Illegal Intrusion Research Articles

Vulnerability analysis of super high-rise building security system based on Bayesian network and digital twin technology

Recent years have seen the increasing need to prevent illegal intrusions in super high-rise buildings. To improve the security system of super high-rise buildings by adding dynamic vulnerability assessments for illegal intrusion risks and rectification plan optimization capabilities, while also to increase its visualization level, a method utilizing digital twin technology and integrating Bayesian networks is proposed. To validate this method, a case study within a prominent super high-rise building in the Central Business District (CBD) of Beijing is conducted, in which the vulnerability analysis results have shown that among all the tertiary parent nodes, the coverage extent of the video surveillance system most significantly influences the vulnerability. By using the comprehensive blind spot analysis function of the established digital twin platform, thoughtful additions and angle adjustments of surveillance cameras have successfully increased the score of the security level from 84 to 90. This has proved that the method proposed in this study can provide decision support for the vulnerability assessment and improvement for the security system of super high-rise buildings.

A robust and effective 3-factor authentication protocol for smart factory in IIoT

Smart factory, as an intelligent application of industrial internet of things (IIoT), significantly enhances the efficiency of industrial processes while also reducing resource waste. In a system where security is crucial, the user authentication mechanism is essentially designed to prevent a range of security issues, including production failures caused by illegal intrusions from hackers. To verify the user’s identity and ensure data be accessed with a secure session key, a large number of authentication protocols have been provided for IIoT. However, existing newly alternatives show deficiency either in terms of superior performance or robust security. To get a better balance of security and efficiency, this paper designs a robust and effective 3-factor user authentication protocol. Then this paper shows the detailed security analyses and automated verification by the Proverif tool, where the ProVerif tool used in this paper can detect whether the eCK adversary with stronger attack ability can break the protocol, while the general ProVerif can only detect threat of the Dolev–Yao adversary with weaker attack ability to the protocol security. Subsequently, this paper presents performance comparison which indicates that the proposed protocol can be superior to those newly alternatives. Especially, the comparison results show that the computation cost consumed in the proposed protocol can be reduced by 62.2% than the average cost of all six compared alternatives. Lastly, the evaluation results on the energy consumption and the network delay indicate the practicability of proposed protocol for smart factory.

Developing a SmartRail Security System with YOLO and OpenCV

SmartRail Security System is an innovative research designed to significantly enhance security and safety measures within the station. The primary objective of this research is to implement advanced surveillance technology, revolutionizing the security infrastructure of the railway station and ultimately creating a smarter and more efficient approach to protecting the station environment completely. The SmartRail security system prioritizes key features aimed at enhancing rail station security System. It incorporates state-of-the-art missing person tracking capabilities that allow rapid identification and monitoring of individuals with previous missing records within the station premises. The system's goal is to provide real-time tracking and analysis, ensuring proactive measures are taken to prevent and respond swiftly to security threats. The purpose of the SmartRail security system is to combat illegal intrusion inside the restricted area of the railway station. The aim of this system is to create a strong monitoring system by introducing smart monitoring tools. This will strengthen the defenses against unauthorized access and bolster the overall security situation of the station comprehensively. SmartRail security systems go beyond mere surveillance; they promise to redefine safety standards within the station. This smart and efficient approach is deemed essential to ensure the security of staff and the entire station environment.

Intrusion Detection System Using Machine Learning by RNN Method

As computer networks continue to grow, network intrusions become more frequent, advanced, and volatile, making it challenging to detect them. This has led to an increase in illegal intrusions that current security tools cannot handle. NIDS is currently available and most reliable ways to monitor network traffic, identify unauthorized usage, and detect malicious attacks. NIDS can provide better visibility of network activity and detect any evidence of attacks and malicious traffic. Recent research has shown that machine learning-based NIDS, particularly with deep learning, is more effective in detecting variants of network attacks compared to traditional rule-based solutions. This proposed model that introduces novel deep learning methodologies for network intrusion detection. The model consists of three approaches: LSTM-RNN, various classifying methodology, and a hybrid Sparse autoencoder with DNN. The LSTM-RNN evaluated NSL-KDD dataset and classified as multi-attack classification. The model outperformed with Adamax optimizer in terms of accuracy, detection rate, and low false alarm rate.

