Large-scale dynamic intranet environments are characterized by constantly changing configurations, evolving user behaviors, and diverse assets that increase vulnerability pathways. These factors undermine the effectiveness of Bayesian attack graphs and reveal the limitations of traditional security methods that rely on static assumptions. To address these challenges, this paper proposes a novel Bayesian attack graph method designed for large-scale, active intranet security assessments. It captures real-time intranet changes by extracting system audit logs and generates attack graphs with MulVAL, ultimately resulting in a time-spanning understanding of potential security risks. Furthermore, it identifies direct-risk paths by eliminating weak dependencies between actions and estimates the likelihood of action execution based on expectations, thereby substantially reducing the computational complexity of Bayesian security analysis. To validate the proposed method, this paper conducts dynamic threat modeling and quantitative security analysis on an enterprise intranet using logs from over 1,000 hosts. The results demonstrate that the proposed method not only provides internal network security risk values at any given time but also identifies specific and observable potential attack paths. Furthermore, this study provides a reference framework for prioritizing vulnerability remediation based on changes in internal network security conditions

On-demand food delivery service has widely served people's daily demands worldwide, e.g., customers place over 40 million online orders in Meituan food delivery platform per day in Q3 of 2021. Predicting the food preparation time (FPT) of each order accurately is very significant for the courier and customer experience over the platform. However, there are two challenges, namely incomplete label and huge uncertainty in FPT data, to make the prediction of FPT in practice. In this paper, we apply probabilistic forecasting to FPT for the first time and propose a non-parametric method based on deep learning. Apart from the data with precise label of FPT, we make full use of the lower/upper bound of orders without precise label, during feature extraction and model construction. A number of categories of meaningful features are extracted based on the detailed data analysis to produce sharp probability distribution. For probabilistic forecasting, we propose S-QL and prove its relationship with S-CRPS for interval-censored data for the first time, which serves the quantile discretization of S-CRPS and optimization for the constructed neural network model. Extensive offline experiments over the large-scale real-world dataset, and online A/B test both demonstrate the effectiveness of our proposed method.

Online food ordering and delivery service has widely served people's daily demands worldwide, e.g., it has reached a number of 34.9 million online orders per day in Q3 of 2020 in Meituan food delivery platform. For the food delivery service, accurate estimation of the driver's delivery route and time, defined as the FD-RTP task, is very significant to customer satisfaction and driver experience. In the paper, we apply deep learning to the FD-RTP task for the first time, and propose a deep network named FDNET. Different from traditional heuristic search algorithms, we predict the probability of each feasible location the driver will visit next, through mining a large amount of food delivery data. Guided by the probabilities, FDNET greatly reduces the search space in delivery route generation, and the calculation times of time prediction. As a result, various kinds of information can be fully utilized in FDNET within the limited computation time. Careful consideration of the factors having effect on the driver's behaviors and introduction of more abundant spatiotemporal information both contribute to the improvements. Offline experiments over the large-scale real-world dataset, and online A/B test demonstrate the effectiveness of our proposed FDNET.

Abstract The advances of mobile equipment and localization techniques put forward the accuracy of the location-based service (LBS) in mobile networks. One core issue for the industry to exploit the economic interest of the LBSs is to make appropriate point-of-interest (POI) recommendation based on users’ interests. Today, the LBS applications expect the recommender systems to recommend the accurate next POI in an anonymous manner, without inquiring users’ attributes or knowing the detailed features of the vast number of POIs. To cope with the challenge, we propose a novel attentive model to recommend appropriate new POIs for users, namely Geographical Attentive Recommendation via Graph (GARG), which takes full advantage of the collaborative, sequential and content-aware information. Unlike previous strategies that equally treat POIs in the sequence or manually define the relationships between POIs, GARG adaptively differentiates the relevance of POIs in the sequence to the prediction, and automatically identifies the POI-wise correlation. Extensive experiments on three real-world datasets demonstrate the effectiveness of GARG and reveal a significant improvement by GARG on the precision, recall and mAP metrics, compared to several state-of-the-art baseline methods.

