Cybercrime attribution : an Eastern European case study

Phishing and related cybercrime is responsible for billions of dollars in losses annually. Gartner reported more than 5 million U.S. consumers lost money to phishing attacks in the 12 months ending in September 2008 (Gartner 2009). This paper asks whether the majority of organised phishing and related cybercrime originates in Eastern Europe rather than elsewhere such as China or the USA. The Russian “Mafiya” in particular has been popularised by the media and entertainment industries to the point where it can be hard to separate fact from fiction but we have endeavoured to look critically at the information available on this area to produce a survey. We take a particular focus on cybercrime from an Australian perspective, as Australia was one of the first places where Phishing attacks against Internet banks were seen. It is suspected these attacks came from Ukrainian spammers. The survey is built from case studies both where individuals from Eastern Europe have been charged with related crimes or unsolved cases where there is some nexus to Eastern Europe. It also uses some earlier work done looking at those early Phishing attacks, archival analysis of Phishing attacks in July 2006 and new work looking at correlation between the Corruption Perception Index, Internet penetration and tertiary education in Russia and the Ukraine. The value of this work is to inform and educate those charged with responding to cybercrime where a large part of the problem originates and try to understand why.

Survey : video forensic tools

Due to extension of using CCTVs and the other video security systems in all areas, these sorts of devices have been introduced as the most important digital evidences to search and seizure crimes. Video forensics tools are developed as a part of digital forensics tools to analyze digital evidences and clear vague points of them for presenting in the courts Existing video forensics tools have been facilitated the investigation process by providing different features based on various video editing techniques. In this paper, some of the most popular video forensics tools are discussed and the strengths and shortages of them are compared and consequently, an alternative framework which includes the strengths of existing popular tools is introduced.

Speaker verification using I-vector features

This PhD research has provided novel solutions to three major challenges which have prevented the wide spread deployment of speaker recognition technology: (1) combating enrolment/ verification mismatch, (2) reducing the large amount of development and training data that is required and (3) reducing the duration of speech required to verify a speaker. A range of applications of speaker recognition technology from forensics in criminal investigations to secure access in banking will benefit from the research outcomes.

Combining opinions for use in Bayesian networks: A measurement error approach

Bayesian networks (BNs) are graphical probabilistic models used for reasoning under uncertainty. These models are becoming increasing popular in a range of fields including ecology, computational biology, medical diagnosis, and forensics. In most of these cases, the BNs are quantified using information from experts, or from user opinions. An interest therefore lies in the way in which multiple opinions can be represented and used in a BN. This paper proposes the use of a measurement error model to combine opinions for use in the quantification of a BN. The multiple opinions are treated as a realisation of measurement error and the model uses the posterior probabilities ascribed to each node in the BN which are computed from the prior information given by each expert. The proposed model addresses the issues associated with current methods of combining opinions such as the absence of a coherent probability model, the lack of the conditional independence structure of the BN being maintained, and the provision of only a point estimate for the consensus. The proposed model is applied an existing Bayesian Network and performed well when compared to existing methods of combining opinions.

Improvement in Intrusion Detection With Advances in Sensor Fusion

Various intrusion detection systems (IDSs) reported in the literature have shown distinct preferences for detecting a certain class of attack with improved accuracy, while performing moderately on the other classes. In view of the enormous computing power available in the present-day processors, deploying multiple IDSs in the same network to obtain best-of-breed solutions has been attempted earlier. The paper presented here addresses the problem of optimizing the performance of IDSs using sensor fusion with multiple sensors. The trade-off between the detection rate and false alarms with multiple sensors is highlighted. It is illustrated that the performance of the detector is better when the fusion threshold is determined according to the Chebyshev inequality. In the proposed data-dependent decision ( DD) fusion method, the performance optimization of ndividual IDSs is first addressed. A neural network supervised learner has been designed to determine the weights of individual IDSs depending on their reliability in detecting a certain attack. The final stage of this DD fusion architecture is a sensor fusion unit which does the weighted aggregation in order to make an appropriate decision. This paper theoretically models the fusion of IDSs for the purpose of demonstrating the improvement in performance, supplemented with the empirical evaluation.

Mobile agent based TCP attacker identification in MANET using the traffic history (MAITH)

TCP attacks are the major problem faced by Mobile Ad hoc Networks (MANETs) due to its limited network and host resources. Attacker traceback is a promising solution which allows a victim to identify the exact location of the attacker and hence enables the victim to take proper countermeasure near attack origins, for forensics and to discourage attackers from launching the attacks. However, attacker traceback in MANET is a challenging problem due to dynamic network topology, limited network and host resources such as memory, bandwidth and battery life. We introduce a novel method of TCP attacker Identification in MANET using the Traffic History - MAITH. Based on the comprehensive evaluation based on simulations, we showed that MAITH can successfully track down the attacker under diverse mobile multi-hop network environment with low communication, computation, and memory overhead.

