Detection and Control of Abnormal Behavior in Controlled Network System

As society develops, the demand for security is rapidly increasing. Accordingly, there is growing interest in research on methods to detect and prevent abnormal behavior using surveillance cameras in public places and private spaces such as shopping malls and airports for human safety. Many detection techniques based on deep learning models have been researched in the field of abnormal behavior detection. However, due to the lack of labeled abnormal behavior data, there are significant difficulties in developing an effective detection system. This paper surveys methods for deep learning methods to detect abnormal human behavior in surveillance video and presents recent techniques. First, I will introduce popular datasets that have often been used in previous research. After that, we categorized the existing methods for detecting abnormal behavior using deep learning into three types: supervised learning, unsupervised learning, and partially-supervised learning. We then explained the basic concepts and advantages of each method and summarized their shortcomings. We also briefly describe future research directions based on the advantages and disadvantages of each method. Based on this, it is expected that the technology of video surveillance systems that apply abnormal behavior detection will further develop.

Information security involves many branches of effort, including information assurance, host level security, physical security, and network security. Computer network security methods and implementations are given a top-down description to permit a medically focused audience to anchor this information to their daily practice. The depth of detail of network functionality and security measures, like that of the study of human anatomy, can be highly involved. Presented at the level of major gross anatomical systems, this paper will focus on network backbone implementation and perimeter defenses, then diagnostic tools, and finally the user practices (the human element). Physical security measures, though significant, have been defined as beyond the scope of this presentation.

Security Metrics help network administrators master the security status and strengthen security management for many years. Recently, with the usages of many new techniques and network structures, the cyber attacks become complex and the security measurement has received more and more attentions. However, existing methods usually focus on one aspect of security and the indicators used are usually difficult to quantify, which makes it difficult to understand network security status in some real circumstance. In this paper, we consider the network system security from the perspective of attack and defense and the changes of external security environment to propose a comprehensive and quantifiable index system for network security measurement. We illustrate the corresponding theories and the usages of each selected indicators and we also complete the real-time security measurement in various attacks and defenses by using NS3 simulator. The simulation results verify the correctness and rationality of the proposed Security Measurement Index System.

With the rapid development of information technology and the rapid popularization of the Internet, while people enjoy the convenience and efficiency brought about by new technologies, they are also suffering from the harm caused by cyber attacks. In addition to efficiently thwarting network assaults, a high volume of complicated security event data might unintentionally increase the strain of policy makers. At present, NS threats mainly include network viruses, trojans, DOS (Denial-Of-Service), etc. For the increasingly complex Network Security (NS) problems, the traditional rule-based network monitoring technology is difficult to predict the unknown attack behavior. Environment-based, dynamic and integrated data fusion can integrate data from a macro perspective. In recent years, Machine Learning (ML) technology has developed rapidly, which could easily train, test and predict existing third-party models. It uses ML algorithms to find out the association between data rather than manually sets rules. Support vector machine is a common ML method, which can predict the security of the network well after training and testing. In order to monitor the overall security status of the entire network, NS situation awareness refers to the real-time and accurate reproduction of network attacks using the reconstruction approach. Situation awareness technology is a powerful network monitoring and security technology, but there are many problems in the existing NS technology. For example, the state of the network cannot be accurately detected, and its change rule cannot be understood. In order to effectively predict network attacks, this paper adopted a technology based on ML and data analysis, and constructed a NS situational awareness model. The results showed that the detection efficiency of the model based on ML and data analysis was 7.18% higher than that of the traditional NS state awareness model.

Abnormal crowd behavior detection has recently attracted increasing attention due to its wide applications in computer vision research areas. However, it is still an extremely challenging task due to the great variability of abnormal behavior coupled with huge ambiguity and uncertainty of video contents. To tackle these challenges, we propose a new probabilistic framework named variational abnormal behavior detection (VABD), which can detect abnormal crowd behavior in video sequences. We make three major contributions: (1) We develop a new probabilistic latent variable model that combines the strengths of the U-Net and conditional variational auto-encoder, which also are the backbone of our model; (2) We propose a motion loss based on an optical flow network to impose the motion consistency of generated video frames and input video frames; (3) We embed a Wasserstein generative adversarial network at the end of the backbone network to enhance the framework performance. VABD can accurately discriminate abnormal video frames from video sequences. Experimental results on UCSD, CUHK Avenue, IITB-Corridor, and ShanghaiTech datasets show that VABD outperforms the state-of-the-art algorithms on abnormal crowd behavior detection. Without data augmentation, our VABD achieves 72.24% in terms of AUC on IITB-Corridor, which surpasses the state-of-the-art methods by nearly 5%.

