Deep learning-based vulnerability detection frees human experts from the tedious task of defining features and allows for better detection capabilities. The common practice is to convert program code into vector representation for neural network model training. Since the length of the vector representation varies across program code, finding the optimal vector length is critical to ensuring detection accuracy. This paper proposes an adaptive search optimization algorithm for finding the optimal vector length. It sorts all the vector lengths obtained by word2vec and takes the vector length corresponding to the point where the trend changes from slow to fast as the output. We evaluate our algorithm on three publicly available datasets against state-of-the-art algorithms. The results show that, without significantly increasing the time overhead, our algorithm can more accurately choose an appropriate vector length instead of setting a value empirically or arbitrarily. Furthermore, it shows that while a larger vector length can usually produces a higher detection accuracy, the extra time overhead incurred often does not suffice to compensate for the corresponding accuracy improvement.

With the wide application of software system, software vulnerability has become a major risk in computer security. The on-time detection and proper repair for possible software vulnerabilities are of great importance in maintaining system security and decreasing system crashes. The Control Flow Integrity (CFI) can be used to detect the exploit by some researchers. In this paper, we propose an improved Control Flow Graph with Jump (JCFG) based on CFI and develop a novel Vulnerability Exploit Detection Method based on JCFG (JCFG-VEDM). The detection method of the exploit program is realized based on the analysis results of the exploit program. Then the JCFG is addressed through combining the features of the exploit program and the jump instruction. Finally, we implement JCFG-VEDM and conduct the experiments to verify the effectiveness of the proposed method. The experimental results show that the proposed detection method (JCFG-VEDM) is feasible and effective.

Due to the growth and popularity of the internet, cyber security remains, and will continue, to be an important issue. There are many network traffic classification methods or malware identification approaches that have been proposed to solve this problem. However, the existing methods are not well suited to help security experts effectively solve this challenge due to their low accuracy and high false positive rate. To this end, we employ a machine learning-based classification approach to identify malware. The approach extracts features from network traffic and reduces the dimensionality of the features, which can effectively improve the accuracy of identification. Furthermore, we propose an improved SVM algorithm for classifying the network traffic dubbed Optimized Facile Support Vector Machine (OFSVM). The OFSVM algorithm solves the problem that the original SVM algorithm is not satisfactory for classification from two aspects, i.e., parameter optimization and kernel function selection. Therefore, in this paper, we present an approach for identifying malware in network traffic, called Network Traffic Malware Identification (NTMI). To evaluate the effectiveness of the NTMI approach proposed in this paper, we collect four real network traffic datasets and use a publicly available dataset CAIDA for our experiments. Evaluation results suggest that the NTMI approach can lead to higher accuracy while achieving a lower false positive rate compared with other identification methods. On average, the NTMI approach achieves an accuracy of 92.5% and a false positive rate of 5.527%.

To find software vulnerabilities using software vulnerability detection technology is an important way to ensure the system security. Existing software vulnerability detection methods have some limitations as they can only play a certain role in some specific situations. To accurately analyze and evaluate the existing vulnerability detection methods, an integrated testing and evaluation system (iTES) is designed and implemented in this paper. The main functions of the iTES are:(1) Vulnerability cases with source codes covering common vulnerability types are collected automatically to form a vulnerability cases library; (2) Fourteen methods including static and dynamic vulnerability detection are evaluated in iTES, involving the Windows and Linux platforms; (3) Furthermore, a set of evaluation metrics is designed, including accuracy, false positive rate, utilization efficiency, time cost and resource cost. The final evaluation and test results of iTES have a good guiding significance for the selection of appropriate software vulnerability detection methods or tools according to the actual situation in practice.