Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm

Almomani, Ammar; Nawasrah, Ahmad Al; Meziane, Farid; Al-Betar, Mohammed Azmi; Alauthman, Mohammad

doi:10.1504/ijahuc.2021.10035248

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…Fast-flux manipulates DNS bindings to change IPs mapped to C&C domains rapidly. These behaviours deviate from legitimate DNS traffic [13].…”

Section: Dns-based Detectionmentioning

confidence: 89%

Botnet Detection Techniques: A Comparative Study

Alauthman

2023

Preprint

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Botnets pose a grave cybersecurity threat, enabling widescale malicious activities through networks of compromised devices. Detecting botnets is challenging given their frequent use of evasion techniques like encryption. Traditional signature-based methods fail against modern botnets capable of zero-day attacks. This paper surveys recent advances applying machine learning for botnet detection based on analysis of network traffic payloads, flows, DNS data, and hybrid feature fusion. Core machine learning models include support vector machines, neural networks, random forests, and deep learning architectures, which extract patterns to separate benign and botnet behaviors automatically. Results demonstrate machine learning's capabilities in identifying heterogeneous botnets using artefacts in network streams. However, challenges remain around limited labeled data, real-time streaming, adversarial evasion, and model interpretability. Promising directions involve semi-supervised learning, adversarial training, scalable analytics, and explainable AI to address these gaps. Beyond the technical aspects, responsible development and deployment of botnet detection systems raise ethical considerations around privacy, transparency, and accountability. With diligent cross-disciplinary collaboration, machine learning promises enhanced, generalizable, and trustworthy techniques to combat the serious threat posed by continuously evolving botnets across the digital ecosystem.

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“…Fast-flux manipulates DNS bindings to change IPs mapped to C&C domains rapidly. These behaviours deviate from legitimate DNS traffic [13].…”

Section: Dns-based Detectionmentioning

confidence: 89%

Botnet Detection Techniques: A Comparative Study

Alauthman

2023

Preprint

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“…However, the individual use of either LSTM or CNN for cyberbullying detection, while showing merit, is not devoid of limitations. An intriguing proposition, therefore, is the amalgamation of these networks, aiming to harness their collective strengths for enhanced performance [8]. The crux of this research paper is the conceptualization, development, and evaluation of a hybrid LSTM-CNN neural network tailored for the rigorous task of cyberbullying detection on diverse social media platforms.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Convolutional Recurrent Neural Network for Cyberbullying Detection on Textual Data

Baiganova,

Toxanova,

Yerekesheva

et al. 2024

ijacsa

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With the burgeoning use of social media platforms, online harassment and cyberbullying have become significant concerns. Traditional mechanisms often falter, necessitating advanced methodologies for efficient detection. This study presents an innovative approach to identifying cyberbullying incidents on social media sites, employing a hybrid neural network architecture that amalgamates Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). By harnessing the sequential processing capabilities of LSTM to analyze the temporal progression of textual data, and the spatial discernment of CNN to pinpoint bullying keywords and patterns, the model demonstrates substantial improvement in detection accuracy compared to extant methods. A diverse dataset, encompassing multiple social media platforms and linguistic styles, was utilized to train and test the model, ensuring robustness. Results evince that the LSTM-CNN amalgamation can adeptly handle varied sentence structures and contextual nuances, outstripping traditional machine learning classifiers in both specificity and sensitivity. This research underscores the potential of hybrid neural networks in addressing contemporary digital challenges, urging further exploration into blended architectures for nuanced problem-solving in cyber realms.

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Classification and Generation of Arabic News Titles from Raw Text Based on an Encoder-Decoder Transformer Model (mT5)

Mohsen‬‏,

Al-Nahari,

Alsubari

2024

Preprint

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Multilingual Transformer 5 (MT5) is a versatile architecture in natural language processing (NLP) that demonstrates proficiency across various languages. This study aimed to improve the performance of the MT5 model in two key tasks: topic classification and headline generation. The datasets used were 183K and 294K samples. The classification task involved categorizing news articles, while the news generation task aimed to create coherent and contextually relevant Arabic news content. Through careful fine-tuning and rigorous evaluation, the MT5 model significantly advances its ability to address complex challenges in Arabic NLP. This study provides practical insights into real-world applications in processing Arab news. The performance of the MT5 model was evaluated using various online platforms. The mT5small model achieved an accuracy of 0.7858 and an F1 score of 0.7858, while the mT5base model achieved an accuracy of 0.8230 and an F1 score of 0.8230. The generative approach for headline generation yielded Rouge-1, Rouge-2, and Rouge-L scores under the task "Generative of Headlines." These outcomes demonstrate the effectiveness of the fine-tuned MT5 model across various evaluation metrics and tasks, confirming its potential for practical applications in Arabic NLP.

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Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm

Cited by 3 publications

References 41 publications

Botnet Detection Techniques: A Comparative Study

Botnet Detection Techniques: A Comparative Study

Hybrid Convolutional Recurrent Neural Network for Cyberbullying Detection on Textual Data

Classification and Generation of Arabic News Titles from Raw Text Based on an Encoder-Decoder Transformer Model (mT5)

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