Security Tools Research Articles

Educational Scenario for Teaching Cyber Security Using low-cost Equipment and Open Source Software

This work presents a set of hands-on educational activities designed to teach some cyber security concepts in the classroom. The experimental configuration used an ad-hoc wireless and wired network, and a Raspberry Pi implementing a Web and an SSH server. Students were connected using their own devices (laptops or tablets). Initially the students tested DoS attacks to the Web server using various tools. Next, the students had to create SSH accounts to the server and a pair of RSA keys; using their SSH accounts, the students had to transfer their public keys to the server. Finally, students had to attack the SSH service from Kali Linux running on virtual machines in teams, each team using a different tool. The scenario was implemented in parts during a series of lessons and was positively accepted and evaluated by the students, who got familiar with a number of concepts and tools of computer networking and network security. In the end, the students informally assessed the Kali Linux SSH attack tools. Ways to assess the students qualitatively and quantitatively based on their participation are also presented. These lab exercises used a series of open source software, as well as low-cost equipment.

Perception of People regarding Term Insurance as a Financial Security Tool

Perception of People regarding Term Insurance as a Financial Security Tool

Cyber-physical attack graphs (CPAGs): Composable and scalable attack graphs for cyber-physical systems

Attack graphs are a fundamental security tool focused on depicting how multi-stage attacks can be carried out through a network to compromise specific assets and systems. While attack graphs have been widely utilised in the IT cyber domain, their use in Operational Technology (OT) environments requires new approaches able to properly model and analyse Cyber-Physical Systems (CPS). In this paper, we introduce Cyber-Physical Attack Graphs (CPAGs) as a class of attack graphs able to cover both cyber and physical aspects. CPAGs aim at extending the reach of standard attack graphs to cyber-physical networks typically observed in industrial environments and critical infrastructure systems, analyse how an attacker can move within the network, and understand the impact that these actions may have on the system. We propose a constructive methodology to design CPAGs backed up by a formal rule-based approach that specifies how integral parts of the model can be generated and later composed to build more complex CPAGs. We then explore the semantics of CPAGs associated to cyber and physical attack actions as well as their impact on CPS environments. We also discuss potential CPAG-based analysis techniques and focus on risk analysis using Bayesian CPAGs. Finally, we show the application of the proposed model over a realistic scenario on smart farming using our open source tool T-CITY.

Working beyond health: roles of global health security agenda, experiences of Thailand’s chairmanship amidst of the uncertainty of the COVID-19 pandemic

ABSTRACT The Global Health Security Agenda (GHSA) aims to achieve the vision of a world safe and secure from global health threats posed by infectious diseases through advancing a multi-sectoral approach. Thailand played a prominent role as the Chair of the GHSA Steering Group in 2021, guided by the Chair’s Declaration approved by the Cabinet of Thailand in 2020. In 2021, country efforts were needed to attain the right balance between preparing for future pandemics while responding to COVID-19. This paper details actions and achievements with respect to preparedness and enhancing country’s capacities recommended by the GHSA 2021, which include, among others, garnering high-level support, developing, utilisation of global health security tools, and advocating for sustainable financing. With the continued coordination and collaboration of the Action Packages and translating the Strategic Communications Plan into operation, the GHSA 2024 Target (of measured improvement of health security capacities according to the relevant health security assessments in more than 100 countries) is expected to be achieved by 2024. The unique roles and contributions of the diverse multi-sectoral partners are needed to bridge the gaps and better prepare the world to cope for future pandemics. GHSA not only promotes global preparedness and response, but also complements national preparedness and response. With Thailand’s experience, the GHSA mechanism has been continuously supporting GHSA subnational level activities in border areas. The main outcome of these discussions was the sub-national strategic plan with a multi-sectoral approach to detection, preparation and response.

Study on terahertz spectrum variation of inner package with different wrapped objects

Terahertz (THz) nondestructive testing has emerged as a promising tool for public security check due to its superior detection accuracy and sensitivity for hidden objects under clothing. However, it is unknown if material composition and textile structure of clothing will influence the THz spectrum of the hidden objects. In this paper, the THz time domain spectroscopy (THz-TDS) was used to detect several natural and artificial fabrics containing pure amino acids with characteristic absorption peaks between 0.2 and 1.6 THz. The results show that the absorption peaks of AAs of various cloth materials have different degrees of decrease and redshift. In addition, the absorption peaks of the THz spectrum disappear when the textile structure is destroyed. Moreover, the simulation results also verify that the textile structure has an obvious effect on the THz field distribution. In this sense, it is necessary to analyze the material composition and textile structure when detecting hidden objects under clothing using the THz signal.

