Security Dangers Research Articles

Evaluating the Economic and Trade Cooperation Impact of the Belt and Road Initiative on China-Pakistan Relations

The BRI, initiated by Chinese President Xi Jinping, sounds like an ambitious scheme for the development of integration and network cooperation scheme in the Asia-Europe-Africa region. Pakistan is the closest ally of BRI since it occupies a strategic position as a transit route for exchange. The China-Pakistan Financial Passage (CPEC), a lead venture of the BRI, centers on improving financial participation and foundation improvement between the two nations. Whereas the BRI has encouraged Pakistan's framework development and expanded two-sided exchange with China, challenges such as debasement, responsibility, obligation supportability, and territorial security dangers have risen. Understanding the BRI's effect on China-Pakistan relations makes a difference in the project's openings, challenges, and potential dangers while directing decision-making and collaborative endeavors. To evaluate the BRI's impact, a combination of descriptive, regression, and comparative analyses was used, along with qualitative insights from policymakers. Data conception further illustrates trends in trade, investment, and economic growth.

A Proposed Secure Wearable Device Payment System Based on Blockchain Technology

Given that they are employed in several services, wearable technology is growing in popularity. The wristwatch gathers several types of personal information. Consumers may find gadgets convenient, but there are also security dangers that alert users to the possibility of cybersecurity breaches, device intrusions, and vulnerability-exploiting. Hacking may make the collected data visible since devices are vulnerable to assault. Issues with these devices include security flaws, Bluetooth troubles, and the absence of authentication and location tracking. The Internet of Things (IoT) includes wearable gadgets as cogs in the wheel that might have an effect on the banking and financial industries. Therefore, since blockchain technology has gained a lot of attention recently and is now one of the most popular ways to securely transfer data through decentralised peer-to-peer systems, it is imperative to employ its security capabilities on the existing IoT-based wearable payment system. A secure, decentralised transaction execution process is made possible by blockchain, an unchangeable ledger. It has a great reputation and more customers thanks to its complex yet safe technique. Based on blockchain technology, this research suggested a secure wearable device payment system.

Back to Bipolarity: How China's Rise Transformed the Balance of Power

Abstract China's rise, Russia's military resurgence, and India's economic growth have prompted debates about the end of unipolarity and the future balance of power. Such debates are a staple of international politics; indeed, in the late twentieth century, many observers warned that Japan and the Soviet Union would overtake the United States. Yet scholars and policymakers evince little agreement on how to define power or measure the distribution of power. This article introduces an inductive method for comparing national power. I empirically validate common metrics of national capabilities—economic and military—by assessing their ability to both predict known balances of power across historical systems (1820–1990) and distinguish between great powers and other countries. This method yields three important findings. First, large gaps in national capabilities between great powers and even superpowers have been common throughout history. Great powers need not achieve parity with the leading state to engage in a dangerous security competition. Second, this method shows that China on most dimensions is not only a great power but a superpower. Third, neither Russia nor India is a great power. The system is bipolar. These findings inform debates about the stability of international politics and the future of international order and suggest the likelihood of shifts in U.S. grand strategy and alliances.

Mathematical Approach towards Adaptive Machine Learning Models for Dynamic Security Threats in Industrial IoT

