7,301 research outputs found
Statistical and fuzzy approach for database security
A new type of database anomaly is described by
addressing the concept of Cumulated Anomaly in this
paper. Dubiety-Determining Model (DDM), which is a
detection model basing on statistical and fuzzy set
theories for Cumulated Anomaly, is proposed. DDM
can measure the dubiety degree of each database
transaction quantitatively. Software system
architecture to support the DDM for monitoring
database transactions is designed. We also
implemented the system and tested it. Our
experimental results show that the DDM method is
feasible and effective
Recommended from our members
A dubiety-determining based model for database cumulated anomaly intrusion
The concept of Cumulated Anomaly (CA), which describes a new type of database anomalies, is addressed. A
typical CA intrusion is that when a user who is authorized to modify data records under certain constraints deliberately
hides his/her intentions to change data beyond constraints in different operations and different transactions. It happens
when some appearing to be authorized and normal transactions lead to certain accumulated results out of given thresholds.
The existing intrusion techniques are unable to deal with CAs. This paper proposes a detection model,
Dubiety-Determining Model (DDM), for Cumulated Anomaly. This model is mainly based on statistical theories and fuzzy
set theories. It measures the dubiety degree, which is presented by a real number between 0 and 1, for each database
transaction, to show the likelihood of a transaction to be intrusive. The algorithms used in the DDM are introduced. A
DDM-based software architecture has been designed and implemented for monitoring database transactions. The
experimental results show that the DDM method is feasible and effective
Adaptive probability scheme for behaviour monitoring of the elderly using a specialised ambient device
A Hidden Markov Model (HMM) modified to work in combination with a Fuzzy System is utilised to determine the current behavioural state of the user from information obtained with specialised hardware. Due to the high dimensionality and not-linearly-separable nature of the Fuzzy System and the sensor data obtained with the hardware which informs the state decision, a new method is devised to update the HMM and replace the initial Fuzzy System such that subsequent state decisions are based on the most recent information. The resultant system first reduces the dimensionality of the original information by using a manifold representation in the high dimension which is unfolded in the lower dimension. The data is then linearly separable in the lower dimension where a simple linear classifier, such as the perceptron used here, is applied to determine the probability of the observations belonging to a state. Experiments using the new system verify its applicability in a real scenario
Recommended from our members
A survey of intrusion detection techniques in Cloud
Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks
Recommended from our members
Anomaly Detection in IoT-Based PIR Occupancy Sensors to Improve Building Energy Efficiency
- …