Predicting Spam Messages Using Back Propagation Neural Network

Jain, Ankit Kumar; Goel, Diksha; Agarwal, Sanjli; Singh, Yukta; Bajaj, Gaurav

doi:10.1007/s11277-019-06734-y

Cited by 29 publications

(11 citation statements)

References 14 publications

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“…Artificial Intelligence and Machine Learning are widely accepted and utilized in various fields like Cyber Security [94][95][96][97][98][99][100][101][102][103], design and manufacturing [104], medicine [105][106][107][108], education [109], and finance [110][111][112]. Machine Learning techniques are used widely in the following areas of cyber security intrusion detection [113][114][115][116], dark web or deep web sites [117,118], phishing [119][120][121], malware detection [122][123][124][125], fraud detection [126][127][128][129], and spam classification [130][131][132][133]. As time changes there is a need for vigorous and novel techniques to address the issues of cyber security.…”

Section: Machinementioning

confidence: 99%

A Review on Machine Learning Strategies for Real-World Engineering Applications

Jhaveri

Revathi

Ramana³

et al. 2022

Mobile Information Systems

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Huge amounts of data are circulating in the digital world in the era of the Industry 5.0 revolution. Machine learning is experiencing success in several sectors such as intelligent control, decision making, speech recognition, natural language processing, computer graphics, and computer vision, despite the requirement to analyze and interpret data. Due to their amazing performance, Deep Learning and Machine Learning Techniques have recently become extensively recognized and implemented by a variety of real-time engineering applications. Knowledge of machine learning is essential for designing automated and intelligent applications that can handle data in fields such as health, cyber-security, and intelligent transportation systems. There are a range of strategies in the field of machine learning, including reinforcement learning, semi-supervised, unsupervised, and supervised algorithms. This study provides a complete study of managing real-time engineering applications using machine learning, which will improve an application's capabilities and intelligence. This work adds to the understanding of the applicability of various machine learning approaches in real-world applications such as cyber security, healthcare, and intelligent transportation systems. This study highlights the research objectives and obstacles that Machine Learning approaches encounter while managing real-world applications. This study will act as a reference point for both industry professionals and academics, and from a technical standpoint, it will serve as a benchmark for decision-makers on a range of application domains and real-world scenarios.

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Section: Machinementioning

confidence: 99%

A Review on Machine Learning Strategies for Real-World Engineering Applications

Jhaveri

Revathi

Ramana³

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…The accuracy shown by their model is 95%. In the research paper [ 14 ], the backpropagation algorithm is used for the classification of spam messages. 14 spam features of the messages were selected for the identification of spam messages and finally, their classification showed an accuracy of 95.81%.…”

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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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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“…In the current decade, Big data analytics is being devoted increased attention as its involved in extraction in information, wisdom and knowledge from Big Data such as from social media [1][2][3]. Big Data in the literature is characterized by Variety, Volume and Velocity along with complexity as huge population are using it at an instant.…”

Section: Introductionmentioning

confidence: 99%

Cyber Threat Detection Based On Angle-Based Subspace Anomaly For Machine Learning Applications

Revathy

2022

Preprint

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In real time applications related to social media, conventional anomaly detection techniques are not applicable as the accuracy is degraded due to higher dimensionality thereby hampering these applications leading to cyber threat. This paper focuses on an approach that can select feature subspaces of social media which have meaningful information and thereby conduct anomaly detection in the projected subspace correspondingly. Major goal is to maintain the accuracy of detection in the circumstance of high dimensionality detecting cyber threat. This approach determines the angle between any of the two lines for one of the anomaly candidate specifically where the first line is in connection with relevant data points along with centers of adjacent points and the other line is any parallel axis line. For a particular candidate in social media, any dimension having smallest angle with the first line is chosen as subspace parallel to the axis. In the projected subspace, the local outlierness is measured for an object by introducing normalized values of Mahalanobis distance. Artificial datasets are constructed for comparing proposed approach of detecting cyber threats in social media comprehensively and found to be accurate for machine learning applications.

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Predicting Spam Messages Using Back Propagation Neural Network

Cited by 29 publications

References 14 publications

A Review on Machine Learning Strategies for Real-World Engineering Applications

A Review on Machine Learning Strategies for Real-World Engineering Applications

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

Cyber Threat Detection Based On Angle-Based Subspace Anomaly For Machine Learning Applications

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