Design of Intelligent Security System for Higher Vocational Colleges Based on Internet of Things

Abstract The increase in Internet of Things (IoT) technologies for security in higher vocational institutions provides new solutions to enhance campus security. The challenges faced by campus security management include break-ins, emergency response, and environmental monitoring. Effective security system design can improve the ability to deal with these challenges and ensure the safety and comfort of students and faculty. In this study, an intelligent security system based on IoT technology is designed to realize dynamic management and real-time response to campus security by integrating advanced face recognition, environmental monitoring and data analysis technologies, thus improving campus security and management efficiency. Haar feature recognition, face detection optimized by Adaboost algorithm, and ecological monitoring technologies are some of the technological approaches introduced in the study. The system design integrates multiple aspects such as video surveillance, environmental parameter monitoring, and data analysis to build an efficient and reliable security monitoring system. In this paper, the system can monitor the campus security situation in real time, the face recognition accuracy rate reaches 95%, and the illegal intrusion detection time is reduced to within 2 seconds, effectively improving the response speed and accuracy of campus security management. This study proves the effectiveness and feasibility of IoT technology in designing campus intelligent security systems, which is of great significance to improve campus security management.

Illegal Intrusion Detection of Internet of Things Based on Deep Mining Algorithm

Illegal Intrusion Detection of Internet of Things Based on Deep Mining Algorithm

Intrusion into Computer Information System: Criminal Law Rule Based on Different Judicial Recognition Perspectives of State Affairs

Cybercrime refers to a new form of crime generated with the application and prevalence of computer technology. In China, there are increasingly more instances of unauthorized access to computer systems every year, which has severely affected network security and the property security of the people, and even national security. This paper delves deeper and more methodically into the problems surrounding the criminal act of unlawful access to computer information systems. The ambiguous definition and limited reach of the criminal object make Chinas punishment for unauthorised access to computer systems less effective., the crossover of the criminal law system caused by the contradiction of legislative logic, as well as the insufficient prevention of too lenient penalties. Based on the above legislative deficiencies, it is imperative to improve the criminal law system, expand the protection scope of the first clause of Article 285 of the Criminal Law, Increase the number of cases that are investigated and prosecuted, explain computer information systems in the context of state affairs, expand the definition of state affairs computer information systems, and strictly regulate the amount of crime that is legal. Additionally, it is essential to establish an independent status for data crimes by adding provisions on data security protection concerning the three major computer information systems; increasing the intensity of punishment, raising the range of statutory penalties, and adding property penalties, thereby imposing a comprehensive regulation on illegal intrusion into computer information systems.

Enhancing Network Security: A Machine Learning-Based Approach for Detecting and Mitigating Krack and Kr00k Attacks in IEEE 802.11

The rise in internet users has brought with it the impending threat of cybercrime as the Internet of Things (IoT) increases and the introduction of 5G technologies continues to transform our digital world. It is now essential to protect communication networks from illegal intrusions to guarantee data integrity and user privacy. In this situation, machine learning techniques used in data mining have proven to be effective tools for constructing intrusion detection systems (IDS) and improving their precision. We use the well-known AWID3 dataset, a comprehensive collection of wireless network traffic, to investigate the effectiveness of machine learning in enhancing network security. Our work primarily concentrates on Krack and Kr00k attacks, which target the most recent and dangerous flaws in IEEE 802.11 protocols. Through diligent implementation, we were able to successfully identify these threats using an IDS model that is based on machine learning. Notably, the resilience of our method was demonstrated by our ensemble classifier’s astounding 99% success rate in detecting the Krack attack. The effectiveness of our suggested remedy was further demonstrated by the high accuracy rate of 96.7% displayed by our neural network-based model in recognizing instances of the Kr00k attack. Our research shows the potential for considerably boosting network security in the face of new threats by leveraging the capabilities of machine learning and a diversified dataset. Our findings open the door for stronger, more proactive security measures to protect IEEE. 802.11 networks’ integrity, resulting in a safer online environment for all users.