FIVDL: Fingerprint Image Verification using Dictionary Learning

Fingerprints are used for identification in forensics and are classified into Manual and Automatic. Automatic fingerprint identification system is classified into Latent and Exemplar. A novel Exemplar technique of Fingerprint Image Verification using Dictionary Learning (FIVDL) is proposed to improve the performance of low quality fingerprints, where Dictionary learning method reduces the time complexity by using block processing instead of pixel processing. The dynamic range of an image is adjusted by using Successive Mean Quantization Transform (SMQT) technique and the frequency domain noise is reduced using spectral frequency Histogram Equalization. Then, an adaptive nonlinear dynamic range adjustment technique is utilized to determine the local spectral features on corresponding fingerprint ridge frequency and orientation. The dictionary is constructed using spatial fundamental frequency that is determined from the spectral features. These dictionaries help in removing the spurious noise present in fingerprints and reduce the time complexity by using block processing instead of pixel processing. Further, dictionaries are used to reconstruct the image for matching. The proposed FIVDL is verified on FVC database sets and Experimental result shows an improvement over the state-of-the-art techniques. (C) 2015 The Authors. Published by Elsevier B.V.

O ensino da Medicina Legal na formação profissional da carreira jurídica

A introdução do ensino médico-legal nos currículos de Direito, já assegura longa data e teve sua proposta relatada por Rui Barbosa e aprovada na Câmara dos Deputados, após o que o Governo brasileiro determinou a criação da cátedra de Medicina Legal nas Faculdades de Direito do país a partir de 1891. Ao longo de muitos anos foi disciplina obrigatória nos cursos de Direito transparecendo a importância da matéria na formação dos profissionais, mormente aqueles que militam na esfera criminal, mas também aplicável a, praticamente, todas as especialidades da área jurídica. A despeito da evolução das ciências forenses, que introduziram no século XXI novos horizontes da sua aplicação no contexto jurídico, ressalto ainda a própria cobrança da matéria nos concursos, para aqueles que almejam a carreira Policial. No entanto, independente da indiscutível importância da matéria, na formação acadêmica do profissional de direito, o Ministério da Educação decidiu estabelecer a Medicina Legal como disciplina optativa nos cursos de Direito. Essa medida veio ao de encontro dos interesses sociais, pois a sociedade, na busca de seus direitos, requer profissionais bem formados, com conhecimento compatível com a evolução científica. Ensinar Medicina Legal é uma árdua tarefa, pois há necessidade de valorizar mais a atividade docente e proporcionar meios para que esse ensino seja amplamente desenvolvido na formação da carreira jurídica. No presente trabalho são expostas as argumentações técnicas e pedagógicas para a manutenção de disciplina como obrigatória nos Cursos de graduação em Direito, visando, com isso, uma formação acadêmica mais completa, que corresponda a sua importante aplicação nas diversas áreas do Direito, bem como sua implantação como disciplina obrigatória nos exames de ordem da OAB.

A critical reassessment of the role of mitochondria in tumorigenesis

Background Mitochondrial DNA (mtDNA) is being analyzed by an increasing number of laboratories in order to investigate its potential role as an active marker of tumorigenesis in various types of cancer. Here we question the conclusions drawn in most of these investigations, especially those published in high-rank cancer research journals, under the evidence that a significant number of these medical mtDNA studies are based on obviously flawed sequencing results. Methods and Findings In our analyses, we take a phylogenetic approach and employ thorough database searches, which together have proven successful for detecting erroneous sequences in the fields of human population genetics and forensics. Apart from conceptual problems concerning the interpretation of mtDNA variation in tumorigenesis, in most cases, blocks of seemingly somatic mutations clearly point to contamination or sample mix-up and, therefore, have nothing to do with tumorigenesis. Conclusion The role of mitochondria in tumorigenesis remains unclarified. Our findings of laboratory errors in many contributions would represent only the tip of the iceberg since most published studies do not provide the raw sequence data for inspection, thus hindering a posteriori evaluation of the results. There is no precedent for such a concatenation of errors and misconceptions affecting a whole subfield of medical research.

基于安全操作系统的电子证据获取与存储

基于实时取证的思想,提出了一种安全可取证操作系统(security forensics operating system,简称SeFOS)的概念和实现思路.提出了其总体结构,建立了该系统的取证行为模型,对其取证服务和取证机制进行了分析并作了有关形式化描述,阐述了证据数据的采集和安全保护方法,提出把取证机制置于内核,基于进程、系统调用、内核资源分配和网络数据等获取证据的方法,并通过模拟实验验证了SeFOS的可取证性.可取证操作系统的研究对于进一步研究可取证数据库管理系统(forensic database management system,简称FDBMS)和可取证网络系统(forensic network,简称FNetWork)具有重要意义.