Naver Corporation

About: Naver Corporation is a company organization based out in Seongnam-si, South Korea. It is known for research contribution in the topics: Terminal (electronics) & Computer science. The organization has 4038 authors who have published 4294 publications receiving 35045 citations. The organization is also known as: NAVER Corporation & NAVER.

HXD: Hybrid XSS detection by using a headless browser

Abstract: Cross-Site Scripting (XSS) is the most prevalent web application vulnerability that occurs when a web application implements insufficient input validation or output sanitation. Adversaries can use XSS to deliver a malicious script which leads to session hijacking, credential theft, privilege escalation, etc. In order to detect XSS attacks, several works have been proposed and implemented. Although the detection of XSS vulnerability is fairly easy via static or dynamic analysis, it still remains nontrivial because most approaches fall into three problems: (1) too slow to analyze tons of URLs, (2) have some missing cases (i.e., false negatives), (3) produce unignorable number of false positives. In this paper, we propose a hybrid XSS detection (HXD) system, a black-box based XSS detection approach using both static string analysis and dynamic browser rendering. We extract URLs from web logs and refine them as proper input URLs. Therefore, HXD does not need to crawl or fuzz URL inputs. HXD uses PhantomJS, a headless browser to execute a javascript and detect XSS flaws so it can detect XSS vulnerabilities in javascript frameworks. The static analyzer of HXD utilizes string analysis based approach to accelerate the detection speed. We evaluate HXD by using web logs of Korean major internet portal (i.e., Naver). Our evaluation result demonstrates that HXD has low false positives and detects XSS flaws missed by other black-box based detectors.

Naver Labs Europe’s Systems for the Document-Level Generation and Translation Task at WNGT 2019

Abstract: Recently, neural models led to significant improvements in both machine translation (MT) and natural language generation tasks (NLG). However, generation of long descriptive summaries conditioned on structured data remains an open challenge. Likewise, MT that goes beyond sentence-level context is still an open issue (e.g., document-level MT or MT with metadata). To address these challenges, we propose to leverage data from both tasks and do transfer learning between MT, NLG, and MT with source-side metadata (MT+NLG). First, we train document-based MT systems with large amounts of parallel data. Then, we adapt these models to pure NLG and MT+NLG tasks by fine-tuning with smaller amounts of domain-specific data. This end-to-end NLG approach, without data selection and planning, outperforms the previous state of the art on the Rotowire NLG task. We participated to the “Document Generation and Translation” task at WNGT 2019, and ranked first in all tracks.

Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation.

Abstract: Graph Neural Networks (GNNs) have been emerging as a promising method for relational representation including recommender systems. However, various challenging issues of social graphs hinder the practical usage of GNNs for social recommendation, such as their complex noisy connections and high heterogeneity. The oversmoothing of GNNs is an obstacle of GNN-based social recommendation as well. Here we propose a new graph embedding method Heterogeneous Graph Propagation (HGP) to tackle these issues. HGP uses a group-user-item tripartite graph as input to reduce the number of edges and the complexity of paths in a social graph. To solve the oversmoothing issue, HGP embeds nodes under a personalized PageRank based propagation scheme, separately for group-user graph and user-item graph. Node embeddings from each graph are integrated using an attention mechanism. We evaluate our HGP on a large-scale real-world dataset consisting of 1,645,279 nodes and 4,711,208 edges. The experimental results show that HGP outperforms several baselines in terms of AUC and F1-score metrics.

Microscopic modelling circadian and bursty pattern of human activities.

Abstract: Recent studies for a wide range of human activities such as email communication, Web browsing, and library visiting, have revealed the bursty nature of human activities. The distribution of inter-event times (IETs) between two consecutive human activities exhibits a heavy-tailed decay behavior and the oscillating pattern with a one-day period, reflective of the circadian pattern of human life. Even though a priority-based queueing model was successful as a basic model for understanding the heavy-tailed behavior, it ignored important ingredients, such as the diversity of individual activities and the circadian pattern of human life. Here, we collect a large scale of dataset which contains individuals’ time stamps when articles are posted on blog posts, and based on which we construct a theoretical model which can take into account of both ignored ingredients. Once we identify active and inactive time intervals of individuals and remove the inactive time interval, thereby constructing an ad hoc continuous time domain. Therein, the priority-based queueing model is applied by adjusting the arrival and the execution rates of tasks by comparing them with the activity data of individuals. Then, the obtained results are transferred back to the real-time domain, which produces the oscillating and heavy-tailed IET distribution. This microscopic model enables us to develop theoretical understanding towards more empirical results.

SORT: An Interactive Source-Rewriting Tool for Improved Translation

Abstract: The quality of automatic translation is affected by many factors. One is the divergence between the specific source and target languages. Another lies in the source text itself, as some texts are more complex than others. One way to handle such texts is to modify them prior to translation. Yet, an important factor that is often overlooked is the source translatability with respect to the specific translation system and the specific model that are being used. In this paper we present an interactive system where source modifications are induced by confidence estimates that are derived from the translation model in use. Modifications are automatically generated and proposed for the user’s approval. Such a system can reduce postediting effort, replacing it by cost-effective pre-editing that can be done by monolinguals.