Potential Security Risks Research Articles

Tell me more, tell me more: repeated personal data requests increase disclosure

Abstract Personal data is of great commercial benefit and potential sensitivity. However, for the consumers who provide their personal data, doing so comes with potential costs, benefits and security risks. Typically, consumers have the option to consent to the use of personal/sensitive data but existing research suggests consumer choices may only be weakly related to their concerns (the privacy paradox). Here, we examine if the repetitive nature of data requests alters behaviour but not concern, therefore, explaining the divergence. This work is theoretically grounded in ‘Foot in the door’ research in which small initial requests facilitate subsequent larger requests. An initial laboratory study asking for real, personal data demonstrated increased information disclosure at a subsequent request. A second online study replicated the increased information disclosure effect and found no change in associated privacy concern. We find this supports foot-in-the-door as one explanation of the privacy paradox. We suggest ways for businesses and consumers to encourage an acceptable level of disclosure to match personal beliefs for mutual trust and benefit.

Securing image-based document transmission in logistics and supply chain management through cheating-resistant visual cryptographic protocols.

In today's digital landscape, securing multimedia visual information-specifically color images-is of critical importance across a range of sectors, including the burgeoning fields of logistics and supply chain management. Traditional Visual Cryptography (VC) schemes lay the groundwork for encrypting visual data by fragmenting a secret image into multiple shares, thereby ensuring no single share divulges the secret. Nevertheless, VC faces challenges in ascertaining the integrity of reconstructed images, especially when shares are manipulated maliciously. Existing solutions often necessitate additional shares or a trusted third party for integrity verification, thereby adding complexity and potential security risks. This paper introduces a novel Cheating-Resistant Visual Cryptographic Protocol (CRVC) for Color Images that aims to address these limitations. Utilizing self-computational models, this enhanced protocol simplifies the integrated integrity verification process, eliminating the need for extra shares. A standout feature is its capability to securely transmit meaningful shares for color images without compromising the quality of the reconstructed image as the PSNR maintains to be ∞. Experimental findings substantiate the protocol's resilience against quality degradation and its effectiveness in verifying the authenticity of the reconstructed image. This innovative approach holds promise for a wide array of applications, notably in sectors requiring secure document transmission, such as Logistics and Supply Chain Management, E-Governance, Medical and Military Applications.

Multiple Observation HMM-Based CAN Bus Intrusion Detection System for In-Vehicle Network

As modern vehicles become more intelligent and connected, the number of ECUs and communication interfaces with external networks (such as 3G/4G and Bluetooth) has increased significantly, which raises potential network security risks. Due to the absence of effective security measures, there is a frequent occurrence of cybersecurity incidents targeting vehicles, particularly the in-vehicle CAN bus network. As the main bus in the vehicle, CAN bus faces important challenges in its safety due to the lack of security mechanisms. Therefore, we propose a CAN bus intrusion detection system based on multiple observation HMM for in vehicle networks to enhance the security of the vehicle. Specifically, the proposed algorithm builds a multiple observation HMM based on the ID and data fields of normal CAN bus traffic. According to the established HMMs, we calculate the existence probability of the frame under the defined time window as the detection threshold. When the existence probability of the frame to be detected exceeds the normal threshold range, it is considered abnormal. Furthermore, we establish four common attack models based on the collected real vehicle data, and evaluate the performance of the proposed algorithm in these attack scenarios. The experimental results show that the proposed method has better detection performance than other frame by frame anomaly detection methods in four attack scenarios.

CyLearn: An Assistive Web-Based e-Learning System for Cybersecurity Skills Course

Distance learning has become common in Philippines as schools have started to cater to online education due to the outbreak of COVID-19. E-learning has been adapted to provide learning materials and train students over the Internet. However, for people with impairments such as hearing and vision, this can be a problem. With this situation, the study developed an e-learning application with an assistive laboratory for assessments, specifically for college students. The subject matter used for the developed system is cybersecurity. cybersecurity is now one of the most in-demand courses due to the rising cases of cyberattacks. The developed system allows students to learn cybersecurity and conduct laboratory exercises in a virtual machine with text-to-speech and closed-caption technology. The study used two post-tests in a controlled group of respondents to determine if the developed system accommodates online cybersecurity learning compared to the traditional learning methods available. Comparing scores from the first group who used the developed system to the second group who used the current learning methods shows that the former received higher scores and has deemed the developed system. The developed system that has presented a guided cybersecurity learning scenario is more effective than current learning methods. Security testing was applied in this study to identify potential vulnerabilities and possible security risks in e-learning systems like this.

