Figure 1 - uploaded by Marwan Omar
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Internet traffic (petabytes per year)
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The repeal of net neutrality has caused a great public outcry from academic down to the end-users. Net neutrality was an FCC order that specified the principles for Internet Service Providers. The most prevalent principles were related to bandwidth throttling, preferential treatments, and privacy. Some described the action of the FCC will lead to t...
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Context 1
... was one of the main premises for ISPs for setting priorities, providing preferential services, and charging tiers for Internet traffic. Figure 1 shows the distribution of Internet traffic based on Web/Data, P2P/File Sharing, and Internet Video. The traffic data were compiled from 2005 to 2016. ...
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Citations
This chapter presents an in-depth study on the application of decision tree-based classifiers for the detection of malware in internet of things (IoT) environments. With the burgeoning expansion of IoT devices, the threat landscape has grown increasingly complex, making traditional security measures insufficient. This study proposes an innovative approach using decision tree algorithms to address the growing concern of IoT malware. The research methodology encompasses a comprehensive analysis of IoT vulnerabilities, focusing on malware threats and the development of a decision tree-based classifier. The classifier is empirically validated using the MaleVis dataset, a rich source of real-world IoT malware data. Performance metrics such as precision, recall, specificity, F1-score, accuracy, and processing time are meticulously evaluated to determine the efficacy of the model.
This study explores the efficacy of the bidirectional encoder representations from transformers (BERT) model in the domain of Android malware detection, comparing its performance against traditional machine learning models such as convolutional neural networks (CNNs) and long short-term memory (LSTMs). Employing a comprehensive methodology, the research utilizes two significant datasets, the Drebin dataset and the CIC AndMal2017 dataset, known for their extensive collection of Android malware and benign applications. The models are evaluated based on accuracy, precision, recall, and F1 score. Additionally, the study addresses the challenge of concept drift in malware detection by incorporating active learning techniques to adapt to evolving malware patterns. The results indicate that BERT outperforms traditional models, demonstrating higher accuracy and adaptability, primarily due to its advanced natural language processing capabilities. This study contributes to the field of cybersecurity and NLP.
As cyber attacks continue to increase in frequency and sophistication, detecting malware has become a critical task for maintaining the security of computer systems. Traditional signature-based methods of malware detection have limitations in detecting complex and evolving threats. In recent years, machine learning (ML) has emerged as a promising solution to detect malware effectively. ML algorithms are capable of analyzing large datasets and identifying patterns that are difficult for humans to identify. This paper presents a comprehensive review of the state-of-the-art ML techniques used in malware detection, including supervised and unsupervised learning, deep learning, and reinforcement learning. We also examine the challenges and limitations of ML-based malware detection, such as the potential for adversarial attacks and the need for large amounts of labeled data. Furthermore, we discuss future directions in ML-based malware detection, including the integration of multiple ML algorithms and the use of explainable AI techniques to enhance the interpret ability of ML-based detection systems. Our research highlights the potential of ML-based techniques to improve the speed and accuracy of malware detection, and contribute to enhancing cybersecurity
Recently, deep learning techniques have garnered substantial attention for their ability to identify vulnerable code patterns accurately. However, current state-of-the-art deep learning models, such as Convolutional Neural Networks (CNN), and Long Short-Term Memories (LSTMs) require substantial computational resources. This results in a level of overhead that makes their implementation unfeasible for deployment in realtime settings. This study presents a novel transformer-based vulnerability detection framework, referred to as VulDetect, which is achieved through the fine-tuning of a pre-trained large language model, (GPT) on various benchmark datasets of vulnerable code. Our empirical findings indicate that our framework is capable of identifying vulnerable software code with an accuracy of up to 92.65%. Our proposed technique outperforms SyseVR and VulDeBERT, two state-of-the-art vulnerability detection techniques
As machine learning (ML) systems are being increasingly employed in the real world to handle sensitive tasks and make decisions in various fields, the security and privacy of those models have also become increasingly critical. In particular, Deep Neural Networks (DNN) have been shown to be vulnerable to backdoor attacks whereby adversaries have access to the training data and the opportunity to manipulate such data by inserting carefully developed samples into the training dataset. Although the NLP community has produced several studies on generating backdoor attacks proving the vulnerable state of language modes, to the best of our knowledge, there does not exist any work to combat such attacks. To bridge this gap, we present RobustEncoder: a novel clustering-based technique for detecting and removing backdoor attacks in the text domain. Extensive empirical results demonstrate the effectiveness of our technique in detecting and removing backdoor triggers. Our code is available at https://github.com/marwanomar1/Backdoor-Learning-for-NLP
Cyber-crimes have become a multi-billion-dollar industry in the recent years. Most cybercrimes/attacks involve deploying some type of malware. Malware that viciously targets every industry, every sector, every enterprise and even individuals has shown its capabilities to take entire business organizations offline and cause significant financial damage in billions of dollars annually. Malware authors are constantly evolving in their attack strategies and sophistication and are developing malware that is difficult to detect and can lay dormant in the background for quite some time in order to evade security controls. Given the above argument, Traditional approaches to malware detection are no longer effective. As a result, deep learning models have become an emerging trend to detect and classify malware. This paper proposes a new convolutional deep learning neural network to accurately and effectively detect malware with high precision. This paper is different than most other papers in the literature in that it uses an expert data science approach by developing a convolutional neural network from scratch to establish a baseline of the performance model first, explores and implements an improvement model from the baseline model, and finally it evaluates the performance of the final model. The baseline model initially achieves 98% accurate rate but after increasing the depth of the CNN model, its accuracy reaches 99.183 which outperforms most of the CNN models in the literature. Finally, to further solidify the effectiveness of this CNN model, we use the improved model to make predictions on new malware samples within our dataset.
