Method For Detecting Shilling Attacks In E-commerce Systems Using Weighted Temporal Rules

The problem of shilling attacks detecting in e-commerce systems is considered. The purpose of such attacks is to artificially change the rating of individual goods or services by users in order to increase their sales. A method for detecting shilling attacks based on a comparison of weighted temporal rules for the processes of selecting objects with explicit and implicit feedback from users is proposed. Implicit dependencies are specified through the purchase of goods and services. Explicit feedback is formed through the ratings of these products. The temporal rules are used to describe hidden relationships between the choices of user groups at two consecutive time intervals. The method includes the construction of temporal rules for explicit and implicit feedback, their comparison, as well as the formation of an ordered subset of temporal rules that capture potential shilling attacks. The method imposes restrictions on the input data on sales and ratings, which must be ordered by time or have timestamps. This method can be used in combination with other approaches to detecting shilling attacks. Integration of approaches allows to refine and supplement the existing attack patterns, taking into account the latest changes in user priorities

Method for Detecting Anomalous States of a Control Object in Information Systems Based on the Analysis of Temporal Data and Knowledge

The problem of finding the anomalous states of the control object in the management information system under conditions of uncertainty caused by the incompleteness of knowledge about this object is considered. The method of classifying the current state of the control object in real time, allowing to identify the current anomalous state. The method uses temporal data and knowledge. Data is represented by sequences of events with timestamps. Knowledge is represented as weighted temporal rules and constraints. The method includes the following key phases: the formation of sequences of logical facts; selection of temporal rules and constraints; classification based on a comparison of rules and constraints. Logical facts are represented as predicates on event attributes and reflect the state of the control object. Logical rules define valid sequences of logical facts. Performing a classification by successive comparisons of constraints and weights of the rules makes it possible to more effectively identify the anomalous state since the comparison of the constraints reduces the subset of facts comparing to the current state. The method creates conditions for improving management efficiency in the context of incomplete information on the state of a complex object by using logical inference in knowledge bases for anomalous states of such control objects

METHOD OF DETERMINING WEIGHTS OF TEMPORAL RULES IN MARKOV LOGIC NETWORK FOR BUILDING KNOWLEDGE BASE IN INFORMATION CONTROL SYSTEMS

The problem of constructing and expanding the temporal knowledge base for the information-control system is considered. This knowledge base is formally represented by the Markov logic network. It is shown that the behavior of the control object of a given class can be reflected in the form of a set of weighted temporal rules. These rules are formed on the basis of identifying links between events that reflect known variants of the behavior of the control object. A method is proposed for calculating the weights of temporal rules in a Markov logic network for a given level of detail of the control object. The level of detail is determined by the context for executing the sequences of control actions and for weighted temporal rules is specified by selecting subsets of the event attributes. The method includes such basic phases: preparation of a subset of temporal rules for a given level of detail; finding the weights of the rules taking into account the a priori probabilities of the event traces. The method creates conditions for supporting management decisions in information management systems at various levels of detail of complex management objects. Decision support is provided by predicting the probability of success in executing a sequence of actions that implement the management function in the current situation. These probabilities are determined using the weights of the temporal rules

METHOD FOR DETECTING SHILLING ATTACKS IN E-COMMERCE SYSTEMS USING WEIGHTED TEMPORAL RULES

The problem of shilling attacks detecting in e-commerce systems is considered. The purpose of such attacks is to artificially change the rating of individual goods or services by users in order to increase their sales. A method for detecting shilling attacks based on a comparison of weighted temporal rules for the processes of selecting objects with explicit and implicit feedback from users is proposed. Implicit dependencies are specified through the purchase of goods and services. Explicit feedback is formed through the ratings of these products. The temporal rules are used to describe hidden relationships between the choices of user groups at two consecutive time intervals. The method includes the construction of temporal rules for explicit and implicit feedback, their comparison, as well as the formation of an ordered subset of temporal rules that capture potential shilling attacks. The method imposes restrictions on the input data on sales and ratings, which must be ordered by time or have timestamps. This method can be used in combination with other approaches to detecting shilling attacks. Integration of approaches allows to refine and supplement the existing attack patterns, taking into account the latest changes in user priorities

Extracting Interval Temporal Logic Rules: A First Approach

Discovering association rules is a classical data mining task with a wide range of applications that include the medical, the financial, and the planning domains, among others. Modern rule extraction algorithms focus on static rules, typically expressed in the language of Horn propositional logic, as opposed to temporal ones, which have received less attention in the literature. Since in many application domains temporal information is stored in form of intervals, extracting interval-based temporal rules seems the natural choice. In this paper we extend the well-known algorithm APRIORI for rule extraction to discover interval temporal rules written in the Horn fragment of Halpern and Shoham\u27s interval temporal logic

Applied temporal Rule Mining to Time Series

Association rule mining from time series has attracted considerable interest over the last years and various methods have been developed. Temporal rules between discovered episodes provide useful knowledge for the dynamics of the problem domain and the underlying data generating process. However, temporal rule mining has received little attention over the last years. In addition, the proposed methods suffer from two significant drawbacks. First the rules they produce are not robust enough with respect to noise. Second the proposed methods are highly dependent on the choice of the parameters since small perturbations on the parameters lead to significantly different results. In this paper we propose a frame-work to derive temporal rules from time series. Our approach is based on episode rule mining that discovers temporal rules from time series in the frequency domain using the discrete cosine transform. The rules are then translated to temporal relations between time series patterns of arbitrary length. Experimental results of the proposed framework are presented in the relevant section

A Temporal-Rule Based Verification System for Business Collaboration Reliability

Based on the temporal rules defined for the business processe participating in a business collaboration, we present an implementation for a system called TiCoBTS to verify the reliability of the business collaboration

Designing compliant business processes with obligations and permissions. Business process management workshops.

The sequence and timing constraints on the activities in business processes are an important aspect of business process compliance. To date, these constraints are most often implicitly transcribed into control-flow-based process models. This implicit representation of constraints, however, complicates the verification, validation and reuse in business process design. In this paper, we investigate the use of temporal deontic assignments on activities as a means to declaratively capture the control-flow semantics that reside in business regulations and business policies. In particular, we introduce PENELOPE, a language to express temporal rules about the obligations and permissions in a business interaction, and an algorithm to generate compliant sequence-flow-based process models that can be used in business process design.

The Impact of Spatial Incongruence on an Auditory-Visual Illusion

The sound-induced flash illusion is an auditory-visual illusion--when a single flash is presented along with two or more beeps, observers report seeing two or more flashes. Previous research has shown that the illusion gradually disappears as the temporal delay between auditory and visual stimuli increases, suggesting that the illusion is consistent with existing temporal rules of neural activation in the superior colliculus to multisensory stimuli. However little is known about the effect of spatial incongruence, and whether the illusion follows the corresponding spatial rule. If the illusion occurs less strongly when auditory and visual stimuli are separated, then integrative processes supporting the illusion must be strongly dependant on spatial congruence. In this case, the illusion would be consistent with both the spatial and temporal rules describing response properties of multisensory neurons in the superior colliculus.status: publishe