Somatic mutations in mitochondrial DNA (mtDNA) have been demonstrated in various tumors. Mitochondrial D-loop is a non-coding region in the mitochondrial genome, which has essential transcription and replication elements, and alterations in this region may affect both these processes. The D-loop has a poly-C tract (PCT) located between 303 and 315 nucleotides known as D310, which has been identified as a frequent hot spot mutation region in human neoplasia. In the present study, 77 pairs of breast tumor and adjacent non-tumorous tissue samples were analyzed by polymerase chain reaction-single-strand conformational polymorphism, restriction fragment length polymorphism, and sequencing to evaluate the frequency of D310 (PCT) mutations and its association with clinicopathologic parameters of breast cancer. Alterations were detected in 25 of 77 (32.5 %) breast cancer samples; these included 7/25 (28 %) cases with heteroplasmy. This is the first study from Asian Indian breast cancer (BC) patients indicating a relatively high frequency of D310 mutations, suggesting that mtDNA instability at D310 may be a common characteristic of BC. However, 66.7 % of the alterations were observed in stage II BC, indicating that this may be a more important change for early progression of the disease rather than its initiation.
Attack graphs are models that offer significant capabilities to analyse security in network systems. An attack graph allows the representation of vulnerabilities, exploits and conditions for each attack in a single unifying model. This paper proposes a methodology to explore the graph using a genetic algorithm (GA). Each attack path is considered as an independent attack scenario from the source of attack to the target. Many such paths form the individuals in the evolutionary GA solution. The population-based strategy of a GA provides a natural way of exploring a large number of possible attack paths to find the paths that are most important. Thus unlike many other optimisation solutions a range of solutions can be presented to a user of the methodology.
Attack graphs have been widely used to represent and analyze security attacks. More specifically, they show all ways of how an attacker violets a security policy. Most attack graphs are constructed from nodes (vertices) and edges (arcs). Since there are so many research papers, each has a different representation of attack graphs. This paper discuses attack graph representations in terms of its nodes and edges interpretations. Other factors are addressed such the attack graph constructions and scalability.
Attack graphs are models that offer significant capabilities to analyse security in network systems because they can represent vulnerabilities, exploits and conditions for each attack in a single unifying model. This paper proposes a methodology to explore the graph. Each attack path is considered as an independent attack scenario from the source of attack to the target. The attack graph-based risk assessment model helps organisations and decision makers to make appropriate decisions in terms of security risks. We develop a genetic algorithm (GA) approach to determine the risks of attack paths and produce useful numeric values for the overall risk of a given network. The population-based strategy of a GA provides a natural way of exploring a large number of possible attack paths to find the paths that are most important.
Attack graphs are useful tools to both display possible attack vectors in simple systems and as an analysis tool for more complex systems. This paper considers the latter case and how an attack graph can be used to minimize the cost of deploying countermeasures. Specifically we develop an approach to find the minimum cut set in dependency attack graphs using a genetic algorithm (GA). The minimum cut set is a natural graph representation describing a set of security countermeasures that prevent attackers reaching their targets. The work shows that the problem maps naturally to a binary encoded GA and gives satisfactory results without the need to deploy problem specific GA operators.
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