The always changing nature of security dangers within the Industrialization Internet of Things (IIoT) makes it exceptionally difficult for standard security strategies to work. Since of this, versatile machine learning (ML) models got to be made that can respond to unused dangers in genuine time. This study proposes the utilize of versatile machine learning procedures as a better approach to form IIoT situations more secure. To begin with, the paper addresses the most security issues that IIoT frameworks have, cantering on how the frameworks must be able to alter to address unused dangers. Cutting edge online dangers are exceedingly cleverly and alter quickly, frequently resisting conventional security measures. This appears how vital it is to discover arrangements that can alter rapidly. The proposed customizable machine learning show employments real-time information streams sent by IIoT gadgets to keep risk recognizable proof and defence strategies up to date. These models utilize strategies such as design acknowledgment, peculiarity discovery, and prescient analytics to distinguish bizarre behavior which will indicate a security breach. By learning from past information and adjusting to modern designs and patterns, the models can distinguish known and obscure dangers more precisely and rapidly. A key angle that creates this customizable machine learning models work so well is the truth that they can operate autonomously in IIoT situations. They complement standard rule-based strategies with energetic data-driven bits of knowledge and work consistently with current security frameworks. This combination permits you to halt dangers some time recently they happen and react rapidly, decreasing the probability of a security occurrence and the harm it may cause. The study also covers the technical pieces and strategies required to convey adaptable machine learning in an IIoT environment. She talks approximately the challenges that emerge in collecting and planning information, preparing models, and drawing conclusions in genuine time. She emphasizes the significance of having a high-performance, versatile framework that can handle huge sums of information of different sorts. The study talks about important issues like privacy, data security, and following the rules when using flexible machine learning models in IIoT ecosystems that are sensitive. It supports open and responsible methods to make sure that release is done in an ethical way and that operations are safe from threats from enemies.

Revolutionizing Cloud Security: Innovative Approaches for Safeguarding Data Integrity

Cloud computing uses third-party servers or private clouds to provide online services and on-demand enterprise resource access. Microsoft Azure, Amazon, IBM, and Google Apps allow customers to design and run cloud-based apps from anywhere. These cloud services store and retrieve data from faraway servers, highlighting the importance of security due to internet data transfer. Before using cloud computing, firms must handle security issues. Pay-per-use, self-service, and scalability make the model popular in banking, healthcare, retail, education, manufacturing, and business. Pay-per-use models let users use servers, networks, storage, services, and applications without buying them. Limited data control can result in account service issues, traffic hijacking, insecure APIs, malicious insiders, technology sharing issues, and multi-tenancy data failures. Continuous research and development improve security and user confidence. This paper provides a framework for understanding cloud computing, identifies security dangers and research issues, and emphasizes its relevance in major industries. It also recommends cloud security innovations and assesses worldwide data protection policies to improve data security and reduce risks. Cloud-stored data is vulnerable to programs with security weaknesses, notwithstanding its benefits. If the guest OS’s security is unreliable, virtualization on a hypervisor may expose data to attacks. Additionally, data security vulnerabilities in transit and at rest will be addressed.

ASMTP: Anonymous secure messaging token‐based protocol assisted data security in swarm of unmanned aerial vehicles

AbstractSwarm of Unmanned Aerial Vehicles (UAVs) broaden the field of application in various fields like military surveillance, crop monitoring in agriculture, combat operations, etc. Unfortunately, they are becoming increasingly susceptible to security attacks, such as jamming, information leakage and spoofing, as they become more common and in more demand. So, there is a wider need for UAVs, which requires the design of strong security procedures to fend off such attacks and security dangers. Even though several studies focused on security aspects, many questions remain unanswered, particularly in the areas of secure UAV‐to‐UAV communication, support for perfect forward secrecy and non‐repudiation. In a battle situation, it is extremely important to close these gaps. The security requirements for the UAV communication protocol in a military setting were the focus of this study. In this paper, we present the issues faced by the UAV swarm, especially during military surveillance operations. To secure the communication link in UAV, a new protocol for UAV Swarm communication is proposed with anonymous secure messaging token‐based protocol (ASMTP). The proposed protocol secures UAV‐to‐base station communication and safeguards the metadata of the sender and receiver nodes. The proposed model maintains the confidentiality, integrity and availability of data in the UAV Swarm and achieves robustness. In addition, it provides a different strategy for the cybersecurity gaps in the swarm of UAVs during military surveillance and combat operations.

