Biblio

In order to solve the problem that collaborative filtering recommendation algorithm completely depends on the interactive behavior information of users while ignoring the correlation information between items, this paper introduces a link prediction algorithm based on knowledge graph to integrate ItemCF algorithm. Through the linear weighted fusion of the item similarity matrix obtained by the ItemCF algorithm and the item similarity matrix obtained by the link prediction algorithm, the new fusion matrix is then introduced into ItemCF algorithm. The MovieLens-1M data set is used to verify the KGLP-ItemCF model proposed in this paper, and the experimental results show that the KGLP-ItemCF model effectively improves the precision, recall rate and F1 value. KGLP-ItemCF model effectively solves the problems of sparse data and over-reliance on user interaction information by introducing knowledge graph into ItemCF algorithm.

SQL injection has always been a major threat in the field of web application security. Traditional methods such as the rule-matching-based SQL injection detection solutions, which are inefficient to cope with the ever-changing SQL injection techniques and there is always a risk of bypassing variants. In this paper, we extract SQL injection attack related payloads from network flow and propose a SQL injection detection model based on Convolutional Neural Network (CNN), which can take the advantages of high-dimensional features of SQL injection behavior to deal with this issue. The proposed approach was tested in a real-traffic case study along with ModSecurity, which is the representative rule-matching-based method. The experimental results show that the CNN based model has higher accuracy, precision and recall rate, which validate its detection effectiveness and robustness against obfuscation of attacks.

Despite a long history and numerous proposed defenses, memory corruption attacks are still viable. A secure and low-overhead defense against return-oriented programming (ROP) continues to elude the security community. Currently proposed solutions still must choose between either not fully protecting critical data and relying instead on information hiding, or using incomplete, coarse-grain checking that can be circumvented by a suitably skilled attacker. In this paper, we present a light-weighted memory protection approach (LMP) that uses Intel's MPX hardware extensions to provide complete, fast ROP protection without having to rely in information hiding. We demonstrate a prototype that defeats ROP attacks while incurring an average runtime overhead of 3.9%.