Two-stage database intrusion detection by combining multiple evidence and belief update

Insider threats have gained prominence and pose the most challenging threats to a database system. In this paper, we have proposed a new approach for detecting intrusive attacks in databases by fusion of information sources and use of belief update. In database intrusion detection, only intra-transactional features are not sufficient for detecting attackers within the organization as they are potentially familiar with the day-to-day work. Thus, the proposed system uses inter-transactional as well as intra-transactional features for intrusion detection. Moreover, we have also considered three different sensitivity levels of table attributes for keeping track of the malicious modification of the highly sensitive attributes more carefully. We have analyzed the performance of the proposed database intrusion detection system using stochastic models. Our system performs significantly better compared to two intrusion detection systems recently proposed in the literature.     

BLAST-SSAHA Hybridization for Credit Card Fraud Detection

A phenomenal growth in the number of credit card transactions, especially for online purchases, has recently led to a substantial rise in fraudulent activities. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. In real life, fraudulent transactions are interspersed with genuine transactions and simple pattern matching is not often sufficient to detect them accurately. Thus, there is a need for combining both anomaly detection as well as misuse detection techniques. In this paper, we propose to use two-stage sequence alignment in which a profile analyzer (PA) first determines the similarity of an incoming sequence of transactions on a given credit card with the genuine cardholder's past spending sequences. The unusual transactions traced by the profile analyzer are next passed on to a deviation analyzer (DA) for possible alignment with past fraudulent behavior. The final decision about the nature of a transaction is taken on the basis of the observations by these two analyzers. In order to achieve online response time for both PA and DA, we suggest a new approach for combining two sequence alignment algorithms BLAST and SSAHA.     

Security analysis of GTRBAC and its variants using model checking

Security analysis is a formal verification technique to ascertain certain desirable guarantees on the access control policy specification. Given a set of access control policies, a general safety requirement in such a system is to determine whether a desirable property is satisfied in all the reachable states. Such an analysis calls for the use of formal verification techniques. While formal analysis on traditional Role Based Access Control (RBAC) has been done to some extent, recent extensions to RBAC lack such an analysis. In this paper, we consider the temporal RBAC extensions and propose a formal technique using timed automata to perform security analysis by analyzing both safety and liveness properties. Using safety properties one ensures that something bad never happens while liveness properties show that some good state is also achieved. GTRBAC is a well accepted generalized temporal RBAC model which can handle a wide range of temporal constraints while specifying different access control policies. Analysis of such a model involves a process of mapping a GTRBAC based system into a state transition system. Different reduction rules are proposed to simplify the modeling process depending upon the constraints supported by the system. The effect of different constraints on the modeling process is also studied.     

Alterations in Membrane Phospholipid Fatty Acids of Gram-Positive Piezotolerant Bacterium Sporosarcina sp. DSK25 in Response to Growth Pressure

Pressure is an important thermodynamic property of the ocean and the deep biosphere that affects microbial physiology and biochemistry. Here, we report on our investigation of the response of Gram-positive piezotolerant bacterium Sporosarcina sp. DSK25 to hydrostatic pressure. Strain DSK25 responded in an adaptive manner to upshifts of growth pressure and showed systematic changes in phospholipid fatty acids. As the pressure increased from 0.1 to 10 MPa (Megapascal), unsaturated fatty acids in DSK25 increased from 21.7 to 31.1 % of total fatty acids, while the level of iso- and anteiso-branched fatty acids remained unchanged. At higher pressures (30, 50, and 60 MPa), the amount of unsaturated fatty acids decreased, and that of anteiso-branched fatty acids increased from 34.4 to 49.9 % at the expense of iso-branched fatty acids. For the first time, two polyunsaturated fatty acids (PUFA), 18:2n-6 and 18:2n-x, with the latter having much higher abundance than the former, were identified in DSK25. The concentration of the PUFA increased with growth pressure. These results indicate the involvement of unsaturated and methyl-branched fatty acids in the modulation of bacteria membrane fluidity and function over environmentally relevant parameter (pressure). Piezotolerant bacterium Sporosarcina sp. DSK25 appears to utilize two regulatory mechanisms for adaptation to high pressure, a rapid-responding mechanism on transient scale, expressed as increased biosynthesis of monounsaturated fatty acids, and a long-term adaptation mechanism in increased synthesis of anteiso-branched and polyunsaturated fatty acids. Our results further suggest that Gram-positive piezophilic bacteria respond differently than Gram-negative bacteria in adaptation to high pressure.     