China's Cultural Revolution and Mao's External Threat Inflation: Crushing Soviet-Style Capitalist Restoration, 1966–1969

Abstract Stoking fear and hatred of foreign countries to serve internal needs has been a recurring pattern in Communist China. This article examines Chinese discursive treatment of the Soviet Union in 1966–1969 in textual, visual, and audio outlets. During the height of the Cultural Revolution, radical followers of Mao Zedong deliberately choreographed anti-Soviet propaganda in accordance with their evolving political agenda at home. By emphasizing the need to forestall “capitalist restoration” in China, the propagandists alleged that capitalism had already been restored in the USSR. In the face of strong inner-party objection to the terror and destruction of the Cultural Revolution, Maoist polemicists discredited and attacked their domestic foes by accusing them of disloyalty and collaboration with Soviet “revisionists” to subvert China's Communist system. The radical Maoists sought to justify the chaotic violence of the Cultural Revolution by conjuring up the threat of a Soviet invasion, hoping to refocus domestic mass struggle on alleged sympathizers of the USSR. Yet Mao's invocation of this war scare triggered a dangerous security dilemma and turned the USSR into a strategic enemy, a striking example of a self-fulfilling prophecy in world politics.

Хибридни войни, медии, интернет: основни понятия и пресечни точки

Abstract: The article is predominantly of a theoretical and review nature and the aim is to bring out notions related to hybrid warfare, the role of the media and the Internet in a complicated and at the same time dynamically developing inter¬national environment through a critical reading of scientific publications, strate¬gies and normative documents. The scientific field implies an interdisciplinary approach. The methods of analysis and synthesis and desk research are used. No empirical research results are presented, but examples are given as illustrations of events and processes related to hybrid warfare. An attempt is made to answer the research questions: What are the characteristics of hybrid warfare in the 21st century? How does the media function in the face of confrontation, cyber threats and cyber security dangers? The article outlines the characteristics of hybrid warfare and describes the specifics of media used for information dissemination and manipulation. The role of institutions is outlined and some of their functions to neutralize the effects of disinformation in hybrid warfare are indicated. Keywords: hybrid warfare, internet, media, social networks, communication.

Age-Related Features of Perceptions about Personal Security in Adolescents with Intellectual Disabilities

The paper studies the way younger and older adolescents with intellectual disabilities perceive their personal security. The research data were collected by formalized interview, subjective scaling techniques, analysis of personal files, observation diaries, etc. The obtained information was processed using the methods of content and frequency analyses, the Mann-Whitney U test, the Spearman’s rank correlation, and the chi-square distribution. The perception of personal security included such concepts as threat, defense, security, and subjective personal security. The study revealed significant differences between younger and older adolescents in their perception of danger, threat, defense, security, and responsibility for personal security. Children with intellectual disabilities developed ideas about personal security in adolescence, their perception of threat and defense depended on life experience and socialization. Their categorical structure corresponded to the norm but was inferior in terms of generalization and cognitive complexity. They separated threat and defense from security, which they associated with safety rules. The results could be included in programs of corrective psychological and pedagogical support.

BCSDNCC: A Secure Blockchain SDN framework for IoT and Cloud Computing

Rapid progress can be observed in the field of computer network technologies. Blockchain technology(BCT) presents a potentially viable alternative for effectively mitigating performance and security issues encountered in distributed systems. Recent studies have focused on exploring a number of exciting new technologies, including BlockChain (BC), Software-Defined Networking (SDN), and the Internet of Things (IoT). Various technologies offer data integrity and secrecy. One such technology that has been utilized for a number of years is cloud computing (CC). Cloud architecture facilitates the flow of confidential information, enabling customers to access remote resources. CC is also accompanied with notable security dangers, concerns, and challenges. In order to tackle these difficulties, we suggest integrating BC and SDN into a CC framework designed for the IoT. The fundamental flexibility and centralized capabilities of SDN facilitate network management, facilitate network abstraction, simplify network evolution, and possess the capacity to effectively handle the IoT network. The utilization of BCT is widely acknowledged as a means to ensure robust security inside distributed SDN (DSDN) and IoT networks, hence enhancing the efficacy of the detection and mitigation procedures.

