Detection of minority variants within bovine respiratory syncytial virus populations using oligonucleotide-based microarrays

Microarray technology, originally developed for highly parallel examination of gene expression is regarded as a potential tool in prognosis and diagnosis. With respect to a discrimination analysis, difference as small as one nucleotide base can be distinguished using oligonucleotide-basedmicroarrays. However, this degree of specificity is dependent on several parameters, including the size of the oligoprobes and the sequence context of the probes (e.g. local melting temperature), hybridization conditions and to some extent the chemistry of the glass slides onto which the probes are deposited. Using bovine respiratory syncytial virus (BRSV) as a model study, an oligonucleotide-based microarray approach was developed to measure the relative abundance of a particular single nucleotide variant within mixed BRSV populations. Using this technology, we show that it is possible to discriminate at a rate of 1%, minority variants in a BRSV population

Metamorphic Code Generation from LLVM IR Bytecode

Metamorphic software changes its internal structure across generations with its functionality remaining unchanged. Metamorphism has been employed by malware writers as a means of evading signature detection and other advanced detection strate- gies. However, code morphing also has potential security benefits, since it increases the “genetic diversity” of software. In this research, we have created a metamorphic code generator within the LLVM compiler framework. LLVM is a three-phase compiler that supports multiple source languages and target architectures. It uses a common intermediate representation (IR) bytecode in its optimizer. Consequently, any supported high-level programming language can be transformed to this IR bytecode as part of the LLVM compila- tion process. Our metamorphic generator functions at the IR bytecode level, which provides many advantages over previously developed metamorphic generators. The morphing techniques that we employ include dead code insertion—where the dead code is actually executed within the morphed code—and subroutine permutation. We have tested the effectiveness of our code morphing using hidden Markov model analysis

Securing library information system: Vulnerabilities and threats

Threats and vulnerabilities in computers and networks are common nowadays since computers are widely used by the public. The risks of computer threats and vulnerabilities are high since most computers are connected to the internet. Library Information Systems is also vulnerable to attack since it is a public access institution. Majority of users are naive when it comes to computer and network securities. Some breaches in Library Information System are intentional and some are unintentional. Risks analysis should be done to find the threats and risks in designing the Library Information System. Threats are made possible due to lack of proper procedures, software flaws and policies. The administrators should anticipate all the possible attacks and their mitigation techniques. In this paper, we will try to address various issues arise from this vulnerabilities and threats. We will also describe how we can reduce and overcome this vulnerabilities and threats

Machine-Learning based analysis and classification of Android malware signatures

Multi-scanner Antivirus (AV) systems are often used for detecting Android malware since the same piece of software can be checked against multiple different AV engines. However, in many cases the same software application is flagged as malware by few AV engines, and often the signatures provided contradict each other, showing a clear lack of consensus between different AV engines. This work analyzes more than 80 thousand Android applications flagged as malware by at least one AV engine, with a total of almost 260 thousand malware signatures. In the analysis, we identify 41 different malware families, we study their relationships and the relationships between the AV engines involved in such detections, showing that most malware cases belong to either Adware abuse or really dangerous Harmful applications, but some others are unspecified (or Unknown). With the help of Machine Learning and Graph Community Algorithms, we can further combine the different AV detections to classify such Unknown apps into either Adware or Harmful risks, reaching F1-score above 0.84.The authors would like to acknowledge the support of the national project TEXEO (TEC2016-80339-R), funded by the Ministerio de Economia y Competitividad of SPAIN through, and the EU-funded H2020 SMOOTH project, Spain (grant no. H2020-786741). Similarly, the authors would like to remark the support provided by the Tacyt system (https://www.elevenpaths.com/es/te cnologia/tacyt/index.html) for the collection and labeling of AV information. Finally, Ignacio Martin would like to acknowledge the support granted by the Spanish Ministry of education through the FPU scholarship he holds (FPU15/03518)

Evolution and Detection of Polymorphic and Metamorphic Malwares: A Survey

Malwares are big threat to digital world and evolving with high complexity. It can penetrate networks, steal confidential information from computers, bring down servers and can cripple infrastructures etc. To combat the threat/attacks from the malwares, anti- malwares have been developed. The existing anti-malwares are mostly based on the assumption that the malware structure does not changes appreciably. But the recent advancement in second generation malwares can create variants and hence posed a challenge to anti-malwares developers. To combat the threat/attacks from the second generation malwares with low false alarm we present our survey on malwares and its detection techniques.Comment: 5 Page

Evolution of Computer Virus Concealment and Anti-Virus Techniques: A Short Survey

This paper presents a general overview on evolution of concealment methods in computer viruses and defensive techniques employed by anti-virus products. In order to stay far from the anti-virus scanners, computer viruses gradually improve their codes to make them invisible. On the other hand, anti-virus technologies continually follow the virus tricks and methodologies to overcome their threats. In this process, anti-virus experts design and develop new methodologies to make them stronger, more and more, every day. The purpose of this paper is to review these methodologies and outline their strengths and weaknesses to encourage those are interested in more investigation on these areas