Suspicious Behavior Detection Research Articles

Suspicious behaviour detection in multilayer social networks using PF-KMA and SS-GAE techniques

Multilayer Social networks are an important part of human life to interact on different networks at the same time. Due to the openness of such networks, they become a platform for spammers to spread malicious behaviors. Hence, there is an urgent need for effective detection of malicious behaviors; thereby, enabling the networks to take mitigation actions to decrease the possibility to reward such activities. Detection of suspicious behaviors in previous works is challenging due to the problems of community detection, the large amount of feature corruption, and memory requirements. Thus, to deal with such problems, in this paper, an efficient clustering-based detection of malicious users in multilayer social networks is proposed. Initially, the input dataset is pre-processed and used for Exponential Distribution based Erdős–Rényi based graph construction. From the graph structure, two types of data, such as user representations and graph features are extracted for graph encoding using the Soft Sign activated Graph Auto Encoder model. Then, the decoding is done to predict the information diffusion level, thereby, ranking the users using the Laplace Regularization technique. Then, the ranked users are clustered into different groups using the Pareto Front based K-Means Clustering Algorithm technique. Finally, the experimental results were analyzed to demonstrate the efficacy of the proposed model to detect malicious users in multilayer social networks.

Active Chat Monitoring and Suspicious Chat Detection Over Internet

In the digital age, the proliferation of online communication platforms has facilitated unprecedented levels of connectivity and interaction. However, this interconnectedness has also introduced new challenges, particularly in ensuring the safety and security of users in online environments. One critical area of concern is the monitoring and detection of suspicious activities within chat platforms, where malicious actors may engage in harmful behaviors such as cyberbullying, harassment, or illicit activities. This research paper focuses on the development and implementation of active chat monitoring techniques for the detection of suspicious behavior over the internet. By leveraging advancements in natural language processing (NLP), machine learning, and data analytics, this study aims to explore effective methodologies for real-time monitoring and analysis of chat conversations to identify potentially harmful content. The paper will delve into various approaches for detecting suspicious patterns, including keyword analysis, sentiment analysis, and anomaly detection algorithms. Additionally, considerations for privacy, ethics, and legal implications surrounding chat monitoring will be discussed. Ultimately, this research endeavors to contribute to the enhancement of online safety measures by providing insights into the design and implementation of proactive monitoring systems capable of identifying and mitigating risks in virtual communication spaces. Keywords— Active Chat Monitoring, cybersecurity, user experience, digital era, security, user-controlled, effectiveness, evolving threat, Chat content analysis, Threat intelligence.

Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention.

The rise in crime rates in many parts of the world, coupled with advancements in computer vision, has increased the need for automated crime detection services. To address this issue, we propose a new approach for detecting suspicious behavior as a means of preventing shoplifting. Existing methods are based on the use of convolutional neural networks that rely on extracting spatial features from pixel values. In contrast, our proposed method employs object detection based on YOLOv5 with Deep Sort to track people through a video, using the resulting bounding box coordinates as temporal features. The extracted temporal features are then modeled as a time-series classification problem. The proposed method was tested on the popular UCF Crime dataset, and benchmarked against the current state-of-the-art robust temporal feature magnitude (RTFM) method, which relies on the Inflated 3D ConvNet (I3D) preprocessing method. Our results demonstrate an impressive 8.45-fold increase in detection inference speed compared to the state-of-the-art RTFM, along with an F1 score of 92%,outperforming RTFM by 3%. Furthermore, our method achieved these results without requiring expensive data augmentation or image feature extraction.