Enhancing Cryptocurrency Security

Cryptocurrency, a decentralized frame of computerized cash, has picked up ubiquity around the world. In any case, its broad selection has brought consideration to noteworthy security concerns. This paper presents a exhaustive examination of cryptocurrency security, distinguishing key challenges and proposing arrangements to support the security of advanced assets. The paper starts by talking about the foundational innovation of cryptocurrencies, specifically blockchain, which offers straightforwardness and unchanging nature but is defenceless to assaults such as 51% assaults and double-spending. It at that point digs into security dangers related with cryptocurrency capacity and trade stages, counting wallet vulnerabilities and hacking incidents. Current security best hones, such as multi-signature wallets and cold storage solutions, are analysed, alongside rising innovations like zero-knowledge proofs and homomorphic encryption. Furthermore, the part of administrative systems in advancing cryptocurrency security is investigated, highlighting the require for a adjusted approach that energizes advancement whereas securing investors. In conclusion, guaranteeing the security of cryptocurrencies is basic for cultivating believe and widespread adoption. By tending to vulnerabilities and executing vigorous security measures, partners can relieve dangers and open the complete potential of advanced monetary forms. This paper contributes profitable experiences to the continuous discourse on cryptocurrency security and recommends roads for future inquire about in this energetic field.

Exploring VANET Security within the VEINS Framework: A Review

Vehicular Ad Hoc Systems (VANETs) are urgent components of future shrewd transportation frameworks, encouraging communication between vehicles and framework to move forward security, effectiveness, and comfort through vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) intuitive. In any case, the broad selection of VANETs postures critical security challenges, counting potential malevolent assaults and security breaches, which debilitate the unwavering quality and reliability of vehicular communication systems. In reaction to these challenges, the VEINS Vehicles in Organize Recreation system has risen as an unmistakable apparatus for re enacting VANETs and assessing their security instruments. This systematic literature audit points to supplying a comprehensive investigation of investigative endeavours centring on the security angles of VANETs inside the VEINS framework. By synthesizing discoveries from existing considerations, this audit recognizes key security dangers, vulnerabilities, assault models, and countermeasures significant to VANETs re enacted utilizing VEINS. It sheds light on the current state of VANET security inside re enacted situations, highlighting crevices in information and giving experiences into future investigation directions. The discoveries of this audit contribute to a more profound understanding of VANET security challenges and offer profitable direction for analysts, professionals, and policymakers included in planning, creating, and sending secure VANET frameworks. Eventually, the point is to upgrade the security pose of VANETs and guarantee the astuteness and dependability of vehicular communication in future transportation systems.

Protection of Child Victims of Trafficking in Vietnam

Human trafficking rose as a particularly concerning problem for the international community in the 1990s and early 2000. It quickly became an outbreak when facing the global pressure of illegal immigration, loose borders, security danger, and economic and territorial integrity. Women and children are two typical vulnerable objects, hence targeted by culprits in kidnapping, blackmail, trafficking, sexual abuse, exploited labor, or taking organs. With a complicated diverse terrain, child trafficking in Vietnam has been an unsolved issue, forcing the Vietnamese government to work harder to protect the future generation. Therefore, this research focuses on two main objectives, including investigating the protection of child victims of trafficking in Vietnam and proposing potential solutions in the future. The article suggests increasing the number and quality of research (conducted by Vietnamese organizations/experts) about child trafficking. Experiences learned from the Vietnamese context showed that frequent inspection, evaluation, and summarization in implementing international rules are necessary as well as remuneration for border sites or the police army. In the end, this paper might help other developing countries face difficulties from academic studies on child trafficking, new trafficking techniques, and victim assistance measures in the COVID-19 pandemic.