Research on Supervision System of Power Safety Tools and Equipment Based on Internet of Things Technology

A power-system protection device built using Internet-of-Things (IoT) technologies in an intelligent environment. IoT supports electrical and physical parameters monitoring. One of the characteristics that must be checked is electricity usage from electronic gadgets. It is a complex problem to design energy-efficient IoT methods. IoT gets more complicated because of its vast size, and current wireless sensor network approaches cannot be used directly to IoT. Information gathering on the area is monitored by intelligent cellular terminals, intelligent security tools, and other multi-source sensing equipment. That is the foundation for the combined analysis and evaluation of security risk extensive data by cloud computing and edge computing. The IoT-based Power safety tools management (IoT-PSTM) system has been developed to integrate it into intelligent settings, such as smart homes or smart cities, to safeguard electrical equipment. It is meant to increase power security by quickly disconnecting in failure events such as leaking current. The system allows for real-time monitoring and alerting of events using a sophisticated data-concentration architecture communication interface. The goal is to progress and merge several technologies technically and integrate them into a personal safety system to increase security, preserve their availability, eliminate mistakes, and reduce the time required for scheduled or ad hoc interventions. Real-time data transmission, instant data processing from diverse sources, local intelligence in low-power embedded systems, interaction with many on-site users, sophisticated user interfaces, portability, and wearability are the main difficulties for the research project. This article offers a comprehensive explanation of the design and execution of the proposed system and the test findings. The results denote the higher performance of the suggested IoT-PSTM system with IoT module and enhanced performance of 94.7%.

Survey of Distributed and Decentralized IoT Securities: Approaches Using Deep Learning and Blockchain Technology

The Internet of Things (IoT) continues to attract attention in the context of computational resource growth. Various disciplines and fields have begun to employ IoT integration technologies in order to enable smart applications. The main difficulty in supporting industrial development in this scenario involves potential risk or malicious activities occurring in the network. However, there are tensions that are difficult to overcome at this stage in the development of IoT technology. In this situation, the future of security architecture development will involve enabling automatic and smart protection systems. Due to the vulnerability of current IoT devices, it is insufficient to ensure system security by implementing only traditional security tools such as encryption and access control. Deep learning and blockchain technology has now become crucial, as it provides distinct and secure approaches to IoT network security. The aim of this survey paper is to elaborate on the application of deep learning and blockchain technology in the IoT to ensure secure utility. We first provide an introduction to the IoT, deep learning, and blockchain technology, as well as a discussion of their respective security features. We then outline the main obstacles and problems of trusted IoT and how blockchain and deep learning may be able to help. Next, we present the future challenges in integrating deep learning and blockchain technology into the IoT. Finally, as a demonstration of the value of blockchain in establishing trust, we provide a comparison between conventional trust management methods and those based on blockchain.

IoTFlowGenerator: Crafting Synthetic IoT Device Traffic Flows for Cyber Deception

Over the years, honeypots emerged as an important security tool to understand attacker intent and deceive attackers to spend time and resources. Recently, honeypots are being deployed for Internet of things (IoT) devices to lure attackers, and learn their behavior. However, most of the existing IoT honeypots, even the high interaction ones, are easily detected by an attacker who can observe honeypot traffic due to lack of real network traffic originating from the honeypot. This implies that, to build better honeypots and enhance cyber deception capabilities, IoT honeypots need to generate realistic network traffic flows.
 To achieve this goal, we propose a novel deep learning based approach for generating traffic flows that mimic real network traffic due to user and IoT device interactions.A key technical challenge that our approach overcomes is scarcity of device-specific IoT traffic data to effectively train a generator.We address this challenge by leveraging a core generative adversarial learning algorithm for sequences along with domain specific knowledge common to IoT devices.Through an extensive experimental evaluation with 18 IoT devices, we demonstrate that the proposed synthetic IoT traffic generation tool significantly outperforms state of the art sequence and packet generators in remaining indistinguishable from real traffic even to an adaptive attacker.

