XFlow: Benchmarking Flow Behaviors over Graphs

The occurrence of diffusion on a graph is a prevalent and significant phenomenon, as evidenced by the spread of rumors, influenza-like viruses, smart grid failures, and similar events. Comprehending the behaviors of flow is a formidable task, due to the intricate interplay between the distribution of seeds that initiate flow propagation, the propagation model, and the topology of the graph. The study of networks encompasses a diverse range of academic disciplines, including mathematics, physics, social science, and computer science. This interdisciplinary nature of network research is characterized by a high degree of specialization and compartmentalization, and the cooperation facilitated by them is inadequate. From a machine learning standpoint, there is a deficiency in a cohesive platform for assessing algorithms across various domains. One of the primary obstacles to current research in this field is the absence of a comprehensive curated benchmark suite to study the flow behaviors under network scenarios. To address this disparity, we propose the implementation of a novel benchmark suite that encompasses a variety of tasks, baseline models, graph datasets, and evaluation tools. In addition, we present a comprehensive analytical framework that offers a generalized approach to numerous flow-related tasks across diverse domains, serving as a blueprint and roadmap. Drawing upon the outcomes of our empirical investigation, we analyze the advantages and disadvantages of current foundational models, and we underscore potential avenues for further study. The datasets, code, and baseline models have been made available for the public at: https://github.com/XGraphing/XFlo

Benchmark-Based Reference Model for Evaluating Botnet Detection Tools Driven by Traffic-Flow Analytics

Botnets are some of the most recurrent cyber-threats, which take advantage of the wide heterogeneity of endpoint devices at the Edge of the emerging communication environments for enabling the malicious enforcement of fraud and other adversarial tactics, including malware, data leaks or denial of service. There have been significant research advances in the development of accurate botnet detection methods underpinned on supervised analysis but assessing the accuracy and performance of such detection methods requires a clear evaluation model in the pursuit of enforcing proper defensive strategies. In order to contribute to the mitigation of botnets, this paper introduces a novel evaluation scheme grounded on supervised machine learning algorithms that enable the detection and discrimination of different botnets families on real operational environments. The proposal relies on observing, understanding and inferring the behavior of each botnet family based on network indicators measured at flow-level. The assumed evaluation methodology contemplates six phases that allow building a detection model against botnet-related malware distributed through the network, for which five supervised classifiers were instantiated were instantiated for further comparisons—Decision Tree, Random Forest, Naive Bayes Gaussian, Support Vector Machine and K-Neighbors. The experimental validation was performed on two public datasets of real botnet traffic—CIC-AWS-2018 and ISOT HTTP Botnet. Bearing the heterogeneity of the datasets, optimizing the analysis with the Grid Search algorithm led to improve the classification results of the instantiated algorithms. An exhaustive evaluation was carried out demonstrating the adequateness of our proposal which prompted that Random Forest and Decision Tree models are the most suitable for detecting different botnet specimens among the chosen algorithms. They exhibited higher precision rates whilst analyzing a large number of samples with less processing time. The variety of testing scenarios were deeply assessed and reported to set baseline results for future benchmark analysis targeted on flow-based behavioral patterns

Benchmark-Based Reference Model for Evaluating Botnet Detection Tools Driven by Traffic-Flow Analytics

Botnets are some of the most recurrent cyber-threats, which take advantage of the wide heterogeneity of endpoint devices at the Edge of the emerging communication environments for enabling the malicious enforcement of fraud and other adversarial tactics, including malware, data leaks or denial of service. There have been significant research advances in the development of accurate botnet detection methods underpinned on supervised analysis but assessing the accuracy and performance of such detection methods requires a clear evaluation model in the pursuit of enforcing proper defensive strategies. In order to contribute to the mitigation of botnets, this paper introduces a novel evaluation scheme grounded on supervised machine learning algorithms that enable the detection and discrimination of different botnets families on real operational environments. The proposal relies on observing, understanding and inferring the behavior of each botnet family based on network indicators measured at flow-level. The assumed evaluation methodology contemplates six phases that allow building a detection model against botnet-related malware distributed through the network, for which five supervised classifiers were instantiated were instantiated for further comparisons—Decision Tree, Random Forest, Naive Bayes Gaussian, Support Vector Machine and K-Neighbors. The experimental validation was performed on two public datasets of real botnet traffic—CIC-AWS-2018 and ISOT HTTP Botnet. Bearing the heterogeneity of the datasets, optimizing the analysis with the Grid Search algorithm led to improve the classification results of the instantiated algorithms. An exhaustive evaluation was carried out demonstrating the adequateness of our proposal which prompted that Random Forest and Decision Tree models are the most suitable for detecting different botnet specimens among the chosen algorithms. They exhibited higher precision rates whilst analyzing a large number of samples with less processing time. The variety of testing scenarios were deeply assessed and reported to set baseline results for future benchmark analysis targeted on flow-based behavioral patterns

Context-aware Synthesis for Video Frame Interpolation

Video frame interpolation algorithms typically estimate optical flow or its variations and then use it to guide the synthesis of an intermediate frame between two consecutive original frames. To handle challenges like occlusion, bidirectional flow between the two input frames is often estimated and used to warp and blend the input frames. However, how to effectively blend the two warped frames still remains a challenging problem. This paper presents a context-aware synthesis approach that warps not only the input frames but also their pixel-wise contextual information and uses them to interpolate a high-quality intermediate frame. Specifically, we first use a pre-trained neural network to extract per-pixel contextual information for input frames. We then employ a state-of-the-art optical flow algorithm to estimate bidirectional flow between them and pre-warp both input frames and their context maps. Finally, unlike common approaches that blend the pre-warped frames, our method feeds them and their context maps to a video frame synthesis neural network to produce the interpolated frame in a context-aware fashion. Our neural network is fully convolutional and is trained end to end. Our experiments show that our method can handle challenging scenarios such as occlusion and large motion and outperforms representative state-of-the-art approaches.Comment: CVPR 2018, http://graphics.cs.pdx.edu/project/ctxsy