Intrusion Detection Model Based on Improved Transformer

This paper proposes an enhanced Transformer-based intrusion detection model to tackle the challenges of lengthy training time, inaccurate detection of overlapping classes, and poor performance in multi-class classification of current intrusion detection models. Specifically, the proposed model includes the following: (i) A data processing strategy that initially reduces the data dimension using a stacked auto-encoder to speed up training. In addition, a novel under-sampling method based on the KNN principle is introduced, along with the Borderline-SMOTE over-sampling method, for hybrid data sampling that balances the dataset while addressing the issue of low detection accuracy in overlapping data classes. (ii) An improved position encoding method for the Transformer model that effectively learns the dependencies between features by embedding the position information of features, resulting in better classification accuracy. (iii) A two-stage learning strategy in which the model first performs rough binary prediction (determining whether it is an illegal intrusion) and then inputs the prediction value and original features together for further multi-class prediction (predicting the intrusion category), addressing the issue of low accuracy in multi-class classification. Experimental results on the official NSL-KDD test set demonstrate that the proposed model achieves an accuracy of 88.7% and an F1-score of 88.2% in binary classification and an accuracy of 84.1% and an F1-score of 83.8% in multi-class classification. Compared to existing intrusion detection models, our model exhibits higher accuracy and F1-score and trains faster than other models.

Power Line Data Online Acquisition System of Distribution Network Based on ZigBee

Energy Internet promotes the vigorous development of new access network services, which requires access networks to provide support for new services quickly, flexibly, and efficiently. The extension and penetration ability of the access network has been continuously enhanced, and it can adapt to the complex operating environment of the distribution side. Because it is close to the end of the power grid, the network is exposed to the public environment, and it is vulnerable to illegal intrusion, security protection, and other issues. In this paper, based on the ZigBee network, through the study of time series and ARIMA prediction model, first of all, the original data is analyzed in time series, then the data is stabilized. The model is determined and established, and finally, the simulation model is predicted. The test proves that the model studied in this paper has high acquisition accuracy and achieves prediction analysis. Timely warning of meteorological temperature is used as a part of the source data, which can save energy and reduce the acquisition cost while ensuring the amount of data collected.

IoMT-Net: Blockchain-Integrated Unauthorized UAV Localization Using Lightweight Convolution Neural Network for Internet of Military Things

Unmanned aerial vehicle (UAV) contributes substantial strategic benefits on the Internet-of-military-things (IoMT). However, the untrusted party’s misuse of the UAV may violate the security and even demolish the critical operation in the IoMT system. In addition, data manipulation and falsification using unauthorized access are the significant challenges of the IoMT system. In response to this problem, this study proposes a blockchain integrated convolution neural network (CNN)-based intelligent framework named IoMT-Net for identification and tracking illegal UAV in the IoMT system. Blockchain technology prevents illicit access, data manipulation, and illegal intrusions, as well as stored data on the central control server (CCS). Concurrently, the proposed CNN analyzed the radio frequency signal sent by the antenna array element to determine the direction of arrival (DoA) for the localization of the illegal UAV. Therefore, a signal model is designed to processes the received signal array through IoMT-Net. Moreover, the proposed CNN model is designed with two different functional modules, such as the resource accuracy trade-off (RAT) module and the unique feature extraction and accuracy boosting (UAB) module, by adopting depth-wise and grouped convolution layers. These sparsely connected convolution layers offer high DoA estimation accuracy while maintaining the network more lightweight. In addition, the skip connection is also leveraged into the sub-units of RAT and UAB modules for sharing features and handling the vanishing gradients problem. Based on the simulation results, the proposed network achieves superior DoA estimation accuracy (approximately 97.63% accuracy at 10 dB SNR) and outperforms other state-of-the-art models.

A Robust and Effective Two-Factor Authentication (2FA) Protocol Based on ECC for Mobile Computing

The rapid development of mobile computing (e.g., mobile health, mobile payments, and smart homes) has brought great convenience to our lives. It is well-known that the security and privacy of user information from these applications and services is critical. Without the prevention provided by an authentication mechanism, safety vulnerabilities may accumulate, such as illegal intrusion access resulting in data leakage and fraudulent abuse. Luckily, the two-factor authentication (2FA) protocols can secure access and communication for mobile computing. As we understand it, existing 2FA authentication protocols weaken security in the pursuit of high efficiency. How efficiency can be achieved while preserving the protocol’s security remains a challenge. In this study, we designed a robust and effective 2FA protocol based on elliptic curve cryptography (ECC) for authentication of users and service providers. We proved the robustness (respectively, the effectiveness) of the presented protocol with the heuristic analysis and security verification provided by the ProVerif tool (respectively, with a performance comparison based on six schemes). Performance comparisons in terms of message rounds, communication, and computation overheads showed that our scheme was superior to the exiting schemes or comparable as a whole; i.e., only two rounds, 1376 bits, and 1.818 ms were required in our scheme, respectively. The evaluation results showed that the proposed 2FA protocol provides a better balance between security and availability compared to state-of-the-art protocols.