Early Detection of Suspicious Behaviors for Safe Residence from Movement Trajectory Data

Early detection of people’s suspicious behaviors can aid in the prevention of crimes and make the community safer. Existing methods that are focused on identifying abnormal behaviors from video surveillance that are based on computer vision, which are more suitable for detecting ongoing behaviors. While criminals intend to avoid abnormal behaviors under surveillance, their suspicious behaviors prior to crimes will be unconsciously reflected in the trajectories. Herein, we characterize several suspicious behaviors from unusual movement patterns, unusual behaviors, and unusual gatherings of people, and analyze their features that are hidden in the trajectory data. Meanwhile, the algorithms for suspicious behavior detection are proposed based on the main features of the corresponding behavior, which employ spatiotemporal clustering, semantic annotation, outlier detection, and other methods. A practical trajectory dataset (i.e., TucityLife) containing more than 1000 suspicious behaviors was collected, and experiments were conducted to verify the effectiveness of the proposed method. The results indicate that the proposed method for suspicious behavior detection has a recall of 93.5% and a precision of 87.6%, demonstrating its excellent performance in identifying the possible offenders and potential target places. The proposed methods are valuable for preventing city crime and supporting the appropriate allocation of police resources.

Quality Control in Crowdsourcing based on Fine-Grained Behavioral Features

Crowdsourcing is popular for large-scale data collection and labeling, but a major challenge is on detecting low-quality submissions. Recent studies have demonstrated that behavioral features of workers are highly correlated with data quality and can be useful in quality control. However, these studies primarily leveraged coarsely extracted behavioral features, and did not further explore quality control at the fine-grained level, i.e., the annotation unit level. In this paper, we investigate the feasibility and benefits of using fine-grained behavioral features, which are the behavioral features finely extracted from a worker's individual interactions with each single unit in a subtask, for quality control in crowdsourcing. We design and implement a framework named Fine-grained Behavior-based Quality Control (FBQC) that specifically extracts fine-grained behavioral features to provide three quality control mechanisms: (1) quality prediction for objective tasks, (2) suspicious behavior detection for subjective tasks, and (3) unsupervised worker categorization. Using the FBQC framework, we conduct two real-world crowdsourcing experiments and demonstrate that using fine-grained behavioral features is feasible and beneficial in all three quality control mechanisms. Our work provides clues and implications for helping job requesters or crowdsourcing platforms to further achieve better quality control.

Temporal Saliency-Based Suspicious Behavior Pattern Detection

The topic of suspicious behavior detection has been one of the most emergent research themes in computer vision, video analysis, and monitoring. Due to the huge number of CCTV (closed-circuit television) systems, it is not easy for people to manually identify CCTV for suspicious motion monitoring. This paper is concerned with an automatic suspicious behavior detection method using a CCTV video stream. Observers generally focus their attention on behaviors that vary in terms of magnitude or gradient of motion and behave differently in rules of motion with other objects. Based on these facts, the proposed method detected suspicious behavior with a temporal saliency map by combining the moving reactivity features of motion magnitude and gradient extracted by optical flow. It has been tested on various video clips that contain suspicious behavior. The experimental results show that the performance of the proposed method is good at detecting the six designated types of suspicious behavior examined: sudden running, colliding, falling, jumping, fighting, and slipping. The proposed method achieved an average accuracy of 93.89%, a precision of 96.21% and a recall of 94.90%.

Security techniques for intelligent spam sensing and anomaly detection in online social platforms

The recent advances in communication and mobile technologies made it easier to access and share information for most people worldwide. Among the most powerful information spreading platforms are the Online Social Networks (OSN)s that allow Internet-connected users to share different information such as instant messages, tweets, photos, and videos. Adding to that many governmental and private institutions use the OSNs such as Twitter for official announcements. Consequently, there is a tremendous need to provide the required level of security for OSN users. However, there are many challenges due to the different protocols and variety of mobile apps used to access OSNs. Therefore, traditional security techniques fail to provide the needed security and privacy, and more intelligence is required. Computational intelligence adds high-speed computation, fault tolerance, adaptability, and error resilience when used to ensure security in OSN apps. This research provides a comprehensive related work survey and investigates the application of artificial neural networks for intrusion detection systems and spam filtering for OSNs. In addition, we use the concept of social graphs and weighted cliques in the detection of suspicious behavior of certain online groups and to prevent further planned actions such as cyber/terrorist attacks before they happen.

Theory of mind and the detection of suspicious behavior.

Theory of mind and the detection of suspicious behavior.

