Security Surveillance Systems Research Articles

Active Security Surveillance and Object Detection

Active security surveillance and object detection have become crucial components in modern security systems, driven by advancements in computer vision and machine learning. These systems are designed to enhance real-time monitoring and threat detection across various environments, such as public spaces, transportation hubs, and critical infrastructure. Active security surveillance systems continuously scan and analyze video feeds or sensor data to identify potential security threats, unauthorized activities, or anomalies. They employ advanced algorithms for motion detection, facial recognition, and behavioural analysis, allowing for the rapid identification of suspicious activities or individuals. Object detection, a key element of these systems, involves the identification and classification of objects within a given scene. This is typically achieved through deep learning techniques, such as convolutional neural networks (CNNs), which can accurately detect and categorize objects like vehicles, weapons, or unattended bags. The integration of object detection with active surveillance enables more precise and automated responses to potential threats, reducing reliance on human operators and minimizing the risk of oversight. The adoption of active surveillance and object detection technologies has led to significant improvements in security management, offering enhanced situational awareness and faster response times. However, these technologies also raise concerns regarding privacy, data security, and ethical implications, particularly in their deployment in public and private spaces. Addressing these concerns requires careful consideration of data governance, transparency, and regulatory compliance to balance security needs with individual rights. As technology continues to evolve, ongoing research and development are essential to improving the accuracy, efficiency, and ethical deployment of active security surveillance and object detection systems.

Real-time object detection, tracking, and monitoring framework for security surveillance systems

The concept of security is becoming a global challenge, and governments, stakeholders, corporate societies, and individuals must urgently create a reasonable protection mechanism for good. Therefore, a real-time surveillance system is essential for detection, tracking, and monitoring. Many studies have attempted to provide better solutions but more research and better approaches are essential. This study presents a real-time framework for object detection and tracking for security surveillance systems. The system has been designed based on approximate median filtering, component labeling, background subtraction, and deep learning approaches. The new algorithms for object detection, tracking, and recognition have been implemented using Python and integrated with C# programming languages for ease of use. A software application framework is designed, implemented, and evaluated. The experimental results based on MOT-Challenge performance metrics show that the proposed algorithms have much better performance in terms of accuracy and precision on the MOT15, MOT16, and MOT17 datasets compared to state-of-the-art approaches. This framework also provides an accurate and effective means of monitoring and recognizing moving objects. The software development, including the design of the framework user interfaces, is coded in the C# programming language and integrated with Python using Microsoft Visual Studio (2019 edition). The integration is performed to provide a convenient user interface and to enable the execution of the framework as a standard and standalone software application. Future studies will consider the dynamic scalability of the framework to accommodate different surveillance application areas in overcrowded scenarios. Multiple data sources are integrated to enhance the performance for different scene times, locations, and weather conditions. Furthermore, other object-detection techniques such as You Only Look Once (YOLO) and its variants shall be considered in future studies. These techniques allow the framework to adapt to complex situations in which security surveillance is challenging.

A Multi-Stage Approach to UAV Detection, Identification, and Tracking Using Region-of-Interest Management and Rate-Adaptive Video Coding

The drone industry has opened its market to ordinary people, making drones prevalent in daily life. However, safety and security issues have been raised as the number of accidents rises (e.g., losing control and colliding with people or invading secured properties). For safety and security purposes, observers and surveillance systems must be aware of UAVs invading aerial spaces. This paper introduces a UAV tracking system with ROI-based video coding capabilities that can efficiently encode videos with a dynamic coding rate. The proposed system initially uses deep learning-based UAV detection to locate the UAV and determine the ROI surrounding the detected UAVs. Afterward, the ROI is tracked using optical flow, which is relatively light in computational load. Furthermore, our devised module for effective compression, XROI-DCT, is applied to non-ROI regions, so a different coding rate is applied depending on the region during encoding. The proposed UAV tracking system is implemented and evaluated by utilizing videos from YouTube, Kaggle, and a video of 3DR Solo2 taken by the authors. The evaluation verifies that the proposed system can detect and track UAVs significantly faster than YOLOv7 and efficiently encode a video, compressing 70% of the video based on the ROI. Additionally, it can successfully identify the UAV model with a high accuracy of 0.9869 ROC–AUC score.

Robust Detection of Critical Events in the Context of Railway Security Based on Multimodal Sensor Data Fusion.

