${l}\,^2$-MPTCP: A Learning-Driven Latency-Aware Multipath Transport Scheme for Industrial Internet Applications

Cybercriminals often register many pornographic or gambling domains (known as abusive domains) with similar character compositions in bulk to reduce their investment in buying domains and make it easier for clients to remember and spread them. Therefore, this study combines the ideas of text similarity and text generation and proposes an abusive domain generation model based on GRU for rapidly generating new abusive domain names from known ones. Additionally, we develop a two-layer detection system for pornography and gambling domains using fastText and CNN models to obtain an abusive domain dataset for model training and validation. In the end, our detection system identifies pornographic and gambling domains with 99% precision while balancing correctness and speed. By inputting 40,000 random keywords into the abusive domain generation model, we obtained 130,220 online domains that served web pages, of which about 66% were pornographic or gambling domains. The results show that by exploiting cybercriminals’ behaviors in registering abusive domain names, such as bulk registration of similar domain names, we can prospectively acquire a large number of new abusive domains based on known ones. This study demonstrates that predicting new abusive domains not only expands the domain blacklist but also allows researchers to target the generated suspicious domains and dispose of them in time before they show abusive behavior.

Section: Prediction or Generation Based On Rnnmentioning

confidence: 99%

Section: Prediction or Generation Based On Rnnmentioning

confidence: 99%

Birds of a Feather Flock Together: Generating Pornographic and Gambling Domain Names Based on Character Composition Similarity

Cheng

Jiang

Zhang

et al. 2022

“…It is a crucial factor affecting the QoE of user experience [4]. In the study of resource allocation for computational offloading, with the goal of latency optimization, [28][29][30][31] schedule computational tasks through a Markov decision process, [14,32,33] consider game theory to obtain the best strategy for task offloading, and [11,12,[34][35][36][37][38][39] consider algorithms such as reinforcement learning to solve problems related to resource allocation. The lit-eratures [13,[15][16][17] introduced the MAB algorithm framework to learn online to adjust task allocation in real time.…”

Section: Related Workmentioning

confidence: 99%

G/M/1-Based DDoS Attack Mitigation in 5G Ultradense Cellular Networks

Gao

Wang

Wan

et al. 2022

Self Cite

With the 5G millimeter wave (mmWave) application, ultradense cellular networks are gradually becoming one of the core characteristics of 5G cellular networks. In the edge computing environment, considering load balancing among edge nodes is beneficial to slow down the process of distributed denial of service (DDoS) attack. However, most existing studies have given less consideration to congestion in the multiuser and multiedge server models. Someone who uses the M/M/1 model also seems to ignore the effect of scheduling algorithms on the Markov property of the task arrival process. In this manuscript, based on ensuring the quality of experience (QoE) for users, the G/M/1 model is introduced to the task scheduling of edge servers for the first time to improve load balancing between edge servers. For the multi armed bandit (MAB) algorithm framework, specific metrics are established to quantify the degree of its equilibrium. The number of users assigned to the edge nodes and each edge node’s processing of specific tasks is taken into account. We experimentally evaluated its performance against two baseline approaches and three state-of-the-art approaches on a real-world dataset. And the experimental results validate the effectiveness of this method.

“…Obtaining an accurate indoor location is a key for locationbased services such as seamless indoor-outdoor integrated navigation and multimedia information push in smart cities and augmented/virtual reality applications [1]. The demand for the high-precision location-based services in large indoor spaces is also becoming increasingly urgent.…”

Section: Introductionmentioning

confidence: 99%

A Sparse Feature Matching Model Using a Transformer towards Large-View Indoor Visual Localization

2022

Accurate indoor visual localization has been a challenging task under large-view scenes with wide baselines and weak texture images, where it is difficult to accomplish accurate image matching. To address the problem of sparse image features mismatching, we develop a coarse-to-fine feature matching model using a transformer, termed MSFA-T, which assigns the corresponding semantic labels to image features for an incipient coarse matching. To avoid the anomalous scoring of sparse feature interrelationship in the attention assigning phase, we propose a multiscale forward attention mechanism that decomposes the similarity-based features to learn the specificity of sparse features, the influence of position-independence on sparse features is reduced and the performance of the fine image matching in visual localization is effectively improved. We conduct extensive experiments on the challenging datasets; the results show that our model achieves image matching with an average 79.8% probability of the area under the cumulative curve of the corner point error, which outperforms the related state-of-the-art algorithms by an improvement of 13% probability at 1 m accuracy for the image-based visual localization in large view scenes.