PEiD is a packer identification tool widely used for malware analysis but its accuracy is becoming lower and lower recently. There exist two major reasons for that. The first is that PEiD does not provide a way to create signatures, though it adopts a signature-based approach. We need to create signatures manually, and it is difficult to catch up with packers created or upgraded rapidly. The second is that PEiD utilizes exact matching. If a signature contains any error, PEiD cannot identify the packer that corresponds to the signature. In this paper, we propose a new automated packer identification method to overcome the limitations of PEiD and report the results of our numerical study. Our method applies string-kernel-based support vector machine (SVM): it can measure the similarity between packed programs without our operations such as manually creating signature and it provides some error tolerant mechanism that can significantly reduce detection failure caused by minor signature violations. In addition, we use the byte sequence starting from the entry point of a packed program as a packer's feature given to SVM. That is, our method combines the advantages from signature-based approach and machine learning (ML) based approach. The numerical results on 3902 samples with 26 packer classes and 3 unpacked (not-packed) classes shows that our method achieves a high accuracy of 99.46% outperforming PEiD and an existing ML-based method that Sun et al. have proposed.

Analyzing a malware sample requires much more time and cost than creating it. To understand the behavior of a given malware sample, security analysts often make use of API call logs collected by the dynamic malware analysis tools such as a sandbox. As the amount of the log generated for a malware sample could become tremendously large, inspecting the log requires a time-consuming effort. Meanwhile, antivirus vendors usually publish malware analysis reports (vendor reports) on their websites. These malware analysis reports are the results of careful analysis done by security experts. The problem is that even though there are such analyzed examples for malware samples, associating the vendor reports with the sandbox logs is difficult. This makes security analysts not able to retrieve useful information described in vendor reports. To address this issue, we developed a system called AMAR-Generator that aims to automate the generation of malware analysis reports based on sandbox logs by making use of existing vendor reports. Aiming at a convenient assistant tool for security analysts, our system employs techniques including template matching, API behavior mapping, and malicious behavior database to produce concise human-readable reports that describe the malicious behaviors of malware programs. Through the performance evaluation, we first demonstrate that AMAR-Generator can generate human-readable reports that can be used by a security analyst as the first step of the malware analysis. We also demonstrate that AMAR-Generator can identify the malicious behaviors that are conducted by malware from the sandbox logs; the detection rates are up to 96.74%, 100%, and 74.87% on the sandbox logs collected in 2013, 2014, and 2015, respectively. We also present that it can detect malicious behaviors from unknown types of sandbox logs.

Characterization of peer-to-peer (P2P) traffic is an essential step to develop workload models towards capacity planning and cyber-threat countermeasure over P2P networks. In this paper, we present a classification scheme for characterizing P2P file-sharing hosts based on transport layer statistical features. The proposed scheme is accessed on a virtualized environment that simulates a P2P-friendly cloud system. The system shows high accuracy in differentiating P2P file-sharing hosts from ordinary hosts. Its tunability regarding monitoring cost, system response time, and prediction accuracy is demonstrated by a series of experiments. Further study on feature selection is pursued to identify the most essential discriminators that contribute most to the classification. Experimental results show that an equally accurate system could be obtained using only 3 out of the 18 defined discriminators, which further reduces the monitoring cost and enhances the adaptability of the system.

Along with the proliferation of IoT (Internet of Things) devices, cyberattacks towards them are on the rise. In this paper, aiming at efficient precaution and mitigation of emerging IoT cyberthreats, we present a multimodal study on applying machine learning methods to characterize malicious programs which target multiple IoT platforms. Experiments show that opcode sequences obtained from static analysis and API sequences obtained by dynamic analysis provide sufficient discriminant information such that IoT malware can be classified with near optimal accuracy. Automated and accelerated identification and mitigation of new IoT cyberthreats can be enabled based on the findings reported in this study.