Effective security surveillance is crucial in the railway sector to prevent security incidents, including vandalism, trespassing, and sabotage. This paper discusses the challenges of maintaining seamless surveillance over extensive railway infrastructure, considering both technological advances and the growing risks posed by terrorist attacks. Based on previous research, this paper discusses the limitations of current surveillance methods, particularly in managing information overload and false alarms that result from integrating multiple sensor technologies. To address these issues, we propose a new fusion model that utilises Probabilistic Occupancy Maps (POMs) and Bayesian fusion techniques. The fusion model is evaluated on a comprehensive dataset comprising three use cases with a total of eight real life critical scenarios. We show that, with this model, the detection accuracy can be increased while simultaneously reducing the false alarms in railway security surveillance systems. This way, our approach aims to enhance situational awareness and reduce false alarms, thereby improving the effectiveness of railway security measures.

Nature and Extent of Electronic Security Surveillance Systems in Secondary School Safety in Kenya

Secondary safety is paramount to the sustainability of school performance; however, threats leveled against schools globally are interfering with safety. Schools in Kenya have experienced a horde of threats such as fire and theft coupled with radicalization. There is hardly the use of electronic security surveillance systems to mitigate threats. In Trans Nzoia County, secondary schools have been a recipient of security threats threatening the safety of the school community. Against this backdrop, this study assessed the influence of electronic security surveillance systems on the sustainability of secondary school safety in Trans Nzoia County, Kenya. The specific objective of the study was to establish the nature and extent of electronic security surveillance systems in secondary schools in Kenya. The descriptive survey research design was used to study electronic security surveillance systems in secondary school safety in Trans Nzoia County. The study population was composed of 14 secondary schools that had previous security threats and had installed electronic security surveillance systems (unit of analysis). The sampling strategy in this study was simple random and purposive sampling. Quantitative data was analyzed descriptively and inferentially with the aid of SPSS version 25. The data was analyzed at a 0.05 probability level and presented in tables and figures in percentages and frequencies. Results: CCTV was installed in all the secondary schools surveyed sampled. It was agreeable by the respondents (76.9%) that the nature and extent of electronic security surveillance systems in secondary schools promoted safety in the school community. The interaction between the electronic security surveillance systems installed in schools and the implementation of national ICT policy and school safety policy had a 29.9% impact on promoting safety in secondary schools (R2 = 0.299, (F (2, 75) = 15.966, p = 0.000). About 80.8% of the respondents agreed that electronic security surveillance systems effectively promote safety in secondary schools.

Drone Safety and Security Surveillance System (D4S)

Drones offer significant safety and security advantages by enhancing situational awareness across various fields. However, realizing these benefits hinges on well-designed drone systems. This study builds upon previous research on drone deployment challenges and proposes the Drone Safety and Security Surveillance System (D4S). D4S aims to standardize similar drone-based systems, enhancing situational awareness and supporting decision-making processes. While initially tailored for safety and security, D4S holds potential for broader applications. Two system architectures have been proposed and evaluated with positive feedback from safety and security professionals. D4S has the potential to revolutionize safety practices, improve situational awareness, and facilitate timely decision making in critical scenarios.

Video Processing Based Tracking and Vehicle Identification

Video surveillance and analysis have become integral components of various domains such as security, traffic management, and urban planning. However, effective tracking and identification of vehicles in video streams remain challenging due to environmental factors, occlusions, and complex motion patterns. This research proposes a novel approach leveraging YOLOv5, a state-of-the-art object detection algorithm, for real-time vehicle tracking and identification. By integrating YOLOv5 with advanced video processing techniques, including preprocessing for enhancing video quality and Kalman filtering for object tracking, the proposed system achieves improved accuracy and robustness in diverse scenarios. Experimental results demonstrate the effectiveness of the approach, showcasing high accuracy in vehicle tracking and reliable identification performance. The findings suggest significant potential for practical applications in enhancing video surveillance systems for better security and traffic management. Additionally, avenues for future research are discussed to further enhance the capabilities of video-based vehicle tracking and identification systems

Synergizing IoT-Based Gender-Responsive Security Surveillance to Gauge ICT Infrastructure Maturity

