Related papers: Enhancing web traffic attacks identification throu…

Network traffic analytics technology is a cornerstone for cyber security systems. We demonstrate its use through three popular and contemporary cyber security applications in intrusion detection, malware analysis and botnet detection.…

A variety of statistical and machine learning methods are used to model crash frequency on specific roadways with machine learning methods generally having a higher prediction accuracy. Recently, heterogeneous ensemble methods (HEM),…

To address the problem that traditional network traffic anomaly detection algorithms do not suffi-ciently mine potential features in long time domain, an anomaly detection method based on mul-ti-scale residual features of network traffic is…

We evaluate methods for applying unsupervised anomaly detection to cybersecurity applications on computer network traffic data, or flow. We borrow from the natural language processing literature and conceptualize flow as a sort of…

Adversarial example attack endangers the mobile edge systems such as vehicles and drones that adopt deep neural networks for visual sensing. This paper presents {\em Sardino}, an active and dynamic defense approach that renews the inference…

Ensemble techniques for classification and clustering have long proven effective, yet anomaly ensembles have been barely studied. In this work, we tap into this gap and propose a new ensemble approach for anomaly mining, with application to…

Cybersecurity remains a critical challenge in the digital age, with network traffic flow anomaly detection being a key pivotal instrument in the fight against cyber threats. In this study, we address the prevalent issue of data integrity in…

Detecting anomalies in Internet of Things (IoT) networks is a critical security challenge, often hampered by highly imbalanced and diverse network traffic datasets. Standard classifiers struggle to perform well across all traffic types.…

Given the increased growing of Internet of Things networks and their presence in critical aspects of human activities, the security of devices connected to these networks becomes critical. Machine Learning approaches are becoming prominent…

The escalating sophistication of malware necessitates robust detection mechanisms that generalize across diverse data sources. Traditional single-dataset models struggle with cross-domain generalization and often incur high computational…

This study conducts a thorough examination of malware detection using machine learning techniques, focusing on the evaluation of various classification models using the Mal-API-2019 dataset. The aim is to advance cybersecurity capabilities…

This study uses various models to address network traffic classification, categorizing traffic into web, browsing, IPSec, backup, and email. We collected a comprehensive dataset from Arbor Edge Defender (AED) devices, comprising of 30,959…

The World Wide Web's connectivity is greatly attributed to the HTTP protocol, with HTTP messages offering informative header fields that appeal to disciplines like web security and privacy, especially concerning web tracking. Despite…

With rising concern around abusive and hateful behavior on social media platforms, we present an ensemble learning method to identify and analyze the linguistic properties of such content. Our stacked ensemble comprises of three machine…

Anomaly detection is a crucial step for preventing malicious activities in the network and keeping resources available all the time for legitimate users. It is noticed from various studies that classical anomaly detectors work well with…

Spurious credit card transactions are a significant source of financial losses and urge the development of accurate fraud detection algorithms. In this paper, we use machine learning strategies for such an aim. First, we apply a mixed…

The rapid growth of encryption has significantly enhanced privacy and security while posing challenges for network traffic classification. Recent approaches address these challenges by transforming network traffic into text or image formats…

An effective ranking model usually requires a large amount of training data to learn the relevance between documents and queries. User clicks are often used as training data since they can indicate relevance and are cheap to collect, but…

Internet traffic classification plays a key role in network visibility, Quality of Services (QoS), intrusion detection, Quality of Experience (QoE) and traffic-trend analyses. In order to improve privacy, integrity, confidentiality, and…

A significant number of traffic crashes are secondary crashes that occur because of an earlier incident on the road. Thus, early detection of traffic incidents is crucial for road users from safety perspectives with a potential to reduce…