An efficient deep learning-based scheme for web spam detection in IoT environment

Makkar, Aaisha; Kumar, Neeraj

doi:10.1016/j.future.2020.03.004

Cited by 55 publications

(19 citation statements)

References 44 publications

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“…Many researchers worked for spam detection in an IoT environment. Makkar and Kumar developed a spam detection approach by using deep learning 21 and machine learning 22 for an IoT environment. They developed new techniques for smart homes.…”

Section: Literature Reviewmentioning

confidence: 99%

Memetic Spider Monkey Optimization for Spam Review Detection Problem

2023

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Spider monkey optimization (SMO) algorithm imitates the spider monkey's fission-fusion social behavior. It is evident through literature that the SMO is a competitive swarm-based algorithm that is used to solve difficult real-life problems. The SMO's search process is a little bit biased by the random component that drives it with high explorative searching steps. A hybridized SMO with a memetic search to improve the local search ability of SMO is proposed here. The newly developed strategy is titled Memetic SMO (MeSMO). Further, the proposed MeSMO-based clustering approach is applied to solve a big data problem, namely, the spam review detection problem. A customer usually makes decisions to purchase something or make an image of someone based on online reviews. Therefore, there is a good chance that the individuals or companies may write spam reviews to upgrade or degrade the stature or value of a trader/product/company. Therefore, an efficient spam detection algorithm, MeSMO, is proposed and tested over four complex spam datasets. The reported results of MeSMO are compared with the outcomes obtained from the six state-of-art strategies. A comparative analysis of the results proved that MeSMO is a good technique to solve the spam review detection problem and improved precision by 3.68%.

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Section: Literature Reviewmentioning

confidence: 99%

Memetic Spider Monkey Optimization for Spam Review Detection Problem

2023

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“…Makkar and Kumar [71] proposed a deep learning model for web spam detection in an IoT environment. eir system enhances the cognitive ability of search engines for the detection of web spam.…”

Section: Artificial Neural Network An Artificial Neural Network (Annmentioning

confidence: 99%

Machine Learning Techniques for Spam Detection in Email and IoT Platforms: Analysis and Research Challenges

Ahmed

Amin

Aldabbas

et al. 2022

Security and Communication Networks

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Nowaday, emails are used in almost every field, from business to education. Emails have two subcategories, i.e., ham and spam. Email spam, also called junk emails or unwanted emails, is a type of email that can be used to harm any user by wasting his/her time, computing resources, and stealing valuable information. The ratio of spam emails is increasing rapidly day by day. Spam detection and filtration are significant and enormous problems for email and IoT service providers nowadays. Among all the techniques developed for detecting and preventing spam, filtering email is one of the most essential and prominent approaches. Several machine learning and deep learning techniques have been used for this purpose, i.e., Naïve Bayes, decision trees, neural networks, and random forest. This paper surveys the machine learning techniques used for spam filtering techniques used in email and IoT platforms by classifying them into suitable categories. A comprehensive comparison of these techniques is also made based on accuracy, precision, recall, etc. In the end, comprehensive insights and future research directions are also discussed.

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“…Also, an innovative approach based on hinge classification algorithm focused on mini-batch gradient descent with an adaptive learning rate and momentum could be adopted in the IoT context [60]. Other interesting approaches are based on exploiting formal verification applied to hybrid machine learning algorithm to detect malicious behavior in IoT [61] or using Long Short-Term Memory (LSTM) algorithm [62]. Finally, also classic algorithm such as Naïve Bayes, Random Forest, SVM or KNN are widely adopted and validated in the research community [63], [64].…”

Section: Related Workmentioning

confidence: 99%

Exploiting Internet of Things Protocols for Malicious Data Exfiltration Activities

et al. 2021

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Internet of Things is a widely adopted and pervasive technology, but also one of the most relevant in cyber-security, given the volume and sensitivity of shared data and the availability of affordable but insecure products. In this paper, we propose a novel cyber-threat exploiting the Message Queue Telemetry Transport (MQTT) protocol to implement a tunneling attack. In IoT networks, sensitive and critical information are exchanged between devices or external systems to perform data analysis. For this reason, a tunneling threat could be adopted by a malicious user to steal information. In this context, a tunneling system based on MQTT can be considered since this communication protocol could be allowed to pass through enterprise firewalls because it is widely adopted in this IoT world. An attacker can exploit the MQTT protocol for various purposes such as steal information or access to not-allowed websites/servers. In particular in this work, we contribute in two main points: initially we demonstrate how the proposed threat is able to encapsulate messages through the MQTT protocol, by also comparing it with other tunneling systems exploiting different communication protocols. Obtained results show that exploiting MQTT for tunneling purposes is a good choice, compared to other communication protocols, especially for payloads up to 3000 bytes. Then, we propose and validate an initial machine learning based approach able to detect the proposed MQTT tunnel, by comparing different detection algorithms tested with and without a hyperparameter optimization, in terms of accuracy, F1 score and Receiver Operating Characteristic (ROC) curve. In this case, obtained results show that some algorithms are able to identify the attack, with an accuracy exceeding 95%, while others lack of such capability.

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An efficient deep learning-based scheme for web spam detection in IoT environment

Cited by 55 publications

References 44 publications

Memetic Spider Monkey Optimization for Spam Review Detection Problem

Memetic Spider Monkey Optimization for Spam Review Detection Problem

Machine Learning Techniques for Spam Detection in Email and IoT Platforms: Analysis and Research Challenges

Exploiting Internet of Things Protocols for Malicious Data Exfiltration Activities

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