Effective planning and implementation of smart security surveillance ensures campus protection for all genders. Thus, the integration of a reliable ICT infrastructure as the core foundation for the security and privacy of the institutions leads to a substantial transformation in the way individuals interact, increasing the confidentiality of the stakeholders. Hence, the study aims to assess the current state of ICT infrastructure and identify the parameters in the design of an IoT-based gender-responsive security surveillance system for Colegio de San Juan de Letran Manaoag. The study implemented a comprehensive review of related literature about maturity models and their capabilities. A focus group discussion was assembled to administer a semi-structured interview on the current state of the ICT infrastructure and conducted an online survey for the assessment of the ICT infrastructure among the administrators and staff of the MIS Department in identifying the strengths and weaknesses of the current ICT infrastructure. Employing the IT Infrastructure Maturity Model (ITI-MM) as a tool in determining the maturity levels of its capabilities. Based on the results, the ITI-MM model determined the current state of the ICT infrastructure, which is currently operating at maturity level 2, and identified the gaps in achieving the requirements between Level 1 and Level 2. In conclusion, the study indicated that measuring of the maturity level is applicable in integrating the proposed system for developing an IoT-based gender-responsive security surveillance system to enhance the security measures of the institution.

Face anti-spoofing with cross-stage relation enhancement and spoof material perception

Face Anti-Spoofing (FAS) seeks to protect face recognition systems from spoofing attacks, which is applied extensively in scenarios such as access control, electronic payment, and security surveillance systems. Face anti-spoofing requires the integration of local details and global semantic information. Existing CNN-based methods rely on small stride or image patch-based feature extraction structures, which struggle to capture spatial and cross-layer feature correlations effectively. Meanwhile, Transformer-based methods have limitations in extracting discriminative detailed features. To address the aforementioned issues, we introduce a multi-stage CNN-Transformer-based framework, which extracts local features through the convolutional layer and long-distance feature relationships via self-attention. Based on this, we proposed a cross-attention multi-stage feature fusion, employing semantically high-stage features to query task-relevant features in low-stage features for further cross-stage feature fusion. To enhance the discrimination of local features for subtle differences, we design pixel-wise material classification supervision and add a auxiliary branch in the intermediate layers of the model. Moreover, to address the limitations of a single acquisition environment and scarcity of acquisition devices in the existing Near-Infrared dataset, we create a large-scale Near-Infrared Face Anti-Spoofing dataset with 380k pictures of 1040 identities. The proposed method could achieve the state-of-the-art in OULU-NPU and our proposed Near-Infrared dataset at just 1.3GFlops and 3.2M parameter numbers, which demonstrate the effective of the proposed method.

Video Anomaly Detection in Crime Analysis using Deep learning Architecture- A survey

The importance of developing automated video surveillance systems for public safety and security, particularly in crime analysis, has witnessed significant growth in recent years. This survey delves into the current landscape of automated video surveillance systems, emphasizing advancements in crime analysis and exploring existing methodologies and technologies. The study underscores the significance of employing deep learning models in video analysis. Furthermore, the study suggests a deep learning architecture to address the challenges of the existing methods. The goal of the suggested approach is to help security and law enforcement organizations quickly react to any dangers by precisely identifying unusual occurrences or actions in video sequences. The DenseNet-121 architecture is used for efficient spatial and temporal data acquisition from the video frames. This architecture is characterized by a dense connection structure in which all levels get feature mappings from all layers before them. The characteristics of DenseNet-121 can help in the accurate identification of anomalies in video streams and differentiate between normal and abnormal actions. In addition, the study also delves into the topic of using a cell structure with varied sizes to effectively split video sequences. This allows for flexible analysis and can accommodate different sorts of abnormalities. Anomaly detection accuracy can be further improved by adding size, motion, and location information to prediction and measurement models. This study serves as a foundation for the future research that aims to develop a more robust and efficient automated video surveillance solutions.

Motion Detection and Alarm Notification System: A Machine Learning Approach

This research proposes an automated safety system using Python for motion detection. Motion detection involves identifying any movement in front of a camera. OpenCV provides various methods for object detection and counting, but their performance may be insufficient for certain applications. This research utilizes computer vision and machine learning techniques to enhance motion detection and automatically capture visual data. Computer vision and image processing research are rapidly advancing to improve human lives. In this research, the system acts like a home-based surveillance security system. When unexpected motion is detected, it takes a snapshot, notifies the user, and sends an alert to their smart devices.

Advanced Surveillance System with Encrypted Alerts and Privacy Enhancement

The proposed project "Advanced Surveillance System with Encrypted Alerts and Privacy Enhancement" addresses the critical need to increase public safety through a sophisticated security surveillance system. This multi-faceted system integrates core functions including real-time object detection, encrypted image capture, secure email alerts and comprehensive security measures. Different algorithms are used for face detection, which allows the system to continuously evolve and improve its object detection capabilities and adapt to real-world scenarios. The primary goal is to identify specific objects in public spaces and thus contribute to safer communities. The project further prioritizes the protection of captured and transmitted data, implementing strict security measures to protect against attempts to hack email accounts and decrypt images. This project represents a significant leap forward in security and surveillance, offering a versatile and adaptable solution designed to effectively address evolving public safety challenges.. Keywords — Encryption, Decryption, Cryptography, Object Detection, RNS, Machine learning algorithms.

