Two components were identified from aeration extracts of the virgin female Madeira mealybug, Phenacoccus madeirensis as trans-(1R,3R)-chrysanthemyl (R)-2-methylbutanoate and (R)-lavandulyl (R)-2-methylbutanoate (with a ratio of 3:1) by a combination of gas chromatography retention time matches, mass spectrometry, and microchemical tests. The structures and chirality of the compounds were confirmed by comparing with synthetic compounds. The synthetic trans-(1R,3R)-chrysanthemyl (R)-2-methylbutanoate was highly attractive to males in laboratory bioassays; the synthetic (R)-lavandulyl (R)-2-methylbutanoate was weakly attractive. No synergistic effect was observed when the mixture of the two compounds was tested.

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This research aims to build a Mandarin named entity recognition (NER) module using transfer learning to facilitate damage information gathering and analysis in disaster management. The hybrid NER approach proposed in this research includes three modules: (1) data augmentation, which constructs a concise data set for disaster management; (2) reference model, which utilizes the bidirectional long short-term memory–conditional random field framework to implement NER; and (3) the augmented model built by integrating the first two modules via cross-domain transfer with disparate label sets. Through the combination of established rules and learned sentence patterns, the hybrid approach performs well in NER tasks for disaster management and recognizes unfamiliar words successfully. This research applied the proposed NER module to disaster management. In the application, we favorably handled the NER tasks of our related work and achieved our desired outcomes. Through proper transfer, the results of this work can be extended to other fields and consequently bring valuable advantages in diverse applications.

In recent years, PowerShell has become the common tool that helps attackers launch targeted attacks using living-off-the-land tactics and fileless attack techniques. Unfortunately, malwarederived PowerShell Commands (PSCmds) have typically been obfuscated to hide the malicious intent from detection and analysis. Also, malicious PSCmds' expansive use of multiple obfuscation strategies and encryption methods makes them difficult to be revealed. Despite the advances in malicious PSCmds detection incorporating new approaches such as machine learning and deep learning, there is still no consensus on the solution to de-obfuscating malicious PSCmds and profiling their behavior. To address this challenge, we propose a hybrid framework that combines deep learning and program analysis for automatic PowerShell De-obfuscation and behavioral Profiling (PowerDP) through multi-label classification in a static manner. First, we use character distribution features to forecast obfuscation types of malicious PSCmds. Second, we developed an extensive de-obfuscator utilizing static regular expression replacement to recover the original content of obfuscated PSCmds based on the predicted obfuscation types. Finally, we profile the behavior of PSCmds by features extracted from the abstract syntax tree of PSCmds after deobfuscation. Our results show that PowerDP achieves a promising 99.82% accuracy and 0.18% hamming loss in obfuscation multi-label classification using deep learning. Furthermore, the successful recovery rate of the de-obfuscator against 15 obfuscation types is 98.11% on average with semantic similarity comparison, and the accuracy of the behavior multi-label classification for identifying 5 behaviors in malicious PSCmds averages 98.53%. The evaluation indicates that PowerDP is able to classify and profile complicated PSCmds.INDEX TERMS PowerShell, de-obfuscation, machine learning, deep learning, abstract syntax trees, multi-label classification, behavioral profiling 1 https://attack.mitre.org/matrices/enterprise/ documents are still the best combinations for malware delivery media. Because people remain susceptible to manipulation, human psychological weaknesses result in the main vulnerabilities that can be exploited through social engineering, e.g., spear-phishing attacks. In this scenario, attackers typically attached a well-customized malicious document containing PowerShell Commands (PSCmds) to a forged email. Additionally, impersonation is often used in sender names or contact information to lure targets into opening malicious files.Living-off-the-Land (LotL) tactics and fileless attack VOLUME 0, 2022