No abstract

With the rapid exhaustion of fossil resources, and environmental pollution relative to the use of fossil-based products, developing eco-friendly products using biomass and/or biodegradable resources is becoming increasingly conspicuous. In this study, ecofriendly and biodegradable composite membranes containing varying MC/PLA (methylcellulose/polylactic acid) mass ratios were prepared. The properties and structures of the MC/PLA membranes were studied by mechanical testing, 13C NMR techniques, X-ray diffraction (XRD), thermogravimetric analysis (TGA), and hot compression. The MC/PLA membranes displayed markedly improved tensile strength and elongation at the MC/PLA mass ratio range of 99:1 to 9:1. The tensile strength and elongation of the MC/PLA (97:3) membrane was found to be the optimum, at 30% and 35% higher than the neat MC, respectively. It was also found that hot compression could improve the tensile strength and elongation of the membranes. At the same time, the membranes showed enough good thermal stability. In addition, the effect of MC/PLA mass ratio on morphologies of the membranes were studied by microscopy technique.

An SQL Injection Attack (SQLIA) is a major cyber security threat to Web services, and its different stages can cause different levels of damage to an information system. Attackers can construct complex and diverse SQLIA statements, which often cause most existing inbound-based detection methods to have a high false-negative rate when facing deformed or unknown SQLIA statements. Although some existing works have analyzed different features for the stages of SQLIA from the perspectives of attackers, they primarily focus on stage analysis rather than different stages’ identification. To detect SQLIA and identify its stages, we analyze the outbound traffic from the Web server and find that it can differentiate between SQLIA traffic and normal traffic, and the outbound traffic generated during the two stages of SQLIA exhibits distinct characteristics. By employing 13 features extracted from outbound traffic, we propose an SQLIA detection and stage identification method based on outbound traffic (SDSIOT), which is a two-phase method that detects SQLIAs in Phase I and identifies their stages in Phase II. Importantly, it does not need to analyze the complex and diverse malicious statements made by attackers. The experimental results show that SDSIOT achieves an accuracy of 98.57% for SQLIA detection and 94.01% for SQLIA stage identification. Notably, the accuracy of SDSIOT’s SQLIA detection is 8.22 percentage points higher than that of ModSecurity.

No abstract

With the increasing complexity of network attacks, an active defense based on intelligence sharing becomes crucial. There is an important issue in intelligence analysis that automatically extracts threat actions from cyber threat intelligence (CTI) reports. To address this problem, we propose EX-Action, a framework for extracting threat actions from CTI reports. EX-Action finds threat actions by employing the natural language processing (NLP) technology and identifies actions by a multimodal learning algorithm. At the same time, a metric is used to evaluate the information completeness of the extracted action obtained by EX-Action. By the experiment on the CTI reports that consisted of sentences with complex structure, the experimental result indicates that EX-Action can achieve better performance than two state-of-the-art action extraction methods in terms of accuracy, recall, precision, and F1-score.