Marburg virus in Tanzania: examining emergence, consequences, and mitigation approaches

The first-ever Marburg virus disease (MVD) outbreak in Tanzania serves as a stark warning of the vulnerability of the entire world to newly emerging infectious diseases. This article looks at the causes of Tanzania’s susceptibility to MVD, containment efforts, and strategies to stop further outbreaks. The analysis emphasizes the significance of addressing socio-economic variables, environmental issues, and community participation in addition to healthcare infrastructure and surveillance systems for long-term health security. It highlights the requirement for a concerted effort on the part of governments, international organizations, and local communities, as well as ongoing financial support for public health infrastructure. The opinion also urges regional collaboration and coordination, as well as the adoption of a learning attitude to incorporate lessons from this and other outbreaks for enhancing and perfecting public health policies. We can contribute to global efforts to prevent and limit future infectious disease outbreaks and protect the health of communities around the world by adopting a thorough, proactive, and evidence-based strategy.

Computationally intelligent real-time security surveillance system in the education sector using deep learning.

Real-time security surveillance and identity matching using face detection and recognition are central research areas within computer vision. The classical facial detection techniques include Haar-like, MTCNN, AdaBoost, and others. These techniques employ template matching and geometric facial features for detecting faces, striving for a balance between detection time and accuracy. To address this issue, the current research presents an enhanced FaceNet network. The RetinaFace is employed to perform expeditious face detection and alignment. Subsequently, FaceNet, with an improved loss function is used to achieve face verification and recognition with high accuracy. The presented work involves a comparative evaluation of the proposed network framework against both traditional and deep learning techniques in terms of face detection and recognition performance. The experimental findings demonstrate that an enhanced FaceNet can successfully meet the real-time facial recognition requirements, and the accuracy of face recognition is 99.86% which fulfills the actual requirement. Consequently, the proposed solution holds significant potential for applications in face detection and recognition within the education sector for real-time security surveillance.

Automatic Face Recognition System Using Deep Convolutional Mixer Architecture and AdaBoost Classifier

In recent years, advances in deep learning (DL) techniques for video analysis have developed to solve the problem of real-time processing. Automated face recognition in the runtime environment has become necessary in video surveillance systems for urban security. This is a difficult task due to face occlusion, which makes it hard to capture effective features. Existing work focuses on improving performance while ignoring issues like a small dataset, high computational complexity, and a lack of lightweight and efficient feature descriptors. In this paper, face recognition (FR) using a Convolutional mixer (AFR-Conv) algorithm is developed to handle face occlusion problems. A novel AFR-Conv architecture is designed by assigning priority-based weight to the different face patches along with residual connections and an AdaBoost classifier for automatically recognizing human faces. The AFR-Conv also leverages the strengths of pre-trained CNNs by extracting features using ResNet-50, Inception-v3, and DenseNet-161. The AdaBoost classifier combines these features’ weighted votes to predict labels for testing images. To develop this system, we use the data augmentation method to enhance the number of datasets using human face images. The AFR-Conv method is then used to extract robust features from images. Finally, to recognize human identity, an AdaBoost classifier is utilized. For the training and evaluation of the AFR-Conv model, a set of face images is collected from online data sources. The experimental results of the AFR-Conv approach are presented in terms of precision (PR), recall (RE), detection accuracy (DA), and F1-score metrics. Particularly, the proposed approach attains 95.5% PR, 97.6% RE, 97.5% DA, and 98.5% of F1-score on 8500 face images. The experimental results show that our proposed scheme outperforms advanced methods for face classification.

