A Method of detecting web attacks using behavioral profile analysis technique

Behavioral analysis is a valuable aid for the design and maintenance of well-behaved distributed systems. Dataflow and reachability analyses are two orthogonal, but complementary, behavioral analysis techniques. Individually, each of these techniques may be inadequate for the analysis of large-scale distributed systems. On the one hand, dataflow analysis algorithms, while tractable, may not be sufficiently accurate to provide meaningful detection of errors. On the other hand, reachability analysis, while providing exhaustive analysis, may be computationally too expensive for complex systems. In this paper, we present a method which integrates dataflow and reachability analysis techniques to provide a flexible and effective means for analysing distributed systems at the preliminary and final design stages respectively. We also describe some effective measures taken to improve the adequacy of the individual analysis techniques using the concepts of action dependency and context constraints. A prototype supporting the method has been built, and its performance is described in this paper. A realistic example of a distributed track control system is used as a case study. >

This study examines a set of oculomotor measurements, or "oculometric" biomarkers, as potential early indicators of visual and visuomotor deficits due to retinal toxicity in asymptomatic Systemic Lupus Erythematosus (SLE) patients on long-term hydroxychloroquine (HCQ) treatment. The aim is to identify subclinical functional impairments that are otherwise undetectable by standard clinical tests and to link them to structural retinal changes. We measured oculomotor responses in a cohort of SLE patients on chronic HCQ therapy using a previously established behavioral task and analysis technique. We also examined the relationship between oculometrics, OCT measures of retinal thickness, and standard clinical perimetry measures of visual function in our patient group using Bivariate Pearson Correlation and a Linear Mixed-Effects Model (LMM). Significant visual and visuomotor deficits were found in 12 asymptomatic SLE patients on long-term HCQ therapy compared to a cohort of 17 age-matched healthy controls. Notably, six oculometrics were significantly different. The median initial pursuit acceleration was 22%, steady-state pursuit gain 16%, proportion smooth 7%, and target speed responsiveness 31% lower, while catch-up saccade amplitude was 46% and fixation error 46% larger. Excluding the two patients with diagnosed mild toxicity, four oculometrics, all but fixation error and proportion smooth, remained significantly impaired compared to controls. Across our population of 12 patients (24 retinae), we found that pursuit latency, initial acceleration, steady-state gain, and fixation error were linearly related to retinal thickness even when age was accounted for, while standard measures of clinical function (Mean Deviation and Pattern Standard Deviation) were not. Our data show that specific oculometrics are sensitive early biomarkers of functional deficits in SLE patients on HCQ that could be harnessed to assist in the early detection of HCQ-induced retinal toxicity and other visual pathologies, potentially providing early diagnostic value beyond standard visual field and OCT evaluations.

Advanced persistent threat (APT) attack is a malicious attack type which has intentional and clear targets. This attack technique has become a challenge for information security systems of organizations, governments, and businesses. The approaches of using machine learning or deep learning algorithms to analyze signs and abnormal behaviors of network traffic for detecting and preventing APT attacks have become popular in recent years. However, the APT attack detection approach that uses behavior analysis and evaluation techniques is facing many difficulties due to the lack of typical data of attack campaigns. To handle this situation, recent studies have selected and extracted the APT attack behaviors which based on datasets are built from experimental tools. Consequently, these properties are few and difficult to obtain in practical monitoring systems. Therefore, although the experimental results show good detection, it does not bring high efficiency in practice. For above reasons, in this paper, a new method based on network traffic analysis using a combined deep learning model to detect APT attacks will be proposed. Specifically, individual deep learning networks such as multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM) will also be sought, built and linked into combined deep learning networks to analyze and detect signs of APT attacks in network traffic. To detect APT attack signals, the combined deep learning models are performed in two main stages including (i) extracting IP features based on flow: In this phase, we will analyze network traffic into networking flows by IP address and then use the combined deep learning models to extract IP features by network flow; (ii) classifying APT attack IPs: Based on IP features extracted in a task (i), the APT attack IPs and normal IPs will be identified and classified. The proposal of a combined deep learning model to detect APT attacks based on network traffic is a new approach, and there is no research proposed and applied yet. In the experimental section, combined deep learning models proved their superior abilities to ensure accuracy on all measurements from 93 to 98%. This is a very good result for APT attack detection based on network traffic.

With the development of the Internet of Things (IoT) and information communication technology (ICT), the traditional healthcare system changes to real-time monitoring of patients using smart devices. These smart devices are capable of collecting vital information and communicating with other devices. The smart devices are more vulnerable to the attackers because of resource constraint devices and connected through wired or wireless. The security and privacy issues like data integrity, availability, and storage, etc., are very important to make the smart healthcare system secure. In this paper, we proposed architecture using blockchain technology to secure data sharing In the smart healthcare system, proposed proxy re-encryption and behavioral analysis techniques make data communication and storage securely. The designed architecture makes vital information tamper-proof and preserves privacy for the data of the patient. Behavioral analysis is about the detailed security analysis which gives security for privacy and stops from manipulating the data.

