI.

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“…This information includes contacts, notes, financial information, pictures, etc. This trend subsequently steals personal information such as security cards, photo identification, or accreditation certificates for other crimes [52]. Various forms of text messages are also found to be compromised by malware, such as the provision of coupons to wedding invitations.…”

Section: G Smsmentioning

confidence: 99%

Phishing Classification Techniques: A Systematic Literature Review

et al. 2022

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Phishing has become a serious and concerning problem within the past 10 years, with many reviews describing attack patterns and anticipating different method utilizations. This indicates that the results are still not comprehensive, subsequently leaving a critical gap in phishing reports. Therefore, this study aims to conduct a systematic review, to show a more crucial issue in phishing attacks, namely classification techniques. These issues were categorized into techniques, datasets, performance evaluation, and phishing types. The obtained results are expected to help developers prevent future phishing attacks more effectively, especially in selectively and carefully determining the techniques and evaluations to address specific types of phishing.

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“…Phishing is mostly carried out through technological means, such as emails and phone calls. Phishing attacks frequently take the shape of an email purporting to be from a reputable organisation, such as your bank, [95] the tax department, or another trustworthy entity, as seen in figure 3. Now we'll go over the most frequent sorts of phishing assaults in this part.…”

Section: Phishing Attackmentioning

confidence: 99%

A Systematic Literature Review on the Cyber Security

Perwej

Abbas

Dixit³

et al. 2021

int.jour.sci.res.mana.

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In recent years, the Internet has become an integral element of people's everyday lifestyles all across the world. Online criminality, on the other hand, has risen in tandem with the growth of Internet activity. Cyber security has advanced greatly in recent years in order to keep up with the rapid changes that occur in cyberspace. Cyber security refers to the methods that a country or organization can use to safeguard its products and information in cyberspace. Two decades ago, the term "cyber security" was barely recognized by the general public. Cyber security isn't just a problem that affects individuals but it also applies to an organization or a government. Everything has recently been digitized, with cybernetics employing a variety of technologies such as cloud computing, smart phones, and Internet of Things techniques, among others. Cyber-attacks are raising concerns about privacy, security, and financial compensation. Cyber security is a set of technologies, processes, and practices aimed at preventing attacks, damage, and illegal access to networks, computers, programmes, and data. The primary goal of this article is to conduct a thorough examination of cyber security kinds, why cyber security is important, cyber security framework, cyber security tools, and cyber security difficulties. Cyber security safeguards the data and integrity of computing assets that are part of or connected to an organization's network, with the goal of defending such assets from all threat actors throughout the life cycle of a cyber-attack.

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A Study on Realtime Detecting Smishing on Cloud Computing Environments

Cited by 9 publications

References 5 publications

Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural Network

Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural Network

Phishing Classification Techniques: A Systematic Literature Review

A Systematic Literature Review on the Cyber Security

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