THE USE AND IMPACT OF ICT AMONG STUDENTS AND TEACHERS:A CASE STUDY WITH REFERENCE TO MILAGRES COLLEGE MANGALURU

Purpose: Realizing the modern trends of ICT, Milagres College Mangalore has initiated and installed all possible ICT related facilities. The college has made extra efforts to provide the ICT facilities for UG students, staff and faculty members to promote and develop the teaching-learning process. Therefore, an attempt is made to take up this topic as a case study to analyze the impact of ICT and its contribution on Education at Milagres College, Mangaluru, D.K, and India. Methodology: The fundamental facts are gathered from primary and secondary sources. Structured surveys are used to gather primary data, and books, articles, and websites are used to collect secondary data. The respondents are chosen using the convenience sample approach. The studys sample size is 50, and the participants are staff and students of Milagres College in Mangalore. Findings: The results of the study showed that the integration of ICT has contributed in creating better atmosphere of teaching learning process. As a result, it has helped teachers to be more engaged, productive,interactive and dynamic in learning teaching environments at Milagres College. The college has provided Internet facilities to every class room, WI -fi on every floor, LCD projectors in every class room. Facilities such as well-set Computer Lab with internet facility, E-Library facility in the reading room and NLIST and Easylib Library Management Softwares, Milagres Learning Management System (MLMS), Enterprise Resource Planning (ERP) Education Administration Management, E-Content recording and publishing, Power Point Presentation, YouTube Videos, CCTV surveillance security system, Biometric Attendance System, Online and digital Payment System, Social Media Platforms, Intercom communication system, Google Meet, Zoom and WebEx online classrooms platforms etc. are been used for teaching learning process and administrative purposes. Originality: This case study discusses the management initiatives that have facilitated the college beyond the campus, through online teaching learning initiatives and analyses the impact of these efforts on knowledge dissemination and a teacher productivity (Eengagement) Paper type: Case study KEY WORDS: ICT, LMS, E-Library, Easylib, ERP, Teaching Learning Process, Resource, Perception, Communication, Promotion, Management, Engagement.

Enhancing Cybersecurity: Smart Intrusion Detection in File Server SYSTEMS

System security is a major challenge worldwide, which has led to the increasing implementation of security surveillance systems in the public and private sectors. Likewise, it is inevitable to secure server-based systems that store vast amounts of sensitive data that is accessed from time to time. Intrusion Detection Systems (IDS) use metrics to detect anomalous activity on computers and computer networks. Modern detection algorithms try to reach detection metrics by acting as an antivirus. This is not enough, the need to explore more controlled, porous, and more secure systems is inevitable, hence this research. Therefore, this study’s main objective is to develop smart intrusion detection systems for file servers and client machines that can be used within any networked environment. A qualitative research methodology was employed in the study. The sources of information included four databases: SpringerOpen, EBSCO, Google Scholar, and Direct Science. The key findings of the study are that cyber-attacks and threats are increasing, and new strategies are needed to handle them because the current intrusion detection systems experience challenges and are unable to detect malware. Intrusion detection systems are the next-generation protection, which offers the visibility to identify advanced threats within legitimate content, even authorized applications and trusted sources. Organizations are recommended to implement smart IDSs in front of file server systems and behind the firewall to ensure all malware is filtered.

Intelligent Recognition of Smoking and Calling Behaviors for Safety Surveillance

Smoking and calling are two typical behaviors involved in public and industrial safety that usually need to be strictly monitored and even prohibited on many occasions. To resolve the problems of missed detection and false detection in the existing traditional and deep-learning-based behavior-recognition methods, an intelligent recognition method using a multi-task YOLOv4 (MT-YOLOv4) network combined with behavioral priors is proposed. The original YOLOv4 is taken as the baseline network to be improved in the proposed method. Firstly, a K-means++ algorithm is used to re-cluster and optimize the anchor boxes, which are a set of predefined bounding boxes to capture the scale and aspect ratio of specific objects. Then, the network is divided into two branches with the same blocks but independent tasks after the shared feature extraction layer of CSPDarknet-53, i.e., the behavior-detection branch and the object-detection branch, which predict the behaviors and their related objects respectively from the input image or video frame. Finally, according to the preliminary predicted results of the two branches, comprehensive reasoning rules are established to obtain the final behavior-recognition result. A dataset on smoking and calling detection is constructed for training and testing, and the experimental results indicate that the proposed method has a 6.2% improvement in recall and a 2.4% improvement in F1 score at the cost of a slight loss in precision compared to the baseline method; the proposed method achieved the best performance among the compared methods. It can be deployed to related security surveillance systems for unsafe-behavior monitoring and early-warning management in practical scenarios.