The Smart work Recommendation System (SWR) was created to meet the difficulties presented by the complex work market of today, which is being impacted by design and the recession. To provide highly customized job recommendations, the SJR uses a hybrid technique that combines the advantages of collaborative filtering (CF) and content-based filtering (CBF). CBF evaluates user talents using Natural Language Processing (NLP) and compares them to pertinent job descriptions. Concurrently, CF finds appropriate job suggestions by analyzing the application history and interests of comparable individuals.By adding features for pay prediction and behavioral profile, the SJR goes above and beyond conventional methods. To ensure a strong fit that goes beyond skills, behavioral profiling examines user behavior and preferences to find team dynamics and company cultures that mesh well. Real-time pay insights are provided via the integrated salary prediction tool, enabling recruiters and job seekers to make well-informed judgments about salary expectations and negotiations.The SJR uses machine learning algorithms to examine patterns and provide pertinent recommendations based on similar user profiles, so addressing the cold start issue that new users face. In order to provide consistently accurate and tailored recommendations for every user, the system is built to continuously learn from and adjust to changing user preferences and employment market trends. The SJR provides a much improved job search experience by integrating behavioral analysis, pay projection, and sophisticated filtering techniques, giving recruiters and job seekers a more accurate, individualized, and effective platform.

This study aims to bridge the gap between traditional consumer behavior analysis and modern techniques by integrating big data analysis, eye-tracking technology, and survey methods. The researchers considered that understanding consumer behavior is crucial for creating effective advertisements in the digital age. Initially, a big data analysis was performed to identify significant clusters of consumer sentiment from online reviews generated during a recent seasonal promotional campaign. The key factors were identified and grouped into the “Product”, “Model”, “Promo”, and “Effect” categories. Using these clusters as a foundation, an eye-tracking analysis measured visual attention metrics such as the fixation duration and count to understand how the participants engaged with the different advertisement content. Subsequently, a survey assessed the same participants’ purchase intentions and preferences related to the identified clusters. The results showed that the sentiment clusters related to products, promotions, and effects positively impacted the customer satisfaction. The eye-tracking data revealed that advertisements featuring products and models garnered the most visual attention, while the survey results indicated that promotional content significantly influenced the purchase intentions. This multi-step approach delivers an in-depth understanding of the factors that affect customer satisfaction and decision-making, providing valuable information for optimizing marketing strategies in the Korean skincare market. The findings emphasize the importance of integrating consumer sentiment analysis with visual engagement metrics to develop more effective and compelling marketing campaigns.

Treating problem behaviors maintained by negative reinforcement

This paper aims, firstly, to detect and characterize the argument strategy of radical behaviorism that we understand is noticeably different from the strategy followed by the rest of behaviorisms. It is argued that the specificity of radical behaviorism consists in keeping to verifying the effectiveness of pragmatic circularity between operant behavior under control and the control of the conduct, which the functional analysis of such conduct consists of. In addition, having to recognize, from such finding, the unnecessary character of all theoretical and methodological claims of methodological neobehaviorists. Secondly, a critical discussion of what is considered the fundamental error of the argument strategy of radical behaviorism. Having kept to verifying the mentioned pragmatic circularity, it would make an abstraction of what we believe is the crucial problem of behavioral biology, which is the conjugate relationship between behavior and physiology, and between behavior and evolution. Thus, radical behaviorism would have distorted and blocked the right approach to this problem, and therefore ultimately would have acted as a character (ideological) authentication of the mere behavioral training technique in which behavioral functional analysis consists of as is detached from the field of behavioral biology. Finally, it also points out how all historical behaviorisms, each in its own way, have converged on the (ideological) authentication of the condition of mere behavioral training technique in which psychological knowledge is detached from the field of behavioral biology, assuming that such knowledge would be a natural or objective science equipped with its own cognitive field.

In the contemporary academic system, regular attendance and activity of students in the classroom play a notable role in performance evaluation and quality monitoring. Conventional attendance monitoring techniques like procuring the count of students manually have challenges such as increased human intervention, and higher time consumption. Installation of Biometric attendance systems or RFID tags for attendance management raises the complexity and cost of the system. Traditional Behaviour analysis techniques such as gesture or posture recognition become a complicated task as the population of the classroom increases. This paper aims to eliminate the complexity involved in the existing classroom management systems and presents an automated system for attendance and behaviour monitoring ensuring convenience and data reliability. The proposed methodology takes a video input and marks the students' attendance, employing the concepts of Computer vision. Facial Encoding and the YOLOv4 Tiny algorithm are used to procure the attendance of the students. The data is further processed to obtain the behavioural analysis by deploying Haar Cascade Eye XML code to analyze the Sleep Status and YOLOv4 Tiny algorithm to detect the Mobile Phone usage to collectively obtain the attentiveness of students in the classroom. The attendance data and analyzed behavioural reports of each student are stored in Google Firebase. Long-term performance can be monitored by utilizing this data for continuous assessment of every student.

