Deep learning based cyber bullying early detection using distributed denial of service flow

Zaib, Muhammad Hassan; Bashir, Faisal; Qureshi, Kashif Naseer; Kausar, Sumaira; Rizwan, Muhammad; Jeon, Gwanggil

doi:10.1007/s00530-021-00771-z

Cited by 8 publications

(2 citation statements)

References 47 publications

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“…However, the authors perform a basic time-aware evaluation using precision and accuracy at k , and no penalty is introduced for late detection. Although the authors in [ 36 ] claim to be working on the early detection of cyberbullying, they in fact focus on computer network cyberattacks, and the time-aware performance evaluation is just based on the time required to make the prediction. Also in [ 37 ], an evaluation based on precision, recall, and F1 is used for the early detection of stress and depression.…”

Section: Related Workmentioning

confidence: 99%

Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

López-Vizcaíno¹,

Nóvoa²,

Artières

et al. 2023

Sensors

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The rise in the use of social media networks has increased the prevalence of cyberbullying, and time is paramount to reduce the negative effects that derive from those behaviours on any social media platform. This paper aims to study the early detection problem from a general perspective by carrying out experiments over two independent datasets (Instagram and Vine), exclusively using users’ comments. We used textual information from comments over baseline early detection models (fixed, threshold, and dual models) to apply three different methods of improving early detection. First, we evaluated the performance of Doc2Vec features. Finally, we also presented multiple instance learning (MIL) on early detection models and we assessed its performance. We applied timeawareprecision (TaP) as an early detection metric to asses the performance of the presented methods. We conclude that the inclusion of Doc2Vec features improves the performance of baseline early detection models by up to 79.6%. Moreover, multiple instance learning shows an important positive effect for the Vine dataset, where smaller post sizes and less use of the English language are present, with a further improvement of up to 13%, but no significant enhancement is shown for the Instagram dataset.

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Section: Related Workmentioning

confidence: 99%

Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

López-Vizcaíno¹,

Nóvoa²,

Artières

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Public datasets are available but not specifically created for smart home IoT networks. Some of the existing solutions suffer from overhead issues or increased time complexity [7]. Moreover, there are various issues observed, such as noise, overfitting, underfitting, complexity, and dimensionality, which lead to carelessness in data cleaning, feature extraction, selection, and normalization techniques.…”

mentioning

confidence: 99%

Intelligent Deep Learning for Anomaly-Based Intrusion Detection in IoT Smart Home Networks

et al. 2022

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The Internet of Things (IoT) is a tremendous network based on connected smart devices. These networks sense and transmit data by using advanced communication standards and technologies. The smart home is one of the areas of IoT networks, where home appliances are connected to the internet and smart grids. However, these networks are at high risk in terms of security violations. Different kinds of attacks have been conducted on these networks where the user lost their data. Intrusion detection systems (IDSs) are used to detect and prevent cyberattacks. These systems are based on machine and deep learning techniques and still suffer from fitting or overfitting issues. This paper proposes a novel solution for anomaly-based intrusion detection for smart home networks. The proposed model addresses overfitting/underfitting issues and ensures high performance in terms of hybridization. The proposed solution uses feature selection and hyperparameter tuning and was tested with an existing dataset. The experimental results indicated a significant increase in performance while minimizing misclassification and other limitations as compared to state-of-the-art solutions.

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Machine Learning-Based Detection and Prevention Systems for IoE

Khatoon,

Ullah,

Yasir

2023

Internet of Things

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Deep learning based cyber bullying early detection using distributed denial of service flow

Cited by 8 publications

References 47 publications

Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

Intelligent Deep Learning for Anomaly-Based Intrusion Detection in IoT Smart Home Networks

Machine Learning-Based Detection and Prevention Systems for IoE

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