Spam e-mail classification for the Internet of Things environment using semantic similarity approach

Venkatraman, S.; Surendiran, B.; Kumar, P. Arun Raj

doi:10.1007/s11227-019-02913-7

Cited by 36 publications

(21 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A few studies have also focused on phishing attack detection in IoT devices. Venkatraman et al [ 33 ] classified spam emails for IoT using the semantic similarity approach. This covers the existing deficiencies in spam detection techniques due to an inefficient detection approach because of the context-sensitive nature of the words.…”

Section: Related Workmentioning

confidence: 99%

Identifying and Mitigating Phishing Attack Threats in IoT Use Cases Using a Threat Modelling Approach

Vaccari

Hussain

Zahid

et al. 2021

Sensors

View full text Add to dashboard Cite

Internet of things (IoT) is a technology that enables our daily life objects to connect on the Internet and to send and receive data for a meaningful purpose. In recent years, IoT has led to many revolutions in almost every sector of our society. Nevertheless, security threats to IoT devices and networks are relentlessly disruptive, because of the proliferation of Internet technologies. Phishing is one of the most prevalent threats to all Internet users, in which attackers aim to fraudulently extract sensitive information of a user or system, using fictitious emails, websites, etc. With the rapid increase in IoT devices, attackers are targeting IoT devices such as security cameras, smart cars, etc., and perpetrating phishing attacks to gain control over such vulnerable devices for malicious purposes. In recent decades, such scams have been spreading, and they have become increasingly advanced over time. By following this trend, in this paper, we propose a threat modelling approach to identify and mitigate the cyber-threats that can cause phishing attacks. We considered two significant IoT use cases, i.e., smart autonomous vehicular system and smart home. The proposed work is carried out by applying the STRIDE threat modelling approach to both use cases, to disclose all the potential threats that may cause a phishing attack. The proposed threat modelling approach can support the IoT researchers, engineers, and IoT cyber-security policymakers in securing and protecting the potential threats in IoT devices and systems in the early design stages, to ensure the secure deployment of IoT devices in critical infrastructures.

show abstract

Section: Related Workmentioning

confidence: 99%

Identifying and Mitigating Phishing Attack Threats in IoT Use Cases Using a Threat Modelling Approach

Vaccari

Hussain

Zahid

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In contrast, although existing studies focusing on rumor detection are relatively fewer, papers relevant to spammer detection could shed light on the understanding of this area, especially for investigation of user-based features. This is because the mechanism of models, applied methods, and employed datasets in spammer detection research are largely similar with the ones used in rumor detection [ 15 , 25 , 27 , 35 ].…”

Section: Related Workmentioning

confidence: 99%

A two-step rumor detection model based on the supernetwork theory about Weibo

et al. 2021

View full text Add to dashboard Cite

Based on the supernetwork theory, a two-step rumor detection model was proposed. The first step was the classification of users on the basis of user-based features. In the second step, non-user-based features, including psychology-based features, content-based features, and parts of supernetwork-based features, were used to detect rumors posted by different types of users. Four machine learning methods, namely, Naive Bayes, Neural Network, Support Vector Machine, and Logistic Regression, were applied to train the classifier. Four real cases and several assessment metrics were employed to verify the effectiveness of the proposed model. Performance of the model regarding early rumor detection was also evaluated by separating the datasets according to the posting time of posts. Results showed that this model exhibited better performance in rumor detection compared to five benchmark models, mainly owing to the application of the supernetwork theory and the two-step mechanism.

show abstract

“…Similarly, in [44] a time based trend prediction approach is designed which works on a particular time frame on the based on citation trend. Feature based similarity and semantic based similarity approaches are also very common in social media text analytics and in [45,46], spam detection approach is designed using machine learning approaches. The authors have designed a methodology which is helpful to analyze the correlation between feature values and emotion values in a particular time series.…”

Section: Related Workmentioning

confidence: 99%

Burst: real-time events burst detection in social text stream

Singh

Kumari

2021

J Supercomput

View full text Add to dashboard Cite

Gigantic growth of social media and unbeatable trend of progress in the direction of the web seeking user's interests have generated a storm of social text streams. Seeking information to know the phenomenon of various events in the early stages is quite interesting. Various kinds of social media live streams attract users to participate in real-time events to become a part of an immense crowd. However, the vast amount of text is present on social media, the unnecessary information bogs a social text stream filtering to extract the appropriate topics and events effectively. Therefore, detecting, classifying, and identifying burst events is quite challenging due to the sparse and noisy text of Twitter. The researchers' significant open challenges are the effective cleaning and profound representation of the text stream data. This research article's main contribution is to provide a detailed study and explore bursty event detection in the social text stream. Thus, this work's main motive is to present a concise approach that classifies and detects the event keywords and maintains the record of the event based on related features. These features permit the approach to successfully determine the booming pattern of events scrupulously at different time span. Experiments are conducted and compared with the state-of-the-art methods, which reveals that the proposed approach is proficient to detect valuable patterns of interest and also achieve better scoresto extract burst events on social media posted by various users.

show abstract

Spam e-mail classification for the Internet of Things environment using semantic similarity approach

Cited by 36 publications

References 27 publications

Identifying and Mitigating Phishing Attack Threats in IoT Use Cases Using a Threat Modelling Approach

Identifying and Mitigating Phishing Attack Threats in IoT Use Cases Using a Threat Modelling Approach

A two-step rumor detection model based on the supernetwork theory about Weibo

Burst: real-time events burst detection in social text stream

Contact Info

Product

Resources

About