The inclusion element method with a simple grid model has been proposed as one of the analytical techniques of the mechanical behavior of textile composites, and the effectiveness of this method has been verified. The inclusion element method is applicable to the analysis of all types of textile composites because the element stiffness obtained by the inclusion method through cooperation with a fabric structure simulator is used. From the result of the analysis by the inclusion element method, it has been confirmed that the peculiar crimp-interchange of woven composites occurs and high tensile stress arises at the elements with fiber bundles oriented in the load direction. A comparison between the analyses using a real model and the inclusion element model has shown relatively good agreement. Although the analytical result is greatly dependent on the grid pattern, the inclusion element method can provide a sufficient accuracy of results even when the number of elements in the model is lower than that in the real model.

Recent emerging trends for automatic behavior analysis and understanding from infrastructure video are reviewed. Research has shifted from high-resolution estimation of vehicle state and instead, pushed machine learning approaches to extract meaningful patterns in aggregates in an unsupervised fashion. These patterns represent priors on observable motion, which can be utilized to describe a scene, answer behavior questions such as where is a vehicle going, how many vehicles are performing the same action, and to detect an abnormal event. The review focuses on two main methods for scene description, trajectory clustering and topic modeling. Example applications that utilize the behavioral modeling techniques are also presented. In addition, the most popular public datasets for behavioral analysis are presented. Discussion and comment on future directions in the field are also provided.

While vaccines have played a pivotal role in the fight against infectious diseases, individuals engage in online resources to find vaccine-related support and information. The benefits and consequences of these online peers are unclear and mainly cause a behavioral shift in user sentiment toward vaccination. This scoping review aims to identify the community and individual factors that longitudinally influence public behavior toward vaccination. The secondary aim is to gain insight into techniques and methodologies used to extract these factors from Twitter data. We followed PRISMA-ScR guidelines to search various online repositories. From this search process, a total of 28 most relevant articles out of 705 relevant studies. Three main themes emerged including individual and community factors influencing public attitude toward vaccination, and techniques employed to identify these factors. Anti-vax, Pro-vax, and neutral are the major communities, while misinformation, vaccine campaign, and user demographics are the common individual factors assessed during this reviewing process. Twitter user sentiment (positive, negative, and neutral) and emotions (fear, trust, sadness) were also discussed to identify the intentions to accept or refuse vaccines. SVM, LDA, BERT are the techniques used for topic modeling, while Louvain, NodeXL, and Infomap algorithms are used for community detection. This research is notable for being the first systematic review that emphasizes the dearth of longitudinal studies and the methodological and underlying practical constraints underpinning the lucrative implementation of an explainable and longitudinal behavior analysis system. Moreover, new possible research directions are suggested for the researchers to perform accurate human behavior analysis.

This paper deals with deviant behavior detection using video surveillance systems. Deviant behaviour detection is a complex problem and includes not only the recognition of the abnormal patterns of individual’s behaviour, but also the problem of general methodology development, which consists of deviant behaviour markers and techniques to identify them. A new complex approach to the object behaviour analysis is presented in this paper as well as the integrated distributed classifier generic architecture is described. The novelty of this research is the consequent subject analysis based on a complex of potentially deviant and socially dangerous behaviour attributes.

Duplicate and redundant workflows can be avoided by encouraging workflow reuse. In this paper, we present how workflow similarity matching approach can be used to further enhance existing workflow modeling tools. Most existing workflow similarity algorithms cater for control-flow oriented types of workflow which are typically associated with business workflows. The increase presence of scientific workflows that are mainly dataflow oriented calls for workflow similarity matching that caters for these types of workflows instead. We demonstrate here how our work of applying a behavioral analysis technique (taking into consideration the causal footprint of the workflow) that has been used for finding similarity in business workflows perform when use for scientific workflows. The distinction of our technique is the use of data provenance within the scientific workflow model where positional information of the workflow activities are taken in consideration in order to find matching workflow models. Preliminary experiments have shown that our proposed solution provides a viable alternative for matching scientific workflows within multiple scenarios. Furthermore, our suggested approach performs better, particularly with the removal and extension types of modification to the original workflow.

The assessment of severe language disabilities: Communicative and cognitive considerations