With the flourishing of the open-source software community, the problem of software vulnerabilities is becoming more and more serious. Hence, it is urgent to come up with an effective and efficient code vulnerability detection method. Source code vulnerability detection techniques used in practice today like symbolic execution and fuzz testing suffer from high false positives and low code coverage, respectively. Traditional machine-learning-based solutions fail to cope with the diversity of vulnerabilities. To overcome these drawbacks, a large number of deep-learning-based code vulnerability detection works have emerged, aiming at building powerful neural network models to fully learn code semantics and vulnerability patterns. In this survey, we mainly focus on code vulnerability detection approaches based on deep sequence modeling and graph modeling technologies. Our goal is to investigate how these two methods are applied to facilitate code vulnerability detection. We also go over current prevailing datasets that are used to evaluate detection models. At last, we identify the current challenges in this field and share our views on future work.

Malware remains a major threat to nowadays Internet. In this paper, we propose a DNS graph mining-based malware detection approach. A DNS graph is composed of DNS nodes, which represent server IPs, client IPs, and queried domain names in the process of DNS resolution. After the graph construction, we next transform the problem of malware detection to the graph mining task of inferring graph nodes’ reputation scores using the belief propagation algorithm. The nodes with lower reputation scores are inferred as those infected by malwares with higher probability. For demonstration, we evaluate the proposed malware detection approach with real-world dataset. Our real-world dataset is collected from campus DNS servers for three months and we built a DNS graph consisting of 19,340,820 vertices and 24,277,564 edges. On the graph, we achieve a true positive rate 80.63% with a false positive rate 0.023%. With a false positive of 1.20%, the true positive rate was improved to 95.66%. We detected 88,592 hosts infected by malware or C&C servers, accounting for the percentage of 5.47% among all hosts. Meanwhile, 117,971 domains are considered to be related to malicious activities, accounting for 1.5% among all domains. The results indicate that our method is efficient and effective in detecting malwares.

We propose a new type of authentication system based on behavioral characteristics for smartphone users. With the sensor and touch screen data in the smartphone, the combination of the motion state detection mode and the authentication mode can effectively distinguish between legitimate smartphone users and other users. The system deploys software on the smartphone to collect data from sensors and touch screens, and upload the data to the cloud. We apply random forest algorithm on the data to extract features and achieve motion state detection. Multilayer perceptron algorithm is used for user authentication in corresponding motion state. The system effectively implements an implicit and continuous authentication mode, which can achieve user identity authentication without users’ being aware of it. The system proposed in this article can achieve 95.96% accuracy with false rejection rate of 2.55% and false acceptance rate of 6.94%.