Suspicious Activity Detection Research Articles

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.

Alert Message Generation on Detection of Suspicious Activity (Robbery)

Alert Message Generation on Detection of Suspicious Activity (Robbery)

Deep Learning Approach for Suspicious Activity Detection from Video Surveillance

Abstract: Video surveillance is getting more important as technology improves. New systems use artificial intelligence to tell the difference between normal and suspicious behaviour in live video. Since people can be unpredictable, it's hard to know for sure if something is suspicious. This system uses deep learning to identify suspicious activity in schools, like classrooms or hallways. If the system sees something suspicious, it can alert security or the police. Here's how it works: The system breaks down the video into individual pictures, one after another. Then, it uses a special program to decide if what's happening in the pictures is suspicious or normal.

Suspicious Crowd Activity Detection and Localization using Computer Vision and CNN

Suspicious Crowd Activity Detection and Localization using Computer Vision and CNN

Enhanced Approaches for Safeguarding Communication Channels from Illicit Messages

This research project presents a comprehensive strategy aimed at mitigating the pervasive threat of human trafficking through the innovative application of machine learning methodologies. The primary objective revolves around the development and deployment of sophisticated algorithms to identify and intercept human trafficking- related communications. Leveraging the power of Support Vector Machine (SVM) classification, the system meticulously scrutinizes textual data streams, flagging messages indicative of trafficking activities for further investigation. Moreover, our approach extends beyond mere message analysis by incorporating cutting-edge Utilize Convolutional Neural Network (CNN) models for performing facial recognition, age estimation, and gender identification. By harnessing the rich visual information embedded in images and videos, the system enhances its capability to identify potential victims and perpetrators with unprecedented accuracy and efficiency. A pivotal component of our solution is the seamless integration of an alert mechanism facilitated by a Simple Mail Transfer Protocol (SMTP) server. This critical feature ensures that pertinent authorities are promptly notified upon the detection of suspicious activities, enabling swift and decisive intervention. Through this amalgamation of advanced technological frameworks, our research endeavors to empower law enforcement agencies and humanitarian organizations in their tireless efforts to combat the heinous crime of human trafficking. In essence, this research represents a significant stride towards the realization of a technologically fortified defense against the exploitation of vulnerable individuals. By amalgamating state-of-the- art machine learning techniques with real-time alert systems, we aspire to create a formidable deterrent against the perpetrators of this egregious crime, thereby Ensuring the protection of human dignity and advocating for social justice.

Security in cloud computing: Methods for ensuring privacy and integration in modern applications

Cloud computing has become a necessary component for data storage and processing and is becoming more widespread. However, there are threats to the security and privacy of user data, which is why it is important to find out the most effective methods for ensuring data security in the cloud. The purpose of the study was to develop methods aimed at ensuring privacy and security in cloud environments and in modern applications. The method of analysis was used to review other publications on the topic, and the method of experiment was used for practical implementation. The main results of the study include the development of a security monitoring programme. It analyses event logs and determines the number of failed login attempts, which indicates the detection or absence of suspicious activity. Access to resources is checked, and the necessary information is displayed on the console. A comparison table of cloud platforms has been created, considering their advantages and disadvantages in the context of data security and privacy. It specifies the criteria for delivering services to the selected services. A block diagram of ways to provide security in cloud computing is developed, illustrating the relationship between various aspects of providing security in cloud systems. It contains parameters and strategies for encrypting data, protecting sensitive data, and countering attacks. Various aspects of security and methods of ensuring privacy in cloud computing are considered, namely authorisation, intrusion detection, regulatory requirements, integration with modern applications, monitoring and logging, user identification and authentication. The practical significance of the study lies in the creation of innovative ways to help improve security and privacy in cloud computing. They will allow cloud developers and administrators to effectively protect user data and ensure their privacy in modern applications

A Survey for Women Safety Alert System with Location-Based Notification and Community Awareness

Individual’s safety and well-being in public settings, particularly for women, have emerged as major concerns in modern society. Enhancing public safety by analysing women’s screams and focusing on the detection of suspicious activities and timely intimating it to the nearby police station and preferred contacts. Proposing a comprehensive system that integrates advanced audio processing techniques, machine learning algorithms, real-time communication mechanisms, combination of geospatial technology, mobile applications and sensors connected to wearable devices provides security alerts. This comprehensive approach aims to create a safer environment and empower women to take control of their safety. It’s a wonder technology and community involvement for a greater impact. By using advanced audio processing and machine learning techniques, the system can identify specific patterns or characteristics in screams that may indicate a potential threat. This innovative approach aims to enhance public safety and provide early warning signs in emergency situations. It’s an interesting application of technology that could help improve response times and prevent incidents

Novel person detection and suspicious activity recognition using enhanced YOLOv5 and motion feature map

Person and suspicious activity detection is a major challenge for image-based surveillance systems. However, the accuracy of person detection is affected by several factors, such as the presence of the person, his trajectory, posture, complex background, and object distortion. In this work, we developed a person-focused dataset that includes various behaviors of students in an educational institution, such as cheating, theft of lab equipment, fights, and threatening situations. This dataset ensures consistent and standardized identification annotations for individuals, making it suitable for detection, tracking, and behavioral analysis of individuals. In addition, we have increased the detection accuracy through an improved architecture called YOLOv5 and introduced an efficient method for detecting global and local anomalous behaviors. This method extracts motion features that accurately describe the person’s movement, speed, and direction. To evaluate the effectiveness of our proposed approach, we validated it against our proposed database and publicly available benchmark datasets. Our method achieves state-of-the-art detection accuracy, namely 96.12%, with an error rate of 6.68% compared to existing methods. The empirical results show a significant improvement in anomalous activity detection. Our paper concludes with a summary and a discussion of possible future research directions.

