Biblio

Automatic vulnerability detection is challenging. In this paper, we report our in-progress work of vulnerability prediction based on graph neural network (GNN). We propose a general GNN-based framework for predicting the vulnerabilities in program functions. We study the different instantiations of the framework in representative program graph representations, initial node encodings, and GNN learning methods. The preliminary experimental results on a representative benchmark indicate that the GNN-based method can improve the accuracy and recall rates of vulnerability prediction.

Use of internet increases day by day so securing network and data is a big issue. So, it is very important to maintain security to ensure safe and trusted communication of information between different organizations. Because of these IDS is a very useful component of computer and network security. IDS system is used by many organizations or industries to detect the weakness in their security, documenting previous attacks and threats and preventing all of this from violating security policies. Because of these advantages, this system is important in system security. In this paper, we find a multilevel solution for different approaches (attacks) based on intrusion detection system. In this paper, we identify different attacks and find the solutions for different type of attacks such as DDOS, SQL injection and Brute force attack. In this case, we use client-server architecture. To implement this we maintain profile of user and base on this we find normal user or attacker when system find that attack is present then it directly block the attack.