In search of blood--detection of minute particles using spectroscopic methods

An examination protocol for rapid detection of remnants of blood particles on garments of suspects in bloody murder cases is described. Microparticles of blood are sampled along with fibres and hairs using the tape lifting method. The tapings are searched with a low power microscope for red particles with morphology similar to blood. Presumed blood traces are further examined using microspectrophotometry on the cut out piece of taping. The typical visible spectrum of haemoglobin is characteristic for blood. Alternatively Raman spectroscopy can be used to measure the characteristic vibrational spectrum of haemoglobin. At a later stage, these particles may be removed from the piece of taping in order to extract the blood and attempt to obtain a genetic profile.     

A LDR‐PCR Approach for Multiplex Polymorphisms Genotyping of Severely Degraded DNA with Fragment Sizes <100 bp*

Abstract: Reducing amplicon sizes has become a major strategy for analyzing degraded DNA typical of forensic samples. However, amplicon sizes in current mini‐short tandem repeat‐polymerase chain reaction (PCR) and mini‐sequencing assays are still not suitable for analysis of severely degraded DNA. In this study, we present a multiplex typing method that couples ligase detection reaction with PCR that can be used to identify single nucleotide polymorphisms and small‐scale insertion/deletions in a sample of severely fragmented DNA. This method adopts thermostable ligation for allele discrimination and subsequent PCR for signal enhancement. In this study, four polymorphic loci were used to assess the ability of this technique to discriminate alleles in an artificially degraded sample of DNA with fragment sizes <100 bp. Our results showed clear allelic discrimination of single or multiple loci, suggesting that this method might aid in the analysis of extremely degraded samples in which allelic drop out of larger fragments is observed.     

An Amino Acid Model for Latent Fingerprints on Porous Surfaces*

Abstract: Analytical standards are needed in latent fingerprint detection for research and development as well as for quality control in routine work because normal fingerprints are too varied for comparison studies and tests. One way is to create latent fingerprints. For the amino acid sensitive detection method this can be achieved by coating test items with an amino acid solution using a modified commercial office bubble jet printer. Besides low costs, fast and easy preparation, the main advantage of a bubble jet printer is that the amino acid loading per area on the test item can be calculated by weighing the cartridge on a balance. This opens the possibility to determine the deviation for every printing series. The reproducibility of prints in a printing series made by one cartridge has a deviation of 2–16% and of prints made by different cartridges 20–25%.     

Leveraging virtual machine introspection with memory forensics to detect and characterize unknown malware using machine learning techniques at hypervisor

The Virtual Machine Introspection (VMI) has emerged as a fine-grained, out-of-VM security solution that detects malware by introspecting and reconstructing the volatile memory state of the live guest Operating System (OS). Specifically, it functions by the Virtual Machine Monitor (VMM), or hypervisor. The reconstructed semantic details obtained by the VMI are available in a combination of benign and malicious states at the hypervisor. In order to distinguish between these two states, the existing out-of-VM security solutions require extensive manual analysis. In this paper, we propose an advanced VMM-based, guest-assisted Automated Internal-and-External (A-IntExt) introspection system by leveraging VMI, Memory Forensics Analysis (MFA), and machine learning techniques at the hypervisor. Further, we use the VMI-based technique to introspect digital artifacts of the live guest OS to obtain a semantic view of the processes details. We implemented an Intelligent Cross View Analyzer (ICVA) and implanted it into our proposed A-IntExt system, which examines the data supplied by the VMI to detect hidden, dead, and dubious processes, while also predicting early symptoms of malware execution on the introspected guest OS in a timely manner. Machine learning techniques are used to analyze the executables that are mined and extracted using MFA-based techniques and ascertain the malicious executables. The practicality of the A-IntExt system is evaluated by executing large real-world malware and benign executables onto the live guest OSs. The evaluation results achieved 99.55% accuracy and 0.004 False Positive Rate (FPR) on the 10-fold cross-validation to detect unknown malware on the generated dataset. Additionally, the proposed system was validated against other benchmarked malware datasets and the A-IntExt system outperforms the detection of real-world malware at the VMM with performance exceeding 6.3%.     

Detection and Classification of Ignitable Liquid Residues Using a Fluorescence‐Based Vapor‐Sensitive Microsphere Array*

Abstract: This paper describes the application of microsphere vapor sensing arrays to the detection of ignitable liquid (IL) vapors as both pure vapors and as residues (ILRs) on simulated fire debris samples. The temporal fluorescence response profile of the microsphere array generated a reproducible pattern unique to each analyte that could be used to classify subsequent sensor responses. This system, together with a support vector machine pattern recognition algorithm, was used to address several different IL and ILR classification scenarios. High classification accuracy (98%) was maintained over more than 200 vapor responses and the array was able to identify ILs when presented to the pattern classification algorithm within a dataset containing 11 other volatile compounds. Both burned and unburned IL treated samples were classified correctly greater than 97% of the time. These results indicate that microsphere vapor sensing arrays may be useful for the rapid identification of ILs and ILRs.     