Suspicious Activity Detection During Physical Exams

Suspicious Activity Detection During Physical Exams

Detection of Suspicious Activity using Mobile Sensor Data and Modified Sub-space K-NN for Criminal Investigations

With the bulk availability of mobile sensors, the data collected from them mustn’t be wasted. Nowadays the creation of black-box software that collects this data is not a very difficult task. It is possible to detect suspicious unlawful events using this black-box data. In this paper, we present a novel way of doing forensic investigation using a modified sub-space K-NN (MSK) algorithm. The MSK algorithm is capable of detecting suspicious activities from mobile sensor data. Using this technique, we could detect any normal activity versus suspicious activity with 99.7 % accuracy. This study lays the foundation for future explorations, envisioning potential applications in diverse fields, including zoology. By adapting and expanding the proposed methodology, researchers in zoology could harness mobile sensor data to study animal behavior, offering an innovative approach to understanding and monitoring wildlife activities. Such interdisciplinary bridges highlight the versatility of technological advancements, where tools developed for criminal investigations may find unexpected yet valuable applications in the study of zoological phenomena

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

SummaryThe rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT‐generated data and IoT‐based networks is crucial. Many studies have explored how ML can solve specific IoT‐related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in‐depth analysis of the state‐of‐the‐art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications.

CogniWatch: Advanced Scene Analysis for Threat Detection and Public Safety

Ensuring the safety of life and property through the deployment of high-quality CCTV cameras has become indispensable in our modern world. Manual monitoring of every moment is not feasible, and the unpredictable nature of human behavior makes distinguishing between suspicious and normal activities a formidable challenge. In this research, we introduce a novel approach utilizing Convolutional Neural Networks (CNN) to discern between suspicious and routine activities within an environment. Our proposed system is designed to automatically alert relevant authorities upon detecting potentially suspicious behavior. The effectiveness of any suspicious activity detection system hinges on several critical factors, including the quality of training data, the architecture of the Machine Learning model, and the operational environment. To maintain the system's accuracy and keep it adaptive to new and evolving threats, continuous monitoring, regular updates, and ongoing improvement are imperative. Our work underscores the importance of robust data sources and the careful design of CNN-based models to ensure the system's reliability in real-world applications. This research not only addresses the pressing need for automated surveillance but also emphasizes the significance of staying current and vigilant in the ever- changing landscape of safety and security. KeyWords: CNN, Object Detection, Anomaly, Threat, Hyperparameter Tunning.

Domain adaptation assisted automatic real-time human-based video summarization

The video summarization plays a momentous role for real-time surveillance for detection of suspicious human activities in public scenes. However, efficient, and accurate human-centric video summarization in real-time videos is still a challenging issue due to several inconsistencies such as noise, illumination effects, scale, or rotational variance, etc. Though, several prominent research studies are available related to human activities detection but a robust human-centric video summarization approach is a major issue. In this research, we expound an efficient video summary generation model (ReHuSum) that creates a short summary with human. Our approach employs the conception of domain adaptation for efficient summarization via transferring the knowledge of a potent pre-trained benchmark Inception-v3 model. Additionally, a frame differencing redundancy removal technique is adopted that aids in generating a precise and accurate video summary with human. Our model generates efficient short summaries from real-time videos with an overall human detection accuracy of 99.97%. Besides, we present two novel qualitative metrics namely; summarization rate (Summrate) and summarization loss (Summloss) for assessing the efficacy of overall summarization. The approach generates a real-time human-based summary of 46s with a (Summrate) of 98.7% and (Summloss) of 0.013 for an outdoor CCTV with a duration of 25.2 min. Similarly, an indoor CCTV of 40.10 min duration is summarized to 44s at a (Summrate) of 96.5% and (Summloss) of 0.035. The model can be utilized in real-time security applications (i.e., border or home surveillance, critical infrastructure, smart cities, etc.) to generate accurate video summaries with human.