An intrusion detection method based on granular autoencoders

Intrusion detection systems have become one of the important tools for network security due to the frequent attacks brought about by the explosive growth of network traffic. Autoencoder is an unsupervised learning model with a neural network structure. It has a powerful feature learning capability and is effective in intrusion detection. However, its network construction suffers from overfitting and gradient disappearance problems. Traditional granular computing methods have advantages in solving such problems, but the process is relatively complex, the granularity dimension is high, and the computational cost is large, which is not suitable for application in intrusion detection systems. To address these problems, we propose a novel autoencoder: Granular AutoEncoders (GAE). The granulation reference set is constructed by random sampling. The granulation of training samples is based on single-feature similarity in a reference set to form granules. The granulation of multiple features results in granular vectors. Some operations of granules are defined. Furthermore, we propose some granular measures, including granular norms and granular loss functions. The GAE is further applied to the field of intrusion detection by designing an anomaly detection algorithm based on the GAE. The algorithm determines whether the network flows are anomalous by comparing the difference between an input granular vector and its output granular vector that is reconstructed by the GAE. Finally, some experiments are conducted using an intrusion detection dataset, comparing multiple metrics in terms of precision, recall, and F1-Score. The experimental results validate the correctness and effectiveness of the intrusion detection method based on GAE. And contrast experiments show that the proposed method has stronger ability for detecting anomalies than the correlation algorithms.

Security Attack on Remote Sensing Equipment: PoIs Recognition Based on HW with Bi-LSTM Attention

<p>Deep learning is an influencer in hardware security applications, which grows up to be an essential tool in hardware security, threats the confidentiality, integrity, and availability of remote sensing equipment. Comparing to traditional physical attack, not only it can greatly reduce the workload of manual selection of POIs (Points of Interests) in security attack and Trojan backdoor, but also replenishes the toolbox for attacking. On account of minute changes between network structure model and hyperparameters constantly affecting the training and attacking effect, literally, deep learning serves as a tool but not key role in hardware security attack, which means it cannot completely replace template attack and other traditional energy attack methods. In this study, we present a method using Bi-LSTM Attention mechanism to focus on the POIs related to Hamming Weight at the last round s-box output. Firstly, it can increase attacking effect and decrease guessing entropy, where attacking FPGA data demonstrates the efficiency of attacking. Secondly, it is different from the traditional template attack and deep learning attack without preprocessing subjecting to raw traces but provides attentional POIs which is the same with artificial selection. Finally, it provides a solution for attacking encrypting equipment running in parallel.</p> <p> </p>

Security Concerns and Solutions for Enterprise Cloud Computing Applications

Computing in the cloud is now one of the most interesting developments in technology owing to the fact that it is both flexible and cost-effective. Despite the enormous stakes, the implementation of cloud computing into an existing business model creates significant safety risks. Enterprises need to foresee and account for possible risks, threats, vulnerabilities, and mitigation strategies before using cloud computing. The concept of cloud computing may be simplified by dividing it into three separate models: "Infrastructure as a Service," "Software as a Service," and "Platform as a Service." It also talks about the current crop of cloud-based security tools. Before using this technology, we think businesses should assess their security risks, threats, and existing defences. We’ve also discussed the advantages and drawbacks of cloud computing as well as places where it might be used for information risk management.Enterprise cloud computing

REANALYSIS SYSTEM - An Exam’s Result Analysis System

Abstract: The REANALYSIS SYSTEM is an innovative tool that addresses the challenges associated with manual analysis of exam results. This desktop-based application is designed to automate the analysis process, making it more efficient and less prone to errors. With a modern user interface and Telegram bot integration, the REANALYSIS SYSTEM is user-friendly and easy to use. One of the key advantages of the REANALYSIS SYSTEM is that it can complete the analysis process in just 20-30 seconds for a class of 100 students. This is a significant improvement over the 2-3 hours it would take to do the same task manually. This feature makes it a time-saving tool that allows teachers to focus on other important tasks. The REANALYSIS SYSTEM is also designed to reduce the occurrence of errors that commonly arise when performing mathematical calculations and statistical analysis manually. This is especially important in large classes, where the number of calculations involved can be overwhelming. The automation process of the system ensures that every calculation is accurate and error-free. Another notable feature of the REANALYSIS SYSTEM is its ability to generate password-protected reports. This is particularly useful when it comes to maintaining the confidentiality of exam result’s report. Teachers can rest assured that only authorized individuals will have access to the reports. The REANALYSIS SYSTEM is an efficient and reliable tool that simplifies the analysis process of exam results. Its modern user interface, Telegram bot integration, and password-protected reports make it a user-friendly and secure tool. It saves time, reduces errors, and ensures accuracy, making it an essential tool for teachers and educators.