In a heterogeneous computer network, resources on each node are protected by the local security mechanism. Network services allow local resources to be accessed remotely from other nodes. Without appropriate network security measures, each node is vulnerable to attack from the network. This paper analyzes the heterogeneous Bell Labs Network (BLN) environment and lists the potential network security risks. By assessing the risks, counter-measures are proposed for minimizing potential exposure. These counter-measures are part of a global network security mechanism being implemented for BLN.

In order to effectively avoid network information leakage and computer network paralysis, and improve the ability of computer network security early warning, it is necessary to design a computer network security intelligent early warning system based on network behavior. Security warning is considered as the second defense mechanism behind the firewall. It can monitor and warn the network without affecting the network performance, so as to provide real-time protection for external attacks, internal attacks and misoperations, and improve the network security. This paper designs an intelligent early warning system for network security based on partial differential equation image matching technology. The system adopts B/S development mode, and the server and browser are located in the campus network. Data fusion technology is used to assess network security, predict potential threats, and add new intrusion features to the feature database for subsequent use. In this paper, a target matching algorithm based on partial differential equation is proposed by using partial differential equation. The algorithm calculates the phase difference through the algebraic combination of orthogonal filter outputs. For scenes with continuous disparity changes, the error matching rate of this algorithm is lower than that of Michael Bleyer algorithm and vertical constraint algorithm. In general, the disparity map generated by this algorithm has high matching accuracy. The results show that the distribution of alarm information is reasonable and in line with the actual situation.

Detecting and identifying intrusions in a network is a challenging research area in the network security domain. Intrusion detection plays an essential role in computer network security since long. An Intrusion Detection System (IDS) is mainly used to detect an unauthorized access to a computer system or network. Moreover, it is capable to detect all types of malicious and harmful attacks in a network. The drawbacks of existing IDS are it can detect only the known attacks and it produces a large number of false alarms due to the unpredictable behavior of users and networks. It also requires extensive training sets in order to characterize the normal behavior of the nodes. In order to overcome these issues, an integration of Hidden Markov Model (HMM)–Relevance Vector Machine (RVM) algorithm namely, Probabilistic Relevance Classification (PRC) is proposed to detect intrusions in Supervisory Control and Data Acquisition (SCADA) network. Here, the power system attack dataset is used to detect the attacks in an SCADA network. In the preprocessing stage, the given data is preprocessed to segregate the relays as R1, R2, R3n and R4. Each relay contains the date, timestamp, control panel log report, relay log report, snort log report, marker, fault location, and load condition information. Then, the Boyer–Moore (BM) technique is employed to perform the string matching operation. After that, the PRC technique is implemented to classify the attack as known or unknown. The novelty of this paper is it manually trains the data and features for unknown attacks. The main intention of this work is to reduce the set of features, amount of database, and to increase the detection rate. The experimental results evaluate the performance in terms of False Acceptance Rate (FAR), False Rejection Rate (FRR), Genuine Acceptance Rate (GA), sensitivity, specificity, accuracy, error rate, and recall.

Abnormal behaviour detection is crucial given the reliance on video surveillance for public safety. However, most vision-based methods fail to adapt to the variability and complexity of the crowd scenes inherent in real-world scenarios. This study introduced an abnormal crowd behaviour detection algorithm based on lightweight Graphic Neural Networks (GNNs) using trajectory similarities and spatial-temporal attributes. Integrating Graph Attention Networks (GATs) to encode the graph structure, creating latent embeddings that capture both local and global relationships within crowds. Also introducing an autoencoder (AE) structure to identify anomalies by comparing true movement with the decoded output. Anomaly is detected when the reconstruction error is high. Experiments on UCSD Pedestrians and ShanghaiTech campus datasets show the successful use of trajectory similarities as graph features for GNNs to generate logical embeddings of crowd movement and highlight the impact of time windows on anomaly detection precision as reflected in F1-scores. The Area Under the Curve (AUC) metric shows stability, highlighting the model’s balanced performance. Qualitative analysis of the ShanghaiTech campus dataset illustrates the model’s ability to align detection with visual anomalies. Overall, this study demonstrates the effectiveness of the proposed graph-based anomaly detection framework for crowd monitoring through a lightweight model suitable for CCTV.