犯罪现场生物检材的发现、提取策略

在改进现场生物检材的发现和提取技术的同时,也要进行理论创新和体系构建,才能够更好地指导实践。全局的观点、具体案件具体分析和犯罪现场重建对于现场勘查中有效地发现、提取和利用生物检材是十分重要的。     

Investigations into Enhancing Yersinia pestis Cells Viability following Environmental Sampling for Forensic Analysis

Following an intentional or accidental bio-warfare agent (BWA) release, environmental sample analysis is absolutely critical to determine the extent of contamination. When dealing with nonspore forming BWA (e.g., Yersinia pestis), retention of cell viability is central to such analyses. Even though significant advances have been achieved in DNA sequencing technologies, a positive identification of BWAs in environmental samples must be made through the ability of cells to form colony-forming units upon culturing. Inability to revive the cells between collection and analysis renders such studies inconclusive. Commercial kits designed to preserve the viability of pathogens contained within clinical samples are available, but many of them have not been examined for their ability to preserve samples containing suspected BWAs. The study was initiated to examine the applicability of commercial solutions aiding in retention of Y. pestis viability in samples stored under nonpermissive temperatures, that is, 40 and 37°C. While none of the tested solutions sustained cell viability at 40°C, the results show five out of 17 tested preservatives were capable of supporting viability of Y. pestis at 37°C.     

Model verification for population detection of counterfeits

The quality of counterfeit items has increased dramatically, with modern global manufacturing being able to duplicate the materials, construction, and visual features of items. Detection of fraudulent coinage can parallel authentication of food, beverages, and manufactured goods by studying product-inherent features. Counterfeit detection is performed by comparing an Example group with a Questioned group. A model is developed for both groups using standard tests on individual pieces. Coin weight is used here as an illustration. The model should also follow the natural science of the system. In this case, the manufacturing process variation is known and steady, and the underlying distribution is known or can be determined from authentic pieces. The proposed detection method uses testing of many individual pieces, then using reverse-quality-engineering methods to identify possible sources. This strategy looks at the variation between individual pieces to determine the process capability of a machine, assembly line, or plant to create product consistency for a manufacturer. Fraudulent items may be manufactured within specification, but demonstrate a manufacturing process capability different than that of the authentic manufacturer. In this report, we examine the model previously reported and use reconstruction techniques to re-create the evidence set to validate the model, increase model accuracy, and confirm the conclusion previously reached, showing that the Questioned set is likely over 37% non-conforming by weight. In this case, the decision outcome of the analysis was improved by using additional methods not included in the modeling software package originally used.     

表面增强拉曼光谱快速检测食品基质中毒物

目的建立常见食品基质中氰化物等五种毒物的拉曼光谱快速分析方法。方法采用水、乙腈及乙酸乙酯作为提取剂对饮用水、苹果以及烤鸭三种常见食品基质中氰化物、百草枯、毒死蜱、甲拌磷以及敌鼠钠五种毒物进行提取,并使用表面增强拉曼光谱法检测。结果实验表明,每种毒物均有其独特的拉曼信号,这些特征信号可作为其定性的判别依据,氰化物在饮用水、苹果以及烤鸭这三种基质中的检出限均不大于1μg/mL(g);百草枯的检出限在0.05~0.1μg/mL(g)之间;毒死蜱、甲拌磷的检出限在0.05~1μg/mL(g)之间;敌鼠钠检出限在0.1~1μg/mL(g)之间,均满足现场检测需求。结论该方法操作简单且分析速度快,为毒物的快速筛查提供了较好的解决方案。     

网络治理视域下电信诈骗犯罪的心理类型与侦防策略

第四次工业革命引发了移动互联网的普及运用、量子计算时代的到来以及人工智能技术的快速发展,同时也催生了非接触性犯罪。电信诈骗犯罪是非接触性犯罪中发展最为猖獗的一种。电信诈骗犯罪的过程就是非直接接触的心理诱导过程。从心理学角度探析电信诈骗犯罪多发的原因,识别电信诈骗者实施心理诱导的骗术,切断犯罪人与被害人互动链条,以此为基础可以进一步构筑打击电信诈骗犯罪的法治防线、心理防线、科技防线和制度防线。