Recent adversaries targeting the Industrial Control Systems (ICSs) have started exploiting their sophisticated inherent contextual semantics such as the data associativity among heterogeneous field devices. In light of the subtlety rendered in these semantics, anomalies triggered by such interactions tend to be extremely covert, hence giving rise to extensive challenges in their detection. Driven by the critical demands of securing ICS processes, a Graph-Neural-Network (GNN) based method is presented to tackle these subtle hostilities by leveraging an ICS’s advanced contextual features refined from a universal perspective, rather than exclusively following GNN’s conventional local aggregation paradigm. Specifically, we design and implement the Graph Sample-and-Integrate Network (GSIN), a general chained framework performing node-level anomaly detection via advanced feature integration, which combines a node’s local awareness with the graph’s prominent global properties extracted via process-oriented pooling. The proposed GSIN is evaluated on multiple well-known datasets with different kinds of integration configurations, and results demonstrate its superiority consistently on not only anomaly detection performance (e.g., F1 score and AUPRC) but also runtime efficiency over recent representative baselines.

Phishing website is becoming a major threat to the information security in Social Network. The attacks not only lessen the users' trust but also influence the benefit of the third party who develops the platform. In order to solve the time lag in phishing website passive detection, this paper proposes a solution to discover phishing website initiatively based on blacklist, in which the anomalies of its URL and WHOIS information are analyzed, and based on this, the heuristic rules that aim to generate suspicious URLs are made. In order to filter out noise sites in the suspicious set, a website filtering solution based on webpages image-layout is presented. We firstly propose a Ray Scan Method to generate the location feature of webpage images quickly, and then, we proposed a method of calculating the webpage layout similarity, which will be compared against the preset threshold to decide whether it will be filtered. The experimental results show that the solution successfully detects some phishing websites out before they are widely spread, and further, the webpage filtering method guarantees both high filtration ratio and high phishing website retention ratio.

The traditional collaborative filtering recommendation algorithm can be divided into the user-based and the item-based two methods, which only uses the information in the rating matrix. Because of the limitation of the information capacity they used, it is difficult to further improve the accuracy of the recommendation, and cold start problem also affects the normal operation of the recommendation system. This paper presented a collaborative filtering recommendation algorithm (UISA) fusing user-based, item-based and social networks data. The algorithm uses the data of the neighbor relations in social networks, calculating the users' friends not reflected in the rating matrix. At the same time, we can calculate the similarity between items by using the data of item text in social networks, mining similar items not reflected in the rating matrix. In this way, it can fundamentally expand available information capacity of the traditional filtering collaboration recommendation algorithms, improve the recommendation accuracy, alleviate cold start problem. Experimental results based on KDD CUP 2012 real data show that compared with the traditional collaborative filtering system, this system has obvious advantages in the recommendation accuracy and ease of cold start.

In traditional collaborative filtering recommendation, the matrix sparsity and cold start restricted the accuracy of system. In this paper, we develop a way to enhance the recommendation effectiveness by merging neighborhood relationship and users keyword of social network information into collaborative filtering. We extend the calculation method of the TOP N neighbors which is the most important from two aspects. Our method expands the information capacity which can be used by collaborative filtering, improves the accuracy of recommendation and eases the cold start problem in recommendation system. We conducts experiment based on KDD 2012 real data set. The result indicates that our algorithm performs more superior than traditional collaborative filtering algorithm.