Predict, Identify and Alert on Suspicious Activity by Multiple Zone

Abstract: Suspicious human activity detection in security capture is a study topic in image processing and vision. The mysterious identification of human activity from video surveillance is an area of study in both fields. Human activity can be monitored visually in conspicuous public spaces like bus depots, airports, railway stations, financial institutions, malls, schools, and universities to avoid terrorist activity, vandalism, accidents, prohibited parking spaces, vandalism, fighting chain theft, criminality, and other unusual behavior. Extremely difficult to continually monitor public spaces, thereby an innovative video surveillance installation system that can track people's movements in real-time, classify them as routine or odd, and send out an alert is needed. The field of visual surveillance to identify aberrant actions has seen a significant amount of publications in the last ten years. Furthermore There are a few surveys in the literature for recognising various abnormal activities, but none have reviewed various abnormal activities, but none of them have reviewed various abnormal activities. This study presents the stateof-the-art in the field of recognizing suspicious behavior from surveillance recordings during the past tenyears. We provide a brief outline of the risks and challenges associated with detecting suspicious human activity. This article examines six aberrant behaviors, including the identification of abandoned objects, theft, falls, traffic accidents, and unlawful parking, as wellas the detection of violence and fire. Generally speaking, we have covered all the processes that have been [1] Foreground object extraction, object identification based on tracking or non-tracking approaches, feature extraction, classification, activity analysis, and recognition are some ofthe techniques that have been used to identify human activity from surveillance movies in the literature. This paper's goal is to give field researchers a literature assessment of six different suspicious activity identificationsystems together with its broad framework.[1]

Using CNN Algorithm Detection of Suspicious Activity from Video Surveillance

Abstract: Detecting suspicious human activity involves predicting the positions of body parts or joints from an image or video. This problem has been extensively studied in computer vision for over 15 years. It is crucial because there are numerous applications that can benefit from activity detection. For instance, human pose estimation is used in various areas such as video surveillance, tracking and understanding animal behaviour, detecting sign language, improving human-computer interaction, and capturing motion without markers.

Deep Learning Approach for Suspicious Activity Detection from Surveillance Video

Abstract: In today's uncertain world, video surveillance plays a vital role in maintaining indoor and outdoor security. Video surveillance system components such as behaviour detection, behaviour understanding, and normal or suspicious activity classification can be used for real-time applications. This article uses a hierarchical approach to detect various suspicious activities such as loitering, fainting, and trespassing. This approach is based on motion properties between different objects. First, various suspicious activities are defined using a semantic approach. Object detection is then performed by background subtraction. Detected objects are classified as live (human) or non-live (bag). These objects need to be tracked and this is done using correlation techniques. Finally, motion features and temporal information are used to classify events as normal or suspicious. A semantics-based approach is used, resulting in low computational complexity and high efficiency of the approach.

Suspicious Activity Detection Using Machine Learning

Abstract: Various technologies have been utilized to implement the safety of life and property byinstalling high quality CCTV cameras. It is not possible to manually monitor each and every moment activity. Furthermore, in practical scenario the most unpredictable one is human behaviour and it is very difficult to find whether it is suspicious or normal. In this work the notion of CNN is used to detect suspicious or normal activity in an environment, and a systemis proposed that sends an alert message to the similarity authority, in case of predicting a suspicious activity. It's worth noting that the effectiveness of a suspicious activity detection system relies on the quality of the training data, the architecture of the Machine Learning model, and the deployment environment. Ongoing monitoring, regular updates, and continuousimprovement are important for maintaining the system's accuracy and adapting it to new and emerging types of suspicious activities

Toward trustworthy human suspicious activity detection from surveillance videos using deep learning

AbstractIn today’s world, suspicious or unusual activities express threats and danger to others. For the prevention of various security issues, an automatic video detection system is very important. It is difficult to consecutively monitor camera videos recorded in public places to detect any abnormal event, so an automated video detection system is needed. The study objective is to create an intelligent and trustworthy system that will take a video stream as input and detect what kind of suspicious activity is happening in that video to reduce the time that consumes watching the video. In this work, we use three models Convolutional Neural Network (CNN), GRU, and ConvLSTM model. These models are trained on the same dataset of 6 suspicious activities of humans that are: Running, Punching, Falling, Snatching, Kicking, and Shooting. The dataset consists of various videos related to each activity. Different deep learning techniques are applied in the proposed work: preprocessing, data annotation model training, and classification. The frames are extracted from the source video, and then features are calculated through the model in Inception v3, a Convolutional Neural Network variant. On the same dataset, the CNN model attains 91.55% accuracy, the ConvLSTM model attains 88.73% accuracy, and the GRU model attains 84.01% accuracy. The performance of the proposed models is evaluated using a confusion matrix, F1-Score, precision, and recall. The proposed model proved better than other models in terms of performance and accuracy. The findings of this study prove helpful unusual events by examining a person's abnormal behavior.

A novel approach for suspicious activity detection with deep learning

A novel approach for suspicious activity detection with deep learning

Comparison of VGG-16, VGG-19, and ResNet-101 CNN Models for the purpose of Suspicious Activity Detection

This paper compares the performance of three popular convolutional neural network (CNN) models, VGG-16, VGG-19, and ResNet-101, for the task of suspicious activity detection. The VGG networks are known for their depth and the use of small convolutional filters, while ResNet is known for its residual connections that allow for deeper networks without the issue of vanishing gradients. The study utilizes a dataset of surveillance videos for training and testing the models. The results show that the VGG-19 model outperforms the other two in terms of accuracy, specifically for the detection of suspicious activities. Overall, this study demonstrates the effectiveness of the three different models for suspicious activity detection and highlights its potential for use in real-world surveillance systems.