Influence of ethnic and social factors on the results of the 14th Saeima elections

The aim of this research is to to research the most significant factors that influenced the Latvian parliament (14th Saeima) election results in 2022. The results of the 14th Saeima elections showed that ethnically non-Latvian citizens of the Republic of Latvia still display a statistically significant negative vote against the political parties included in the previous government of Latvia, which does not promote social or ethnic stability and creates conditions for potential security risks to Latvia in the future.

DoS Attack Detection Based on Deep Factorization Machine in SDN

Software-Defined Network (SDN) decouples the control plane of network devices from the data plane. While alleviating the problems presented in traditional network architectures, it also brings potential security risks, particularly network Denial-of-Service (DoS) attacks. While many research efforts have been devoted to identifying new features for DoS attack detection, detection methods are less accurate in detecting DoS attacks against client hosts due to the high stealth of such attacks. To solve this problem, a new method of DoS attack detection based on Deep Factorization Machine (DeepFM) is proposed in SDN. Firstly, we select the Growth Rate of Max Matched Packets (GRMMP) in SDN as detection feature. Then, the DeepFM algorithm is used to extract features from flow rules and classify them into dense and discrete features to detect DoS attacks. After training, the model can be used to infer whether SDN is under DoS attacks, and a DeepFM-based detection method for DoS attacks against client host is implemented. Simulation results show that our method can effectively detect DoS attacks in SDN. Compared with the K-Nearest Neighbor (K-NN), Artificial Neural Network (ANN) models, Support Vector Machine (SVM) and Random Forest models, our proposed method outperforms in accuracy, precision and F1 values.

A comparative study of web application security scanners for vulnerability detection

A Web Vulnerability Scanner (WVS) is a software tool that assesses the security of web applications by conducting automated penetration tests. It speeds up the process, reduces costs, and eliminates the need for specialized testing engineers. This study evaluates the vulnerability detection capabilities of six WVSs, three commercial scanners, and three open-source scanners. The goal is to identify and mitigate potential security risks before they are exploited by malicious users. The study employed two well-known vulnerable web applications and four relevant metrics, such as detection rate of accuracy, recall, precision, and the ability to detect different vulnerabilities using the Open Web Application Security Project (OWASP) as a reference.

Experimental and mechanical analyses of dust agglomeration on photovoltaic surfaces due to condensation

The dust deposited on a photovoltaic (PV) surface decreases the photoelectric efficiency of a module, leading to potential security risks. A few studies have demonstrated that condensation affects dust accumulation on PV surfaces; however, the mechanism remains unclear. Therefore, this study investigated the effects of dust agglomeration dynamic factors on PV surfaces using micro-experiments and mechanical analysis. An experimental platform for dust particle agglomeration due to condensation was designed and developed, and microscopic experiments were conducted to study the dust particle agglomeration process on the PV panel surface. The particles on the hydrophilic PV surface were confirmed to agglomerate owing to condensation. The particle agglomeration process on the hydrophilic PV surface primarily entails three typical processes that occur sequentially: fusion between droplets, fusion between the semi-submerged particles and droplets, and agglomeration between semi-submerged particles. The particles agglomerated on the hydrophilic PV surfaces have a tiled morphology. An analysis of the particle agglomeration mechanism on the hydrophilic PV surface due to condensation was performed. Water surface tension was identified as the main dynamic factor leading to particle agglomeration during condensation. The results provide theoretical guidance for reducing the dust deposited on PV surfaces.