LSCO: Light spectrum chimp optimization based spinalnet for live face detection and recognition

Face detection (FD) is considered to be of utmost significance in monitoring and safety systems. Finding concealed faces is a crucial research issue, particularly in populated or high-security settings like events and retail stores, where they may pose a security danger. To bridge this gap, a Light Spectrum Chimp Optimization (LSCO) enabled SpinalNet is introduced for live face detection and recognition. At first, an input image is fed up into the pre-processing phase to enhance the contrast image and remove the unwanted noises utilizing the Type 2 Fuzzy and Cuckoo Search (T2FCS) filter. Thereafter, the face is detected utilizing Viola-Jones which classifies the images based on the simple feature values to accurately identify and analyze human faces. Moreover, the detected image is passed to the feature extraction to acquire the face component features like eyes, nose, mouth, etc from the human face image. Here, the features employed are Local Directional Pattern Variance (LDPv), Local Directional Number Pattern (LDNP), Local Gabor Binary Pattern (LGBP) and statistical features like, mean, variance, kurtosis, skewness, and Convolutional Neural Networks (CNN) features. At last, face recognition is accomplished by the proposed LSCO which is tuned by SpinalNet. Here, LSCO is the integration of Light Spectrum Optimizer (LSO) and Chimp optimization (ChOA). The suggested technique has produced accuracy, True positive rate (TPR), True Negative rate (TNR) and execution time obtained 93.5%, 90.7%, 87.5% and 0.236 sec respectively.

A novel approach to IoT security for intrusion detection system using ensemble network and heuristic-assisted feature fusion

The area of “Internet of Things (IoT)” has gained popularity recently as a method for developing intelligent models. Security and privacy are regarded as the two main issues in every real-world IoT application. The security risks posed by IoT-enabled devices are serious threats to the development of the smart industry. Thus, to reduce the security dangers that emanate from IoT devices and give birth to numerous security concerns, Intrusion Detection Systems (IDSs) specifically tailored for the IoT sectors are important. Conventional intrusion detection systems (IDSs) are unsuitable for use in standard Internet of Things (IoT) networks for a variety of reasons, including memory, battery life, and processing power. Various Intrusion Detection Systems (IDSs) have been proposed in academic research to address these problems. However, while identifying anomalies, various intrusion detection systems (IDSs) have issues with low accuracy and numerous false alarms. To counteract the shortcomings of conventional systems, it is recommended that an ensemble deep learning model be utilized to identify intrusions within the Internet of Things (IoT) domain. The final result is then determined by averaging the scores, which is accomplished by combining them. Divergent measurements are thus used to validate the model. On the other hand, by prohibiting unwanted access, the recommended method not only increases the detection rate but also improves network security.

Determinants of CO2 emissions in the BRICS. A dynamic Panel ARDL approach

This paper examines the effects of Foreign Direct Investment, Economic Growth, Trade Openness, Energy Consumption, and Renewable Energy on CO2 Emissions in BRICS nations. This study employs the panel ARDL model to investigate the short- and long-term effects of an association between variables. The PMG estimator has shown itself to be more trustworthy and performs at a higher level of efficiency, leading us to the conclusion that it is the preferable technique. Because the error correction parameter, also known as the adjustment coefficient, is negatively significant, the findings demonstrate that there is a long-term relationship. According to the paper’s findings, there are both short-term and long-term effects of free trade and energy consumption on CO2 emissions. In the short run, FDI has positive impact on CO2 emissions, whilst renewable energy has detrimental effect. Surprisingly, the BRICS countries have not shown a correlation between economic development and carbon dioxide emissions. These findings may encourage policymakers in these countries in better recognizing the complexities of this occurrence, which in turn can assist direct future choices about this growing international security danger. Keywords: BRICS, Energy, Trade, CO2, Panel ARDL.

Investigating the Efficacy of Deep Reinforcement Learning Models in Detecting and Mitigating Cyber-attacks: a Novel Approach