Features of the transformation of legal theory under martial law

Purpose. The aim of this work is to discuss the features of the functioning of legal theory and scientific activities under martial law. Methodology. The research is based on the analytical method, which is used to diagnose problems and generate hypotheses for their solutions. The methodology also includes critical thinking and evaluation of facts and data related to the research. The systemic and functional method was used to research the objectives of law degree education, changes in the legislation system in the conditions of war. The generalization method was used to systematize and interpret the results obtained during the research. Results. The study has provided the acknowledgement that scientific research in the field of law should be focused on the needs of our state under martial law. In such conditions, law degree education cannot be passive, but rather must significantly and dynamically influence the environment by forming a civilized democratic legal field. This is its positive role and educational mission. In view of this, the model of a legal specialist, who will meet the requirements for future legal professionals, as well as the model of their training should be clearly outlined. Scientific novelty. In the process of research, it was established that one of the priority issues of legal development in the conditions of war includes the problems of proper legal provision of national security, i.e. the state of security of man and the citizen as well as spiritual and material values of society from criminal and other illegal intrusions, emergency situations of natural and man-made nature, social conflicts within society, the risk of which increases significantly in the conditions of war. Under martial law, most elements of the rule of law are negatively affected in the form of relevant restrictions, the necessity of which is objective in nature due to the need to prevent global threats to the independence and territorial integrity of the state; to repel armed aggression; to ensure national security; to fight against real and potential threats to the rights and human freedoms. At the same time, in the context of the rule of law, the specified restrictions, firstly, should not relate to absolute (natural) human rights and freedoms, and secondly, they should be properly justified and correspond to the stated purpose of introducing martial law. Practical significance. The results of the research can be used for the methodology of scientific research.

Reliability of a Distributed Data Storage System Considering the External Impacts

With the emergence of the industrial internet of things, distributed data storage systems have become widely used to store the monitoring data of power generation systems. Malicious hackers often try to destroy or steal these confidential data by illegally invading the systems. In addition to hackers’ illegal intrusions, the availability of the data stored is also affected by the internal failures of the system.In this research, two reliability models are formulated to study the reliability of a phased-mission distributed data storage system considering internal failures and illegal intrusion. The first model considers internal failures and data destruction, whereas the second model further considers data theft. Furthermore, the allocation of the data partitions is optimized so that system reliability can be maximized. Numerical experiments demonstrate the effectiveness of the proposed models and algorithms.

The research of electrical parameters of threshold detectors

This research work provides an in-depth analysis of the fire alarm system, considered as a security tool for a variety of facilities, from industrial buildings to residential premises. Two key subsystems serve as the basis for the study: the fire alarm system, which is designed to detect and report the occurrence of fire, and the intrusion alarm system, whose task is to detect attempts at illegal intrusion. For each of these subsystems, their functions and components are investigated, their mechanism of action, principles of operation, and possible implementation options are described, depending on the specific conditions and security requirements. At the same time, emphasis is placed on three types of automatic fire alarm systems: threshold, addressable and addressable-analog, each of which has its own features, advantages and disadvantages. The article goes beyond theoretical analysis and presents the results of a practical study of the three main types of fire alarm systems: wired, wireless and addressable. The study is based on a specially designed training stand, which allows you to simulate the operation of fire alarm systems in conditions as close to reality as possible. The article demonstrates the connection schemes of detectors, explains their states in the "normal" and "alarm" modes. It is noted that the currents and voltages of alarm loops in different modes were measured. Dependency diagrams of these parameters are presented, and the alarm threshold levels are measured. The research presented in the paper provides a valuable contribution to the study and optimization of alarm and fire systems, providing meaningful data for the development and testing of these systems. The paper can be useful for fire and security professionals and those interested in improving the performance of these systems.

INDOOR RISK FACTOR MAPPING FOR LARGE-SCALE ABANDONED BUILDINGS MANAGEMENT

Large number of buildings has been stocked in Japan. Some of them are abandoned and suffering from illegal intrusion, falling of deteriorated components and possible danger of collapse. The target of this paper is one of these abandoned buildings located in hot spring area. To seek for the possible mitigation, this paper reports the results of documentational survey, on-site visual inspection survey and several management measures taken based on the newly proposed indoor risk factor mapping method. The maps were shared with the stakes holders and applied to the buildings in cooperation with several local government sections.