Theory of Mind and the Detection of Suspicious Behavior

Two studies examined whether deficits in theory of mind—the ability to understand and predict the intent, behaviors, and emotions of others—are related to slowness in recognizing that there is something suspicious about the way in which an interaction is unfolding. Participants listened to vignettes, some culminating in a crime, and indicated when they detected something suspicious about the situation unfolding in the scenario. Theory of mind was assessed using the Frith-Happe animations task, which requires the identification of mental states and emotions of pairs of interacting animated geometric stimuli ( White, Coniston, Rogers, & Frith, 2011 ). A meta-analysis of the studies found meaningful—albeit relatively weak—negative relationships between latency to react to cues to suspicious behavior and performance on the theory of mind measure. Ways to refine this novel paradigm that might sharpen the relationship between the key variables are discussed.

Supplemental Material for Theory of Mind and the Detection of Suspicious Behavior

Supplemental Material for Theory of Mind and the Detection of Suspicious Behavior

Detection of Covert and Suspicious DNS Behavior in Advanced Persistent Threats

Detection of Covert and Suspicious DNS Behavior in Advanced Persistent Threats

Uncovering Suspicious Activity From Partially Paired and Incomplete Multimodal Data

Multimodal data can be used to gain additional perspective on a phenomenon. For applications, such as security and the detection of suspicious activity, the need to aggregate and analyze data from multiple modes is vital. Recent research in suspicious behavior detection has introduced methods for identifying and scoring dense blocks in multivariate tensors, which are consistent indicators of suspicious activity. None yet, however, have proposed a method for the merging and analysis of multiple modes of data for suspicious behavior, especially when the set of items described in each data set do not match—that is, the data is partially paired—which is common when data sets originate from different sources. Neither has a method been described for dealing with the similar case of incomplete data. This paper introduces a technique for multimodal data analysis for suspicious activity detection when the data are only partially paired and/or incomplete. The method is applied to synthetic and real data, demonstrating strong precision and recall even in poorly paired cases.

Suspicious Behavior Detection: Current Trends and Future Directions

Different real-world applications have varying definitions of suspicious behaviors. Detection methods often look for the most suspicious parts of the data by optimizing scores, but quantifying the suspiciousness of a behavioral pattern is still an open issue.

Suspicious Behavior Detection in Debit Card Transactions using Data Mining

The approach used in this paper is an implementation of a data mining process against real-life transactions of debit cards with the aim of detecting suspicious behavior. The framework designed for this purpose has been obtained through merging supervised and unsupervised models. First, due to unlabeled data, Twostep and Self-Organizing Map algorithms have been used in clustering the transactions. A C5.0 classification algorithm has been applied to evaluate supervised models and also to detect suspicious behaviors. An innovative plan has been designed to evaluate hybrid models and select the most appropriate model for the solution of the fraud detection problem. The evaluation of the models and the final analysis of the data took place in four stages. The appropriate hybrid model was selected from among 16 models. The results show a high ability of selected model in detecting suspicious behavior in transactions involving debit cards.

Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls

Expert video-surveillance systems are a powerful tool applied in varied scenarios with the aim of automatizing the detection of different risk situations and helping human security officers to take appropriate decisions in order to enhance the protection of assets. In this paper, we propose a complete expert system focused on the real-time detection of potentially suspicious behaviors in shopping malls. Our video-surveillance methodology contributes several innovative proposals that compose a robust application which is able to efficiently track the trajectories of people and to discover questionable actions in a shop context. As a first step, our system applies an image segmentation to locate the foreground objects in scene. In this case, the most effective background subtraction algorithms of the state of the art are compared to find the most suitable for our expert video-surveillance application. After the segmentation stage, the detected blobs may represent full or partial people bodies, thus, we have implemented a novel blob fusion technique to group the partial blobs into the final human targets. Then, we contribute an innovative tracking algorithm which is not only based on people trajectories as the most part of state-of-the-art methods, but also on people appearance in occlusion situations. This tracking is carried out employing a new two-step method: (1) the detections-to-tracks association is solved by using Kalman filtering combined with an own-designed cost optimization for the Linear Sum Assignment Problem (LSAP); and (2) the occlusion management is based on SVM kernels to compute distances between appearance features such as GCH, LBP and HOG. The application of these three features for recognizing human appearance provides a great performance compared to other description techniques, because color, texture and gradient information are effectively combined to obtain a robust visual description of people. Finally, the resultant trajectories of people obtained in the tracking stage are processed by our expert video-surveillance system for analyzing human behaviors and identifying potential shopping mall alarm situations, as are shop entry or exit of people, suspicious behaviors such as loitering and unattended cash desk situations. With the aim of evaluating the performance of some of the main contributions of our proposal, we use the publicly available CAVIAR dataset for testing the proposed tracking method with a success near to 85% in occlusion situations. According to this performance, we corroborate in the presented results that the precision and efficiency of our tracking method is comparable and slightly superior to the most recent state-of-the-art works. Furthermore, the alarms given off by our application are evaluated on a naturalistic private dataset, where it is evidenced that our expert video-surveillance system can effectively detect suspicious behaviors with a low computational cost in a shopping mall context.