The network has become an indispensable element in daily life and makes the connection between things closer. This paper presents a network security simulation model and discusses the basic framework and application of the model. This paper is based on the random forest network security situation awareness model. The model performs dimensionality reduction on the data through data feature analysis to highlight the characteristic attributes of data records and reduce invalid data, thereby reducing the resource overhead of the model and its dependence on network hardware device configuration. As the core of the model, the random forest algorithm enables the model to distinguish various network behaviors. Results show that the average detection rate of its network security model is 95%. Further reflects the high performance of this model.

With the widespread application of computer network technology, computer network security defense capability has become a focus of attention in various industries. The extensive use of computer information systems in various sectors has significantly improved work efficiency but has also introduced security risks and management issues. The deployment of network security detection and control systems allows for effective security monitoring and management of computer operations and real-time network information. This paper presents a computer network security detection and control system based on human-computer interaction, which enables users to handle daily key business processes. The work principles and overall architecture of the network security detection and control system are analyzed and demonstrated. It offers functions such as filtering options, address rules, network security detection, network unreachability, overall traffic analysis, subnet definition, fault diagnosis, and security analysis. Testing and analysis indicate that the systems design achieves the intended goals. In the application of network security features, it can effectively combine various forces to enhance the quality and efficiency of network security, making it widely applicable in various industry units.

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

The modern world technology is civilized, globalized and modernized. The technological development of social networks and e-commerce applications produce larger data. This data communication is major task, because device to device communication need network terminal. This data transmission is not safe because of different types of tools and software available to destroy the existing network. In the field of network security during data transfer from one particular node to other node some security vulnerability is happened this is the one of the critical issue in this sector. The reason for this network security is different types of data attacks are happen in day to day life. It is easy to establish a new network but protecting the entire network is a big issue. This network security is generally two parameter first one is communication and second one is data automation. The network security field is directly or indirectly linked with the concept of data encryption. The development in this network security has taken us to a level that from signature again we came back to thumb print. For example maintain the data secure we use the lock system which is a finger print type. This technology helps us to protect the physical data theft, but logical data theft is still problem for data transmission. This article will brief about the network security it also presents the various network security types. Those types are wired and wireless network security. Apart from the network security the following topics is also discussed in this article. Those are network security protocols and simulation tools in network security. The research problems in network security are privacy and vulnerability of data.

With the proposal of the national industrial 4.0 strategy, the integration of industrial control network and Internet technology is getting higher and higher. At the same time, the closeness of industrial control networks has been broken to a certain extent, making the problem of industrial control network security increasingly serious. S7 protocol is a private protocol of Siemens Company in Germany, which is widely used in the communication process of industrial control network. In this paper, an industrial control intrusion detection model based on S7 protocol is proposed. Traditional protocol parsing technology cannot resolve private industrial control protocols, so, this model uses deep analysis algorithm to realize the analysis of S7 data packets. At the same time, in order to overcome the complexity and portability of static white list configuration, this model dynamically builds a white list through white list self-learning algorithm. Finally, a composite intrusion detection method combining white list detection and abnormal behavior detection is used to detect anomalies. The experiment proves that the method can effectively detect the abnormal S7 protocol packet in the industrial control network.

Addressing the persistent delay problem in traditional network security antivirus protection systems, this paper introduces an innovative approach utilizing virtual reality (VR) technology. The primary objective is to significantly reduce detection delays and enhance the efficiency of network security measures. An enhanced decision-making algorithm is proposed to identify relevant features associated with network security. These features are then weighted and optimized to improve the overall detection process. An injection list is generated through web crawling techniques to strengthen security measures. A virtual protection block is also developed to serve as a barrier against potential threats. The proposed method claims a detection delay of only 75.33 milliseconds, significantly outperforming two traditional methods that recorded 290.11 milliseconds and 337.30 milliseconds, respectively. This substantial decrease in detection delay emphasizes the effectiveness of automatic detection within the context of VR technology. Practical implementation and empirical evidence further validate the success of this approach. The automatic detection of network security vulnerabilities within the VR technology framework is efficient and exhibits considerable progress. As such, this research offers a promising solution to the delay problem in network security antivirus protection. Embracing VR technology achieves shorter detection delays, ultimately improving the security posture of network systems.