Metamorphic malicious code behavior detection using probabilistic inference methods

Existing antivirus programs detect malicious code based on fixed signatures; therefore, they have limitations in detecting metamorphic malicious code that lacks signature information or possesses circumventing code inserted into it. Research on the methods for detecting this type of metamorphic malicious code primarily focuses on techniques that can detect code based on behavioral similarity to known malicious code. However, these techniques measure the degree of similarity with existing malicious code using API function call patterns. Therefore, they have certain disadvantages, such as low accuracy and large detection times. In this paper, we propose a method which can overcome the limitations of existing methods by using the FP-Growth algorithm, a data mining technique, and the Markov Logic Networks algorithm, a probabilistic inference method. To perform a comparative evaluation of the proposed method's malicious code behavior detection, we performed inference experiments using malicious code with an inserted code for random malicious behavior. We performed experiments to select optimal weights for each inference rule to improve our malicious code behavior inferences’ accuracy. The results of experiments, in which we performed a comparative evaluation with the General Bayesian Network, showed that the proposed method had an 8% higher classification performance.     

On the blessing of abstraction

The “blessing of abstraction” refers to the observation that acquiring abstract knowledge sometimes proceeds more quickly than acquiring more speci?c knowledge. This observation can be formalized and reproduced by hierarchical Bayesian models. The key notion is that more abstract layers of the hierarchy have a larger “effective” sample size, because they combine information across multiple speci?c instances lower in the hierarchy. This notion relies on speci?c variables being relatively concentrated around the abstract “overhypothesis”. If the variables are highly dispersed, then the effective sample size for the abstract layers will not be appreciably larger than for the speci?c layers. Moreover, the blessing of abstraction is counterbalanced by the fact that data are more informative about lower levels of the hierarchy, because there is necessarily less stochasticity intervening between speci?c variables and the data. Thus, in certain cases abstract knowledge will be acquired more slowly than speci?c knowledge. This paper reports an experiment that shows how manipulating dispersion can produce both fast and slow acquisition of abstract knowledge in the same paradigm.     

A Monte Carlo evaluation of masked visual analysis in response‐guided versus fixed‐criteria multiple‐baseline designs

We developed masked visual analysis (MVA) as a structured complement to traditional visual analysis. The purpose of the present investigation was to compare the effects of computer‐simulated MVA of a four‐case multiple‐baseline (MB) design in which the phase lengths are determined by an ongoing visual analysis (i.e., response‐guided) versus those in which the phase lengths are established a priori (i.e., fixed criteria). We observed an acceptably low probability (less than .05) of false detection of treatment effects. The probability of correctly detecting a true effect frequently exceeded .80 and was higher when: (a) the masked visual analyst extended phases based on an ongoing visual analysis, (b) the effects were larger, (c) the effects were more immediate and abrupt, and (d) the effects of random and extraneous error factors were simpler. Our findings indicate that MVA is a valuable combined methodological and data‐analysis tool for single‐case intervention researchers.     

Assessment of Differential Rater Functioning in Latent Classes with New Mixture Facets Models

Multifaceted data are very common in the human sciences. For example, test takers' responses to essay items are marked by raters. If multifaceted data are analyzed with standard facets models, it is assumed there is no interaction between facets. In reality, an interaction between facets can occur, referred to as differential facet functioning. A special case of differential facet functioning is the interaction between ratees and raters, referred to as differential rater functioning (DRF). In existing DRF studies, the group membership of ratees is known, such as gender or ethnicity. However, DRF may occur when the group membership is unknown (latent) and thus has to be estimated from data. To solve this problem, in this study, we developed a new mixture facets model to assess DRF when the group membership is latent and we provided two empirical examples to demonstrate its applications. A series of simulations were also conducted to evaluate the performance of the new model in the DRF assessment in the Bayesian framework. Results supported the use of the mixture facets model because all parameters were recovered fairly well, and the more data there were, the better the parameter recovery.     

Pseudocontingencies in Consumer Choice: Preference for Prevalent Product Categories Decreases with Decreasing Set Quality

Building on theorizing about contingency inferences, the present research deals with the impact of product category prevalence on consumer choice. We argue that a category's prevalence in the choice set can signal high quality, but the validity depends on the aggregate quality of the choice set. With ever more low‐quality products in the set, high prevalence should increasingly signal poor rather than high quality. When choosing from a poor‐quality set, choices should thus favor products from the rare category. In four experiments, we show that poor‐quality sets increase the proportion of consumers choosing products from a rare category. Furthermore, we show that these effects increase when base rates of a category and quality become more extreme. Finally, Experiment 4 shows that contingency inferences partly mediate the effect of set quality on choice. The results extend previous research on contingency inferences, indicating the adaptive use of base‐rate information in consumer decision making. Copyright © 2017 John Wiley & Sons, Ltd.     

Reasons and doubt in Dharmottara and his critics

This paper examines the role of doubt in the classification of inferential reasons in Dharmottara’s Nyāyabindu?īkā, and in the works of his Nyāya rivals Ke?avami?ra and Jayantabha??a. As deeply systematic thinkers, larger themes in these philosophers’ metaphysics directly determine the more local issues in epistemology and inferential theory, helping to explain, and show the significance of, the differences in their accounts of inferential reasons. For Ke?ava, an ontology which includes real universals allows for an externalist, reliabilist epistemology, on which the conditions for a genuine inferential reason can be stated in an absolute (as opposed to cogniser-relative) manner. For Dharmottara, on the other hand, an ontology in which mentally constructed universals are imposed upon real particulars leads to greater dependency on the cognising agent, such that the triple conditions for the inferential reason are formulated in a cogniser-relative way. These differences lead to a more restrictive list of acceptable reasons on Dharmottara’s part, which excludes cases involving doubt.     

Priors in perception: Top-down modulation,Bayesian perceptual learning rate,and prediction error minimization

I discuss top-down modulation of perception in terms of a variable Bayesian learning rate, revealing a wide range of prior hierarchical expectations that can modulate perception. I then switch to the prediction error minimization framework and seek to conceive cognitive penetration specifically as prediction error minimization deviations from a variable Bayesian learning rate. This approach retains cognitive penetration as a category somewhat distinct from other top-down effects, and carves a reasonable route between penetrability and impenetrability. It prevents rampant, relativistic cognitive penetration of perception and yet is consistent with the continuity of cognition and perception.     

有关条件推理中概率效应的实验研究

通过预备实验选取了四种不同条件概率的条件规则和四种不同前后件概率组合的条件规则作为实验材料,以大学生为被试,考察了两种概率因素(条件概率和前后件概率)对条件推理的演绎形式(MP、DA、AC、MT)以及变通形式(四卡问题)的影响。结果表明,两种概率因素对四种条件推理的影响都非常显著,研究进一步证实了人们对四种推理的认可程度主要与范畴前提的概率成正比的结论;但概率因素对四卡问题的解决影响不明显。     

论述理解中逻辑有效与逻辑无效条件推理的研究

句法理论和心理模型理论是解释论述理解中逻辑有效 (如MP)与逻辑无效 (如AC)条件推理机制的两种不同观点。本研究通过两项实验旨在对这两种理论加以检测。实验要求被试阅读遵循MP或AC形式的故事后对故事结论的正确性做出判断。结果表明 ,在论述理解中 ,条件前提的语义联系强度影响MP推理 ;深思熟虑对MP和AC推理均产生影响。研究结果支持心理模型理论。