WeChat is the largest mobile instant messaging service in China, where users can send messages to friends or post them over their walls (a.k.a. friend circle, or WeChat Moments). Interactive web apps are quite attractive for businesses, institutes, or individuals to promote products or events. In this paper, we analyze the diffusion statistics of interactive web apps in WeChat and conduct an empirical measurement study over a dataset with 54 million users and 20 thousand web apps crawled. We discover the lifecycle of interactive web apps varies drastically, which is largely dependent on the content, date, time upon the first release, and the social influence of viewers and senders. Meanwhile, we develop a model based on the matrix factorization method to extract latent features of interactive web apps and an app lifecycle model that characterizes how the features affect apps' lifecycle, achieving the mean absolute error (MAE) of 2.32 days in predicting app's lifecycle. Our results hold the promise of helping businesses to promote their marketing information dissemination through long-lived interactive web apps at the right timing and with the appropriate content.

Emotion classification is an important task for identifying users' emotional expressions in text. Though a variety of neural models have been proposed nowadays, these models mainly focus on modeling the content of words or characters without fully employing the emotional features in lexical features, especially the features of part-of -speech (POS). In this paper, we reveal that the information of POS as well as that of words is important for identifying the type of emotion in a given text. We propose two simple models to fully learn the emotional features of the POS of words. Every model consists of the long short-term memory (LSTM) network as input encoders and the component of attention mechanism. One model is to concatenate the POS tags of vectors into the hidden states of representations generated by LSTM as raw feature representations and put them into the component of attention mechanism to generate the text representation toward a special emotion. The other is to use both LSTM and attention mechanism to model the context representation of words and those of POS tags respectively and concatenate these context representations as the text representation toward a special emotion. We conduct some experiments on datasets for evaluation and demonstrate the effectiveness of our model, where the datasets consist of the open-source dataset from NLPCC& 2014 and the dataset of manual annotation. Experimental results show that our models can achieve outstanding performance for emotion classification in Chinese Weibo texts and outperform classical baselines.

Decentralizing content delivery to edge devices has become a popular solution for saving the bandwidth consumption of CDN when the CDN bandwidth is expensive. One successful realization is the hybrid CDN-P2P VoD system, where a client is allowed to request video content from a number of seeds (seed clients) in the P2P network. However, the seed scarcity problem may arise for a video resource when there are an insufficient number of seeds to satisfy requests to the video. To alleviate this problem, many commercial VoD systems have employed a video push mechanism that directly sends the recent scarce video resources to randomly-chosen seeds to serve more requests. However, the current video push mechanism fails to consider which videos will become scarce in the future, or differentiate the uploading capability of different seeds. In this paper, we propose Proactive-Push, a video push mechanism that lowers the bandwidth consumption of CDN by predicting future scarce videos and proactively sending them to competent seeds with strong uploading capabilities. Proactive-Push trains neural network models to correctly predict 80% of future scarce video resources, and identify over 90% of competent seeds. We evaluate Proactive-Push using a trace-driven emulation and a real-world pilot deployment over a commercial VoD system. Results show that Proactive-Push can further reduce the proportion of direct download from CDN by 21%, and save the CDN bandwidth cost at peak time by 18%.

People connect to each other over conventional online social networks (OSNs) based on many parameters (common interests, experiences, locations), while the anonymous social networks (ASNs) recommend candidate friends to a user mainly by the location proximity at a coarse-granularity. In this paper, we formulate a fine-grained trajectory-matching prediction problem for friend recommendation in ASNs-what is the likelihood for two users to encounter with each other in the future based on their historical trajectory data? We define the serendipity of two trajectories in both spatial and temporal domains to quantify the similarity between two users' trajectories, and propose an algorithm that recommends candidate friends to a user by determining the similarity of their trajectories. Experiments show that our proposed algorithm can predict the encounter of users quite accurately and it outperforms the conventional algorithms in terms of the precision and consumed time.

Businesses are interested in marketing over the mobile social network (MSN) during holiday seasons to expand their holiday sales. Understanding the “holiday syndrome” - how people behave during holidays - over the MSN is important to create a successful marketing campaign that delights customers. In this paper, we conduct an empirical measurement study of WeChat Moments (the most popular social network of the mobile messaging app WeChat in China) during the holiday season of Chinese Spring Festival (with 137 million users involved and 329 thousand applications crawled), and present a comprehensive view of the MSN's impact on the social ties among users, user interests, as well as users' migration patterns before, during, and after the holiday. Our research findings suggest that the MSN is predominantly used during holiday seasons for holiday-atmosphere building and experience sharing. It is revealed that there exist strong correlations between the timing and popular topics, and between holiday migration and regional distribution. Our results hold the promise of helping businesses to promote their marketing information dissemination to targeted groups of customers, at the right region, with appealing words, during the holiday season.