A Web Base E-Waste Management and Data Security System

With the introduction and expansion of ICT, there has been an increase in e-waste and the safety of information in e-waste. An empirical study was conducted in Benin City using a well-designed questionnaire distributed among university college students in higher education, telecommunication providers, civil servants, computer dealers, computer technicians, and e-waste collectors to suggest a framework for e-waste management and an information security tool. An average of four hundred respondents have been surveyed. The data were analyzed using SPSS, and the findings show that the human eye is inadequate for e-waste management and information security tools. The findings also revealed a well-known horrible way of thinking closer to the safety of statistics in damaged systems, owing primarily to a lack of information and the inconveniences associated with it. To address this, a framework and a functional online net website have been improved, which are based on reputation and sensitization of e-waste management and information security. This tool has been suggested as a proper e-waste management and information security tool.

Rancang Bangun Smart Home System Pada Rumah Yang Terintegrasi Smartphone

In the 4.0 revolution era, technological developments are developing very fast. The security system is also growing, so that it can be implemented in various needs. One of them is a smart home system that can be monitored without limits using a smartphone. Currently CCTV is one of the security tools that are widely used at home, office, and others. There are also many CCTVs available that are digital, so the images are clearer and can be connected directly to the internet. The internet used also does not require a connection that is too fast but must be stable. Based on the results of the research that has been done, the home security system can use digital CCTV connected to the internet. CCTV that is connected to the internet can be monitored using a smartphone anytime and anywhere. The storage capacity used is 32 Gb and can store recordings for 9 days non-stop. The disadvantage of this system is that if the internet is unstable, playback will be interrupted, because the playback system used is directly accessed from CCTV

Intrusion Prevention System for Website Attacks

Personal computers and computer networks have become increasingly vulnerable to numerous types of attacks since the introduction of the Internet. Information has evolved into a valuable asset that must be pro- tected from cyber-attacks. Privacy may be violated, and vital data may be destroyed as a result of the attack. Typically, the attacks are brought on by a failure to put security rules in place and to use easily available security tools Firewall, Intrusion Detection System, and Intrusion Prevention Systems are some of the security solutions that are accessible. Each tool comes with its own set of features. its characteristics, benefits, and drawbacks

HARDWARE SECURITY OVERVIEW OF FOG COMPUTING END DEVICES IN THE INTERNET OF THINGS

The subject of the study is possible means of increasing the hardware security of end devices of fog computing in Internet of Things (IoT) networks, the spread of which is growing rapidly every year and requires a high level of of protection against all types of attacks. The goal of the work is to review available COTS (commercial off-the-shelf) and/or conceptual hardware solutions for protecting low-end devices in Internet of Things networks based on fog technologies. To achieve the goal, the following tasks were solved: the concept of fog computing and the advantages it will bring to IoT networks are presented; cyber threats and hardware attacks on IoT networks are considered; the consequences of using IoT networks based on fog computing are presented; hardware security tools such as TRM, PUF, HSM, etc. are considered. When performing the tasks, such research methods were used as: theoretical analysis of literary sources; comparative analysis of cloud, fog and mobile computing; analysis of existing security hardware. The following results were obtained: fog computing provides most of the advantages of cloud computing by additionally allowing data to be processed on end devices without burdening the central server. Conclusions: hardware security in IoT systems is no less important than software security. This issue is especially important for systems based on fog computing, where data will be processed on the periphery, without being transferred to the cloud. To increase the level of hardware security of fog computing devices, it is suggested to use standard hardware security platforms, such as: Physically Unclonable Functions, Hardware Security Module, System On a Chip, etc. The hardware components of the system using fog computing are less prone to cyber-attacks/hacking/intrusions/manipulation.