A Novel Approach Automatic Detection of Suspicious Behavior

We propose an efficient method for automatic detection of suspicious behavior in video surveillance data. First of all, we cluster a set of sequences labeled as normal or suspicious. Then, we assign new observation sequences to behavior clusters. We label a sequence as suspicious if it maps to an existing model of suspicious behavior or does not map to any existing model according to the corresponding HMMs. We evaluate our proposed method on a real-world video surveillance and find that the method is very effective at detecting suspicious behavior.

Семантико-вероятностная сеть для определения контекста видеопотока в системах видеонаблюдения

In the recent years a considerable growth of video surveillance uses can be observed. Due to the increasing scale and complexity of such systems manual maintenance becomes impossible, which raises a problem of developing automated intelligent surveillance systems. One of the most important tasks solved by surveillance systems is human behavior analysis and recognition, which has many applications from patient state monitoring in medical establishments to suspicious behavior detection and to crime prevention. Analysis shows, that graphical probabilistic models such as Bayesian networks are widely used and are highly effective approach for human behavior recognition. However, a lack of strict data formalization and structuring makes the task of building a Bayesian network for complex human behavior recognition a highly difficult task. To surpass that limitation, we suggest introducing a domain ontology — a hierarchical decomposition of video contents in the terms of scenarios, situations, object roles and states, which are derived from the low-level features, computed from the annotated ground-truth video data using a set of computer vision methods, and then using this otology as a basis for Bayesian network structure learning. The performance of the proposed framework was evaluated using a HMDB and a CAVIAR datasets, and we noticed an increased efficiency of human behavior recognition compared to other approaches

User Requirements Elicitation in Security and Counter-Terrorism: A Human Factors Approach

This paper presents work conducted as part the ‘Shades of Grey’ (EP/H02302X/1) research project that aims to develop scientific interventions for the detection of suspicious behaviors in public spaces. To provide an understanding of security and counter-terrorism work in different contexts the project adopted a human factors approach as part of a program of user requirements gathering exercises. These activities focussed on the needs of different end-users and stakeholders, ranging from frontline security personnel, managers and strategic security policy makers. By taking a user-centred approach, the discipline of human factors can be integrated into the security/counter-terrorism domain to support the design of practical security solutions. This paper presents a case study investigation using data collected from three security agencies. Common themes are explored, ranging from ‘the importance of temporal measures’ to ‘enhancing positive user experiences’. These factors are discussed in relation to the practical application of human factors methods within security research.

Detection of suspicious communication behavior of one program based on method of difference contrast

In allusion to the damage of baleful program for computer users and its fast growth in quantity,a detection system based on network communication behavior data of one program was proposed.A detection model of suspicious communication behavior based on difference contrast was emphasized.The cooperative work diagram of each module of the system and program code of critical technology of the system were given.The test result shows that the system can detect network communication,communication process and domain name for one program.

Suspicious Behavior Detection Using Case-Based Reasoning for Intelligent Room

Suspicious Behavior Detection Using Case-Based Reasoning for Intelligent Room