Ordinary defence components like rule-based firewalls and mark based detection are not staying aware of the always expanding intricacy and frequency of cyber security dangers. The reason for this work is to explore the way that deep reinforcement learning (DRL), a subfield of artificial intelligence famous for its viability in handling testing decision-production situations, may be utilized to improve cyber security conventions. To mimic and balance threatening cyber-attacks, we present a system that utilizes deep reinforcement learning (DRL). We propose a specialist based model that can learn and adjust ceaselessly in powerful network security situations. In light of the present status of the network and the rewards it gets for its decisions, the specialist concludes what the best game-plans are. Specifically, we utilize the policy gradient (PG)- based double deep Q-network (DDQN) model and trial on three different datasets: NSL-KDD, CIC-IDS, and AWID. Our review demonstrates the way that DRL can really further develop the detection after-effects of cyber-attacks. Utilizing the policy gradient DDQN model on different datasets, we find prominent upgrades in cyber security conventions. Specific boundary modifications upgrade the viability of our philosophy much more, displaying empowering results on different datasets. This exploration features the potential of deep reinforcement learning (DRL) as a successful instrument in the field of cyber security. Our examination progresses detection techniques and gives a versatile arrangement that can be applied to an assortment of cyber security worries by giving areas of strength for a to demonstrating and relieving cyber dangers.

Simulation of Vehicular Bots-Based DDoS Attacks in Connected Vehicles Networks

Connected vehicles are more vulnerable to attacks than wired networks since they involve rapid mobility, continuous data flow across connected nodes, and dynamic network design in a distributed network environment. Distributed Denial of Service (DDOS) is one of the most common and dangerous security attacks on connected vehicle networks. Attackers can remotely control malicious nodes that are programmed to attack other nodes known. The compromised nodes are known as botnets, which will constantly flood the target nodes with User Datagram Protocol (UDP) packets, disrupting the target nodes data flow and operation. Hence, the goal of this research is to create and simulate a vehicular bot-based Distributed Denial of Service (DDoS) assault in connected vehicle networks. A simulation-based methodology is implemented to observe the impact of the number of bots, DDoS rate, and maximum bulk packet size on network performance. Using the NS-3 network simulator, 73 random mobile vehicle nodes with up to 100 vehicle bots were simulated, and the results are discussed. Regardless of the computational constraints, the findings from this study adds to understanding the risks and problems associated with data transmission by analyzing the impact of vehicular bot-based DDoS attacks on connected vehicle performance. Doi: 10.28991/HIJ-2023-04-04-014 Full Text: PDF

A Novel Technique for Enhancing Data Security Using Hybrid Coding in Medical Images

The Internet's rapid development in data transmission has made it simpler to convey data quickly and accurately to the intended recipient. Data is one of the most essential assets to be protected during Internet transmission, thus it is necessary to handle the rising danger of security. Through hacking, unauthorized person can alter or misuse the crucial data. Therefore, security is a primary problem that requiring attention. Compression and encryption are frequently used to share and save images respectively. The effectiveness of digital image services depends on how the two activities are performed. Medical images play a significant role in the modern healthcare system because they are utilized to preserve patient data including illness diagnoses and other pertinent details. Thus, data must be maintained in a secure manner. To deliver data safely and unchanged to the target place, a variety of techniques, including cryptography and steganography are utilized to solve security difficulty, several research and exercises were done to produce a range of tactics and algorithms. In this paper, a technique for concealing the secret image is proposed.

Enhanced capsule network‐based executable files malware detection and classification—deep learning approach

SummaryMalware has considerably increased recently, posing a serious security danger to both people and enterprises. In order to distinguish and stop the negative effects of malware, a variety of machine and deep learning approaches have been used to detect it. However, while extracting malware features, the feature‐to‐feature spatial hierarchy is not taken into account by the existing techniques and as a result, information is lost during the pooling operation. Hence, a modified capsule deep neural network was developed in which discriminative features are extracted from three channel image derived from malware binary with considering feature‐to‐feature spatial hierarchy. Also, conventional capsule deep neural network is modified by adding a global average pooling layer before fully connected layer thereby classified the dataset as malicious or benign without any loss of information. Moreover, these malwares were not accurately classified based on their families using existing variants of convolutional neural network (CNN) since malware family variants can modify due to minute changes in malware binaries. Hence, a hybrid deep convolutional neural network (DCNN) and long‐short‐term memory (LSTM) has been utilized that determine minute changes in malware binaries using LSTM without vanishing gradient issue and effectively perform malware family classification using DCNN. As a result, the proposed approach successfully identifies malware in executable files and categorizes malware into families with 98.5% accuracy.