Encrypted 5G over-the-top voice traffic classification using deep learning

With the commercialization of fifth-generation (5G), the rapid popularity of mobile Over-The-Top (OTT) voice applications brings huge impacts on the traditional telecommunication voice call services. Tunnel encryption technology such as Virtual Private Networks (VPNs) allow OTT users to escape the supervision of network operators easily, which may cause potential security risks to cyberspace. To monitor harmful OTT applications in the context of 5G, it is critical to identify encrypted OTT voice traffic. However, there is no comprehensive study on typical OTT voice traffic identification. This paper mainly focuses on analyzing OTT voice traffic in the 5G network specifically. We propose employing Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs) to identify encrypted 5G OTT voice traffic, study the identification performance of used deep learning methods in three different scenarios. To verify the performance of the proposed approach, we collect 28 types of typical OTT and non-OTT voice traffic from the experimental 5G network. Experimental results prove the effectiveness and robustness of the proposed approach in encrypted 5G OTT voice traffic identification.

A new 5D fractional-order conservative hyperchaos system

At present, most of the encryption algorithms based on chaotic systems use dissipative chaotic systems. However, the dissipative chaotic systems have attractors and are easy to reconstruct, which leads to potential security risks in the process of data transmission. Therefore, a novel five-dimensional conservative hyperchaotic system is proposed in this paper, and the integer order system is transformed into a fractional-order system based on the Adomian decomposition method(ADM). The dynamic characteristics of the system are discussed by using classical analysis methods such as Lyapunov exponent spectrum(LEs), bifurcation diagram, phase diagram, and timing diagram. By changing the system parameters and the differential order q, we found a wealth of dynamic phenomena, such as quasi-periodic flow, chaotic flow, and hyperchaotic flow. When the initial value is used as a variable, it is found that the system has initial offset boosting behavior, multiple stability, and special transient behavior. In addition, we use the spectral entropy algorithm to analyze the complexity of the system. Finally, hardware experiments are also carried out using digital signal processor (DSP) to verify the correctness of the numerical simulation, and also to prove the physical realizability of the system, to create conditions for its subsequent engineering applications.

Blockchain-Based Solutions Supporting Reliable Healthcare for Fog Computing and Internet of Medical Things (IoMT) Integration

The Internet of Things (IoT) has radically transformed how patient information and healthcare monitoring are monitored and recorded and has revolutionized the area by ensuring regular 24 × 7 tracking without costly and restricted human resources and with a low mistake probability. The Internet of Medical Things (IoMT) is a subsection of the Internet of things (IoT) that uses medical equipment as things or nodes to enable cost-effective and efficient patient monitoring and recording. The IoMT can cope with a wide range of problems, including observing patients in hospitals, monitoring patients in their homes, and assisting consulting physicians and nurses in monitoring health conditions at regular intervals and issuing warning signals if emergency care is necessary. EEG signals, electrocardiograms (ECGs), blood sugar levels, blood pressure levels, and other conditions can be examined. In crucial situations, quick and real-time analysis is essential, and failure to provide careful attention can be fatal. A cloud-based IoT platform cannot handle these latency-sensitive conditions. Fog computing (FC) is a novel paradigm for assigning, processing, and storing resources to IoT devices with limited resources. Where substantial processing power or storage is required, all nodes in a fog computing scheme can delegate their jobs to local fog nodes rather than forwarding them to the cloud module at a greater distance. Identifying potential security risks and putting in place adequate security measures are critical. This work aims to examine a blockchain (BC) as a potential tool for mitigating the impact of these difficulties in conjunction with fog computing. This research shows that blockchain can overcome fog computing’s privacy and security concerns. It also discusses blockchain’s issues and limitations from the perspective of fog computing (FC) and the IoMT.

A Neighbor Trust Weight Based Cryptography for Multi Key Distribution for Improving Quality of Service in MANETS

A Mobile Ad-Hoc Network (MANET) is a self-configuring network that provides temporary connections to several wireless nodes. Trust mechanisms are employed in routing protocols to quickly locate a safe path. Because of its openness and complexity, MANET can be attacked in a number of ways. To begin mitigating potential security risks, a number of different cryptographic key generation strategies are explored. A key management system for MANET security is available with different encryption techniques. Identity with Trust Level based Cryptography Model (ITLCM) is used to generate multiple keys and distribute these to particular targets. At this stage, key management protocols are essential to any secure group architecture of communication. Because of its dynamic topology which extensively affects its application, the multi key management is an essential task. When compared to more conventional methods of protecting a network, MANET security is entirely novel. Security routing protocol implementation is difficult since it requires the production and distribution of multiple keys. To provide both connection and message protection without relying on third parties, the Neighbor Trust Weight based Routing Model (NTWRM) is designed. In the proposed model, a trusted node is selected to monitor all of the nodes in the routing process to create a stable multi-key distribution environment that enhances MANET performance. In comparison with traditional methods, the proposed model shows that its findings are better than the existing ones.

