On Embedding Backdoor in Malware Detectors Using Machine Learning Researching for malware detection using machine learning is becoming active. However, conventional detection techniques do not consider the impact of attacks on machine learning, which has become complicated in recent years. In this research, we focus on data poisoning attack, which is one of the typical attacks on machine learning, and aim to clarify the influence of attacks on malware detection technology. Data poisoning attack is an attack method that intentionally manipulates the predicted result of a learned model by injecting poisoning data into training data, and by applying this, it is possible to embed a backdoor that induces misprediction of only specific input data. In this paper, we first propose an attack framework for backdoor embedding that prevents detection of only specific types of malware by data poisoning attack. Next, we will describe a method to generate poisoning data efficiently while avoiding attack detection by solving the optimization problem. Furthermore, we take malware detection technology using logistic regression and show the effectiveness of the our method through evaluation experiments using two datasets.

Identifying Useful Features for Malware Detection in the Ember Dataset Many studies have been conducted to detect malware based on machine learning of program features extracted using static analysis. In this study, we consider the task of distinguishing between malware and benign programs by learning their surface features, such as general file information and imported functions. To make such attempts practical, a good balance among accuracy, learning time, and featuredata sizes is required. Although using only a subset of features can reduce the required time and data sizes, it is not trivial to select an appropriate subset of features. In this paper, we present a case study of feature selection in malware detection based on supervised machine learning. We used the Ember dataset as the target data and identified useful combinations of features in terms of accuracy, learning time, and data size.