Malware classification algorithm using advanced Word2vecbased BiLSTM for ground control stations Abstract   Recently, Internet of Drones (IoD) are issued to utilize the diverse kinds of drones for leisure, education and so on. Researchers study to prevent the situations that drones are disabled by cyberattackers by embedding malwares into the drones and Ground Control Stations (GCS). Therefore, it is required to protect the malwares considering the diverse kinds of features of the drones and GCSs. Signaturebased detection approaches are traditionally utilized. However, given that those approaches only scan files partially, some of malwares are not detected. This paper proposes a novel method for finding the malwares in GCSs that utilizes a fastText model to create lowerdimension vectors than those the vectors by onehot encoding and a bidirectional LSTM model to analyze the correlation with sequential opcodes. In addition, API function names are utilized to increase the classification accuracy of the sequential opcodes. In the experiments, the Microsoft malware classification challenge dataset was utilized and the malwares in the dataset were classified by family types. The proposed method showed the performance improvement of 1.87% comparing with the performance by a onehot encodingbased approach. When the proposed method was compared with a similar decision treebased malware detection approach, the performance of the proposed method was improved by 0.76%.

ImageBased Unknown Malware Classification with FewShot Learning Models Knowing malware types in every malware attacks is very helpful to the administrators to have proper defense policies for their system. It must be a massive benefit for the organization as well as the social if the automatic protection systems could themselves detect, classify an existence of new malware types in the whole network system with a few malware samples. This feature helps to prevent the spreading of malware as soon as any damage is caused to the networks. An approach introduced in this paper takes advantage of Oneshotfewshot learning algorithms in solving the malware classification problems by using some wellknown models such as Matching Networks, Prototypical Networks. To demonstrate an efficiency of the approach, we run the experiments on the two malware datasets (namely, MalImg and Microsoft Malware Classification Challenge), and both experiments all give us very high accuracies. We confirm that if applying models correctly from the machine learning area could bring excellent performance compared to the other traditional methods, open a new area of malware research.