Suspicious Link Detection using AI

With the increase in internet usage, cybersecurity has become a major concern for computer systems, as malicious URLs can release different forms of malware and attempt to collect user data. The global lockdown in 2020 led to a significant rise in the use of internet services for business, which in turn resulted in a surge of cybercrimes committed by cybercriminals and significant data losses for businesses. To prevent such attacks, it is important to identify malicious URLs and understand the types of threats they pose. Signature-based approaches are often used to find such websites, and security tools are deployed to impose access restrictions on them. This chapter proposes using the linguistic aspects of related URLs to enhance the effectiveness of classifiers for identifying dangerous websites through Machine Learning algorithms such as Logistic Regression and Random Forest Technique. The study shows that being able to identify spam URLs solely based on URLs and categorizing them without relying on page content can lead to significant resource savings and a safer browsing experience for users.

Networking and Data Communication

The method may efficiently find rogue nodes by inspecting network traffic in real time. Our system is capable of creating a dynamic blacklist of Network nodes that may be used to block nodes whose trust levels are below a certain minimum. The detection of malicious nodes and the computation of node trust levels are handled by a central server. To increase the network and infrastructure's security, the method may be used in conjunction with other security tools including database activity monitoring, white listing, and data loss prevention. Session based encrypt is used to secure the node from the compromised attacker. Attacker request will be accepted by unique key of individual node validation process. Intruders wouldn't be able to steal TOP-SECRET data or information if this were done.. [1]

Elements of the mechanism of legal support of the social function of the state

The article analyzes the specifics of the legal provision of the social function of the state as a phenomenon. Its content and essence are detailed. It is noted that the study of the mechanism of legal support of the social function of the state is impossible without determining its structural elements. The list of elements of the mechanism of legal support of the social function of the state is highlighted. Normative and legal provision of the social function of the state, which is carried out thanks to the creation of legal norms and relevant normative and legal acts in the social sphere, which regulate the behavior of the subjects of legal relations. Subjects of provision of the social function of the state It is worth noting that the number of such subjects is determined by the variability of the areas of activity, tasks and scope of powers provided for in the legislation. Legal instruments for ensuring the social function of the state. Security tools are used to influence the behavior of all participants in relations to implement the social function of the state in order to establish the permissible limits of activity, prevent illegal actions and encourage compliance with the law. Legal responsibility of the subjects of ensuring the social function of the state. Without its use, all previous elements would not be interconnected, and their activity would often be chaotic and arbitrary. This makes it possible to consider the mechanism both dynamically and statically, and therefore the article highlights its own characteristic features of each of the elements and ways of their interaction with each other. The meaning of each of the elements of the mechanism of legal support of the social function of the state is clarified. Features of the elements of the mechanism of legal support of the social function of the state are characterized. The article clarified that ensuring the social function of the state is carried out for the purpose of meeting the social needs of the population, promoting social development by preventing the state from fulfilling its social obligations. The mechanism of legal protection is dependent on legal regulation and at the same time is closely related to the financial resources of the state and the modern perception of the population about a decent life. The selected elements of the mechanism of legal support cover the reflecting diversity of directions of implementation of the social function and illustrate the importance of common goals and tasks for proper interaction.

DASP: A Framework for Driving the Adoption of Software Security Practices

Implementing software security practices is a critical concern in modern software development. Industry practitioners, security tool providers, and researchers have provided standard security guidelines and sophisticated security development tools to ensure a secure software development pipeline. But despite these efforts, there continues to be an increase in the number of vulnerabilities that can be exploited by malicious hackers. There is thus an urgent need to understand why developers still introduce security vulnerabilities into their applications and to understand what can be done to motivate them to write more secure code. To understand and address this problem further, we propose DASP, a framework for diagnosing and driving the adoption of software security practices among developers. DASP was conceived by combining behavioral science theories to shape a cross-sectional interview study with 28 software practitioners. Our interviews lead to a framework that consists of a comprehensive set of 33 drivers grouped into 7 higher-level categories that represent what needs to happen or change so that the adoption of software security practices occurs. Using the DASP framework, organizations can design interventions suitable for developers' specific development contexts that will motivate them to write more secure code.