Structured Query Language Injection Attack (SQLIA) is the most exposed to attack on the Internet. From this attack, the attacker can take control of the database therefore be able to interpolate the data from the data...Structured Query Language Injection Attack (SQLIA) is the most exposed to attack on the Internet. From this attack, the attacker can take control of the database therefore be able to interpolate the data from the database server for the website. Hence, the big challenge became to secure such website against attack via the Internet. We have presented different types of attack methods and prevention techniques of SQLIA which were used to aid the design and implementation of our model. In the paper, work is separated into two parts. The first aims to put SQLIA into perspective by outlining some of the materials and researches that have already been completed. The section suggesting methods of mitigating SQLIA aims to clarify some misconceptions about SQLIA prevention and provides some useful tips to software developers and database administrators. The second details the creation of a filtering proxy server used to prevent a SQL injection attack and analyses the performance impact of the filtering process on web application.展开更多
With the speeding up of social activities,rapid changes in lifestyles,and an increase in the pressure in professional fields,people are suffering from several types of sleep-related disorders.It is a very tedious task...With the speeding up of social activities,rapid changes in lifestyles,and an increase in the pressure in professional fields,people are suffering from several types of sleep-related disorders.It is a very tedious task for clinicians to monitor the entire sleep durations of the subjects and analyse the sleep staging in traditional and manual laboratory environmental methods.For the purpose of accurate diagnosis of different sleep disorders,we have considered the automated analysis of sleep epochs,which were collected from the subjects during sleep time.The complete process of an automated approach of sleep stages5 classification is majorly executed through four steps:pre-processing the raw signals,feature extraction,feature selection,and classification.In this study,we have extracted 12 statistical properties from input signals.The proposed models are tested in three different combinations of features sets.In the first experiment,the feature set contained all the 12 features.The second and third experiments were conducted with the nine and five best features.The patient records come from the ISRUC-Sleep database.The highest classification accuracy was achieved for sleep staging through combinations with the five feature set.From the categories of the subjects,the reported accuracy results were found to exceed above 90%.As per the outcome from the proposed system the random forest classification techniques achieved best accuracy incomparable to that of the other two classifiers.展开更多
文摘Structured Query Language Injection Attack (SQLIA) is the most exposed to attack on the Internet. From this attack, the attacker can take control of the database therefore be able to interpolate the data from the database server for the website. Hence, the big challenge became to secure such website against attack via the Internet. We have presented different types of attack methods and prevention techniques of SQLIA which were used to aid the design and implementation of our model. In the paper, work is separated into two parts. The first aims to put SQLIA into perspective by outlining some of the materials and researches that have already been completed. The section suggesting methods of mitigating SQLIA aims to clarify some misconceptions about SQLIA prevention and provides some useful tips to software developers and database administrators. The second details the creation of a filtering proxy server used to prevent a SQL injection attack and analyses the performance impact of the filtering process on web application.
文摘With the speeding up of social activities,rapid changes in lifestyles,and an increase in the pressure in professional fields,people are suffering from several types of sleep-related disorders.It is a very tedious task for clinicians to monitor the entire sleep durations of the subjects and analyse the sleep staging in traditional and manual laboratory environmental methods.For the purpose of accurate diagnosis of different sleep disorders,we have considered the automated analysis of sleep epochs,which were collected from the subjects during sleep time.The complete process of an automated approach of sleep stages5 classification is majorly executed through four steps:pre-processing the raw signals,feature extraction,feature selection,and classification.In this study,we have extracted 12 statistical properties from input signals.The proposed models are tested in three different combinations of features sets.In the first experiment,the feature set contained all the 12 features.The second and third experiments were conducted with the nine and five best features.The patient records come from the ISRUC-Sleep database.The highest classification accuracy was achieved for sleep staging through combinations with the five feature set.From the categories of the subjects,the reported accuracy results were found to exceed above 90%.As per the outcome from the proposed system the random forest classification techniques achieved best accuracy incomparable to that of the other two classifiers.