Comparative assessment of back propagation neural network and self organising feature map for iris-based access control system

Iris recognition is considered as one of the best biometric methods used for human identification and verification because of its unique features that differ from one person to another. Self-Organizing Feature Map (SOFM) and Back Propagation Neural Network (BPNN) are two techniques that have been used previously for iris recognition but their performance comparison has not been adequately investigated. This research, therefore, carried out a comparative analysis between SOFM and BPNN in iris recognition. Three hundred (300) iris images from one hundred and fifty (150) subjects were acquired from Covenant University Iris dataset. The acquired iris images were pre-processed by segmenting the iris portion using Hough Transform and were normalized using Daugman’s Rubber Sheet Model. Local Binary Pattern was used for feature extraction and dimension reduction of the iris images. Each of the two algorithms, SOFM and BPNN was used individually as iris image classifier, this technique was implemented using MATLAB (R2016a). The performance of the two iris classifiers was evaluated individually and compared at 0.75 threshold value based on Recognition Accuracy (RA), False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), Training Time (TT) and Recognition Time (RT). The SOFM and BPNN techniques were validated by carrying out a t-test to compare the differences between the two techniques at 5% significance level. The SOFM technique gave RA, FAR, FRR, EER, TT and RT of 97.14%, 1.67%, 3.75%, 3.18%, 104.47s and 81.98s, respectively, while the corresponding values for BPNN were 94.29%, 5.00%, 6.25%, 5.91%, 116.23s and 101.77s respectively. The P value between the SOFM and BPNN techniques was 0.002. This research outcome revealed that SOFM outperformed BPNN in iris recognition system with respect to RA, FAR, FRR, EER, TT and RT. The SOFM technique could be used for more robust iris recognition system than BPNN in security surveillance systems or other related systems.

Armed attacks in educational institutions: social, psychological and informational problems of education security in Russia and abroad

Relevance. The history of armed attacks on educational institutions originated in the United States, but in recent decades this social phenomenon has spread widely to other countries, including Russia. Mass social tragedies associated with deaths of the youth cause great concern among all groups of population and require comprehensive prevention measures.The objective of the study is to identify social environmental factors that shape school shooter mentality, as well as to explore various strategies to prevent attacks in educational institutions of different levels (kindergartens, schools, universities).Methods. Armed attacks in educational institutions are the focus of our research. The material includes published research data (articles, dissertations and monographs) in English and Russian. The set of methods included generalizing conclusions published by peer investigators regarding the considered matter. Bibliographic databases (i.e. Federal State Institution “Russian State Library”, library platforms of Lomonosov Moscow State University and Peoples’ Friendship University of Russia) and scientific networks (ResearchGate, Web of Knowledge, Scopus, Google Scholar, Academia.edu, Mendeley) were used to explore research data. The findings present over 127 thousand publications regarding meta-analysis of armed attacks and mass shooting that occurred in educational institutions, as well as their social, psychological, cultural, and informational implications.Results and Discussion. The analysis of data regarding armed attacks in educational institutions demonstrates a very specific phenomenon that belongs to a specific type of crimes. Findings show that mass shootings of schoolchildren and students occur annually in the United States, apparently due to the free circulation of firearms and the cult of the guns. From 1974 to 1921 the US witnesses over 50 major armed attacks causing over a hundred deaths. In China, this problem is also substantial due to the country’s rapid and profound social changes (reforms, gender imbalance and demographic policy). Cold arms (knives, hammers) is the major weapon of assault in China, which caused deaths of at least a hundred children. In Europe, school shooting is not a critical problem, with only few cases reported by a few countries (Germany, Denmark and Finland). Russia shows no increase in the number of armed attacks using firearms or cold arms. In general, such incidents occur annually (1 to 4 violence cases) and are associated with slumped standards of living, increased aggression, social and psychological maladaptation among the population. To solve this problem, an integrated approach is needed, which could bring together technical and social aspects. The technical aspects imply broader use of video surveillance security systems and barriers, as well as regular profound inspections of school grounds (the venue and inside the school building). However, preventive measures should effectively provide for the social aspect to ensure expanded and deeper social and communicative relations within the student – administration / school teacher – parents paradigm. Taken together, these measures are bound o mitigate many factors of antisocial, aggressive, and violent behaviors in schools.Conclusion. A comprehensive analysis of Russian and foreign literature regarding armed attacks in educational institutions suggests that this is an independent psychosocial phenomenon. A system approach of the study allowed to establish a school shooter’s presumable portrait, as well as to identify typical social and personal characteristics of a shooter’s personality (i.e. social isolation; lack of individual ability to cope with life difficulties, such as study failures, conflicts at school or in the family; weak social integration at school and in other communities; decreased self-esteem and identity-related problems, in particular gender-related issues).