Big data network security defense mode of deep learning algorithm

Abstract With the rapid development and progress of big data technology, people can already use big data to judge the transmission and distribution of network information and make better decisions in time, but it also faces major network threats such as Trojan horses and viruses. Traditional network security functions generally wait until the network power is turned on to a certain extent before starting, and it is difficult to ensure the security of big data networks. To protect the network security of big data and improve its ability to defend against attacks, this article introduces the deep learning algorithm into the research of big data network security defense mode. The test results show that the introduction of deep learning algorithms into the research of network security model can enhance the security defense capability of the network by 5.12%, proactively detect, and kill cyber attacks that can pose threats. At the same time, the security defense mode will evaluate the network security of big data and analyze potential network security risks in detail, which will prevent risks before they occur and effectively protect the network security in the context of big data.

Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction.

A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network’s security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network’s integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network. In the first part of this survey paper, we offer an introduction to the NIDS theory, as well as recent research that has been conducted on the topic. After that, we conduct a thorough analysis of the most recent ML- and DL-based NIDS approaches to ensure reliable identification of potential security risks. Finally, we focus on the opportunities and difficulties that lie ahead for future research on SDN-based ML and DL for NIDS.

Lipidomics and transcriptomics insight into impacts of microplastics exposure on hepatic lipid metabolism in mice

Microplastics (MPs), the emerging environmental pollutants, have attracted global attention due to the potential public health challenge and ecological security risk. Recent studies suggested liver as a vulnerable organ to MPs exposure, evidenced by abnormal hepatic lipid metabolism upon MPs intake in multiple animal species. However, the specific changes of lipid metabolism in mammalian livers, as well as the underlying mechanisms, remain to be elucidated. In the present study, C57BL/6 mice were randomly assigned to normal drinking water or drinking water containing 100 μg L−1 or 1000 μg L−1 polystyrene (PS) MPs for 8 weeks. MPs exposure exerted no significant effect on body weight, serum triglyceride or total cholesteryl esters. However, mice showed impaired glucose tolerance and hepatic lipid deposition in response to high-dose MPs administration. Further lipidomic analysis showed significant alteration in hepatic lipid species particularly with free fatty acids (FFAs) and triacylglycerols (TAGs) in mice exposed to MPs. Meanwhile, the liver transcriptional profile indicated MPs exposure-induced differentially expressed genes (DEGs) were enriched in pathways of lipid metabolism and unfolded protein response. Furthermore, most altered lipid species were significantly correlated with DEGs enriched in lipid metabolic signaling. These findings provide lipidomic and transcriptional signatures of liver in response to MPs exposure, which will shed light on further understanding of the metabolic toxicity of MPs.

Africa’s Ed-Tech Platforms: Protecting Children’s Right to Privacy

Ed-tech platforms are used to create a more engaging, inclusive, and individualized learning experience. A number of these platforms utilize Artificial Intelligence (AI). AI-enabled learning tools and approaches have revolutionized the global education sector (Pedro et al., 2019). They have been recognized for their contributions to enhancing the quality of learning and teaching. AI aids teachers and students in their lessons (Pedro et al., 2019). Additionally, AI has been lauded for its potential to boost students' knowledge and learning habits, while also creating a more personalized approach to learning (Pedro et al., 2019). Children are less likely to read or understand privacy policies, and they may have a limited understanding of their right to privacy and data protection. In addition, they are more susceptible to marketing techniques that adults can identify. The existence of their personal information online poses potential safety and security risks. Therefore, privacy policies on Ed-tech platforms must incorporate children's rights and an understanding of their right to privacy. This includes online protection and security measures established to protect children's data. The primary audience for this policy brief is corporations. Although, children’s rights to privacy are a collective responsibility of the parents, legal guardians, and other individuals legally responsible for the child. In the Ed-tech space, this responsibility extends to AI platform owners, who manage the platforms, and policymakers and regulators concerned with data protection and children’s rights.