Real-Time Reliability Access Control Based on Rail Traffic Data Platform

With the introduction of the industrial internet, Internet of Things, and big data technology, the interconnection degree of the industrial control cloud network is getting higher and higher, the data interface needs to be gradually standardized, and there are more and more open interface components. Data-based attacks will continue to emerge. The real-time and reliability of access control are essential for trust value updating between network participants. This paper proposes a fine-grained dynamic real-time credibility access control method based on zero trust. Continuous authentication and trust evaluation should be carried out throughout the access control process. The zero-trust evaluation indicators of a rail transit data platform that conforms to the requirements of grade protection 2.0 are established. According to the risk feedback, the current trust level is dynamically updated in real time, and the results are used in the access control model. It can reject unauthorized access, reduce the occurrence of illegal intrusion data leakage and data loss events, and has great value in rail transit data security.

Commercial Fixed-Wing Drone Redirection System using GNSS Deception

As drones become more common today, the threat of reconnaissance or attack drones to core facilities has increased, and countermeasures against them have become essential. In this study, a drone redirection system was proposed to counter illegal intrusion of commercial fixed-wing drones. The drone redirection system was designed as a closed-loop system that automatically redirects the drone to a target position. The main novelty of this study is a proposal for a system that can automatically redirect a commercial fixed-wing drone, which has not been previously explored. We proposed two strategies for redirecting drones. Additionally, simple drone modeling with a path-following algorithm was used to easily model various drones. The drone model was then tuned using flight test data, and the results were compared. Simulations were performed on the designed drone redirection system model to verify the performance of the two proposed strategies for redirecting drones in conjunction with drone fail-detection and innovation check. The performance of the drone redirection system was assessed through flight tests of Remo-M and simulations of Micropilot's hardware-in-the-loop simulator (HWILS). Through simple drone modeling, drone flight tests, and the test results from HWILS, it was proved that the drone redirection system can be applied to various fixed-wing drones.

An Examination of Multi-Key Fully Homomorphic Encryption and Its Applications

With the rapid development of the Internet of Things (IoT) technology, the security problems it faces are increasingly prominent and have attracted much attention in industry and the academy. Traditional IoT architecture comes with security risks. Illegal intrusion of attackers into the network layer disrupts the availability of data. The untrusted transmission environment increases the difficulty of users sharing private data, and various outsourced computing and application requirements bring the risk of privacy leakage. Multi-key fully homomorphic encryption (MKFHE) realizes operations between ciphertexts under different key encryption and has great application potential. Since 2012, the first MKFHE scheme LTV12 has been extended from fully homomorphic encryption (FHE) and has ignited the enthusiasm of many cryptographic researchers due to its lattice-based security and quantum-resistant properties. According to its corresponding FHE scheme, the MKFHE schemes can be divided into four kinds: Gentry–Sahai–Water (GSW), number theory research unit (NTRU), Brakerski–Gentry–Vaikuntanathan (BGV), and FHE over the tour (TFHE). Efficiency and cost are urgent issues for MKFHE. New schemes are mainly improved versions of existing schemes. The improvements are mostly related to the four parts of MKFHE: security assumption, key generation, plaintext encryption, and ciphertext processing. We classified MKFHE schemes according to the improved partial schemes, and we present some improved techniques and the applications of MKFHE.

RF-Access: Barrier-Free Access Control Systems with UHF RFID

Traditional RFID-based access control systems use flap barriers to help manage pedestrian access and block unauthorized staff at any entrance, which requires visitors to swipe their cards individually and wait for the opening of the blocking body, resulting in low-frequency pedestrian access and even congestion in places with large passenger flow. This paper proposes a barrier-free access control system (RF-Access) with UHF RFID technology. The main advantage of RF-Access is that it provides non-intrusive access control by removing flap barriers and operations of swiping the card. The visitors just go across the system without any stay at the entrance. Meanwhile RF-Access performs the authentication, which greatly improves time efficiency and quality of service. RF-Access addresses two key issues of the non-intrusive access control: motion direction detection and illegal intrusion detection. In RF-Access, we first propose a dual-antenna system setup together with a time-slot-based model to monitor users’ moving directions, which is robust to different environmental factors, such as multi-path effects. Afterwards, we use a tag array to detect illegal intrusion in case attackers do not carry any RFID tags. We implement a prototype of RF-Access with commercial RFID devices. Extensive experiments show that our system can detect the moving direction with 99.83% accuracy and detect illegal intrusion with an accuracy of 96.67%.