A column dependency-based approach for static and dynamic recovery of databases from malicious transactions

Even state of the art database protection mechanisms often fail to prevent occurrence of malicious attacks. Since in a database environment, the modifications made by one transaction may affect the execution of some of the later transactions, it leads to spreading of the damage caused by malicious (bad) transactions. Following traditional log-based recovery schemes, one can rollback (undo) the effect of all the transactions, both malicious as well as non-malicious. In such a scenario, even the unaffected transactions are also rolled back. In this paper, we propose a column dependency-based approach to identify the affected transactions which need to be compensated along with the malicious transactions. To ensure durability, committed non-malicious transactions are then re-executed in a manner that retains database consistency. We present a static recovery algorithm as well as an on-line version of the same and prove their correctness. A detailed performance evaluation of the proposed scheme with TPC-C benchmark suite is also presented.     

Structural,microstructural, and thermal characterizations of a chalcopyrite concentrate from the Singhbhum shear zone,India

The structural and morphological characterizations of a chalcopyrite concentrate, collected from the Indian Copper Complex, Ghatshila, India, were carried out by X-ray diffraction, scanning electron mi...     

Model predictive control of resonant systems using Kautz model

The scope of this paper broadly spans in two areas: system identification of resonant system and design of an efficient control scheme suitable for resonant systems. Use of filters based on orthogonal basis functions (OBF) have been advocated for modelling of resonant process. Kautz filter has been identified as best suited OBF for this purpose. A state space based system identification technique using Kautz filters, viz. Kautz model, has been demonstrated. Model based controllers are believed to be more efficient than classical controllers because explicit use of process model is essential with these modelling techniques. Extensive literature search concludes that very few reports are available which explore use of the model based control studies on resonant system. Two such model based controllers are considered in this work, viz. model predictive controller and internal model controller. A model predictive control algorithm has been developed using the Kautz model. The efficacy of the model and the controller has been verified by two case studies, viz. linear second order underdamped process and a mildly nonlinear magnetic ball suspension system. Comparative assessment of performances of these controllers in those case studies have been carried out.     

Graph-Based Multiplayer Detection and Tracking in Broadcast Soccer Videos

In this paper, we propose a graph-based approach for detecting and tracking multiple players in broadcast soccer videos. In the first stage, the position of the players in each frame is determined by removing the non player regions. The remaining pixels are then grouped using a region growing algorithm to identify probable player candidates. A directed weighted graph is constructed, where probable player candidates correspond to the nodes of the graph while each edge links candidates in a frame with the candidates in next two consecutive frames. Finally, dynamic programming is applied to find the trajectory of each player. Experiments with several sequences from broadcasted videos of international soccer matches indicate that the proposed approach is able to track the players reasonably well even under varied illumination and ground conditions.      

Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning

We propose a novel approach for credit card fraud detection, which combines evidences from current as well as past behavior. The fraud detection system (FDS) consists of four components, namely, rule-based filter, Dempster–Shafer adder, transaction history database and Bayesian learner. In the rule-based component, we determine the suspicion level of each incoming transaction based on the extent of its deviation from good pattern. Dempster–Shafer’s theory is used to combine multiple such evidences and an initial belief is computed. The transaction is classified as normal, abnormal or suspicious depending on this initial belief. Once a transaction is found to be suspicious, belief is further strengthened or weakened according to its similarity with fraudulent or genuine transaction history using Bayesian learning. Extensive simulation with stochastic models shows that fusion of different evidences has a very high positive impact on the performance of a credit card fraud detection system as compared to other methods.     

Contributions of talin-1 to glioma cell�Cmatrix tensional homeostasis

The ability of cells to adapt their mechanical properties to those of the surrounding microenvironment (tensional homeostasis) has been implicated in the progression of a variety of solid tumours, including the brain tumour glioblastoma multiforme (GBM). GBM tumour cells are highly sensitive to extracellular matrix (ECM) stiffness and overexpress a variety of focal adhesion proteins, such as talin. While talin has been shown to play critical early roles in integrin-based force-sensing in non-tumour cells, it remains unclear whether this protein contributes to tensional homeostasis in GBM cells. Here, we investigate the role of the talin isoform talin-1 in enabling human GBM cells to adapt to ECM stiffness. We show that human GBM cells express talin-1, and we use RNA interference to suppress talin-1 expression without affecting levels of talin-2, vinculin or phosphorylated focal adhesion kinase. Knockdown of talin-1 strongly reduces both cell spreading area and random migration speed but does not significantly affect overall focal adhesion size distributions. Most strikingly, atomic force microscopy indentation reveals that talin-1 suppression compromises adaptation of cell stiffness to changes in ECM stiffness. Together, these data support a role for talin-1 in the maintenance of tensional homeostasis in GBM and suggest a functional role for enriched talin expression in this tumour.