Security and Privacy in Artificial Intelligence-Enabled 6G

The sixth-generation (6G) mobile communication network is expected to provide world-connected smart and autonomous services by leveraging artificial intelligence (AI) technologies. In particular, federated learning (FL) is advocated to promote the implementations of ubiquitous AI applications in 6G, where massive distributed data is utilized to cooperatively train AI models as well as protect privacy and reduce resource consumption. However, due to the large number of interactions among mobile devices or infrastructures in FL, the AI inevitably suffers from a series of potential privacy and security risks. In this article, we investigate the security and privacy of AI-enabled 6G. Specifically, we first present the AI-enabled 6G architecture, including the space-air-ground-ocean integrated network (SAGOIN) architecture and two different FL-based AI model training frameworks in 6G. Furthermore, we discuss in detail the privacy and security threats, including cheating attack, low-quality local model training attack, and privacy stealing attack. Afterwards, we present a case study to design the security and privacy preservation scheme by integrating the trust evaluation, Q-learning based incentive, and local differential privacy (LDP) mechanism. Finally, we give out several future research directions in promising AI-enabled 6G.

Cybersecurity Issues of IoT in Ambient Intelligence (AmI) Environment

Ambient intelligence (AmI) refers to the ability of devices to interact seamlessly with their surroundings. An increased use of IoT in ambient intelligence has led to a heightened concern for cybersecurity. Vulnerabilities in the software or firmware of IoT devices have been exploited by hackers to gain control of the devices or the networks. They could also use ambient intelligence systems to collect sensitive data from IoT devices. The problem of IoT security is twofold. First, it concerns the safety of the embedded IoT system and the protection of embedded data. To protect the devices, it's essential to understand the various types of attacks that are possible and to deploy appropriate security measures. This article addresses these issues by providing an overview of potential security risks and the latest solutions and approaches that can help to tackle these problems.

A Secure and Authenticated Mobile Payment Protocol Against Off-Site Attack Strategy

Mobile payment system has been expected to provide more efficient and convenient payment methods. However, compared to traditional payments, mobile payment issues related to the security of electronic accounts and payment apps present serious challenges. In this paper, we find the potential security risks by analyzing the commonly used tokenized mobile payment method and put forward the corresponding off-site attack strategy. In this scenario, the attackers are not only limited to malicious third parties but also can be illegal merchants. To address the off-site attack, especially the potential attackers who may be malicious merchants, we also propose SALP, a secure and authenticated payment protocol, using time and position as necessary conditions for the payment confirmation. Furthermore, we leverage identity-based signature (IBS) to prevent altering the information and reduce the overhead of the third-party authentication. We conduct case studies to demonstrate that the SALP can effectively prevent the off-site payment attack without a trusted hardware environment. In particular, we finally argue that SALP does not bring additional system overhead without degrading the convenience of mobile payment.

Fine-Grained Identification for Large-Scale IoT Devices: A Smart Probe-Scheduling Approach Based on Information Feedback

A large number of IoT devices access the Internet. While enriching our lives, IoT devices bring potential security risks. Device identification is one effective way to mitigate security risks and manage IoT assets. Typical identification algorithms generally separate data capture and target identification into two parts. As a result, it is inefficient and coarse-grained to evaluate the results only once the identification process is complete and then adjust the data capture strategy afterward. To solve this problem, we propose a fine-grained probe-scheduling approach based on information feedback. First, we model the probe surface as three layers for IoT devices and define their relationships. Then, we improve the policy gradient algorithm to optimize the probe policy and generate the optimal probe sequence for the target device. We implement a prototype system and evaluate it on 53,000 IoT devices across various categories to show its wide applicability. The results indicate that our approach can achieve success rates of 96.89%, 93.43%, and 83.71% for device brand, model, and firmware version, respectively, and reduce the identification time by 55.96%.