Cloud Computing is an emerging technology that enhances capability, usability and scalability of computer systems. On account of some exclusive features, cloud computing system always differs from the traditional computer system; not only the capabilities but also the vulnerabilities and threats.
Intrusion Detection System (IDS) is a significant component of computer system security and compliance practices that protects computer systems from various types of malicious activities and attacks. Intrusion Detection Systems have been evolved over decades and various types of systems are currently available to identify and eradicate attacks based on different system conditions and different aptitudes.The main purpose of this paper is to review the state-of-art Intrusion Detection Systems available for cloud computing paradigm, which adhere to features of cloud computing architecture. Scalability, elasticity, reliability, performance, security and distributed nature of the Intrusion Detection Systems will be reviewed in order to identify suitable approaches for cloud computing.
Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification- and scoring-based prioritization methods in determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI) methods in conjunction with the K nearest neighbors' could be used in accurately categorizing the genetic factors in disease causation.
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