Network Events Research Articles

Creating and analyzing datasets using eBPF technology in container infrastructures

Containerized environments such as Docker provide isolation and deployment of distributed services and applications. However, anomalies and attacks can still occur within containers and within containerized networks. This paper proposes the creation of a dataset using eBPF-based traceback to monitor communication between containers in a Docker environment. In the process, a Docker environment with containers providing different services is created and eBPF-based data collection programs are configured to capture packet-level data. The collected dataset contains characteristics such as process IDs, container IDs, network events, and function arguments. A detailed analysis of these characteristics can provide valuable information about container network behavior, performance issues and vulnerabilities. As a proof of concept, the paper evaluates the applicability of classifiers such as Naive Bayes, KNN, SVM and Random Forest to detect anomalies in the dataset. The results show accuracy in the range of 0.42-0.67 and F-measure performance in the range of 0.54-0.65. Overall, the study demonstrates the potential of creating eBPF-based datasets for container security monitoring and anomaly detection by further optimizing data analysis techniques. The proposed approach can help in securing, troubleshooting, and optimizing container deploymet., mobile network, visibility graph, geometric graph, random walks, simulation.

Development of an information system for determining the opinion of citizens based on analysis of information on social networks and mass media

Currently, most of the modern society is concentrated in large and medium-sized cities. This gives residents more opportunities for self-realization, professional development, socialization, as well as a greater number of options for spending their personal time, which undoubtedly affects the satisfaction of a person living in an urban environment. To prevent a decline in the development of the city, constant monitoring of the state of the urban environment is necessary, since the population is constantly in direct contact with it. The number of the city's population and the satisfaction of living in it is one of the most important components of the indicator of the development of all sectors of the city. Assessment of the mood of the population is most often done through personal interviews with citizens. The purpose of the survey is to get an opinion about the general situation in the city or about certain areas, events, news. To automate this process, it was decided to create an information system for collecting citizens' opinions on certain events in social networks and mass media, which greatly simplifies the process of assessing the mood of citizens.

Modeling the Social Influence Effectiveness of Popular Weibo Events

Social networks are important for people to obtain information, make comments, and exchange opinions. The public opinion generated in social networks has a great impact on public life and value guidance, and the effect of public opinion is worth paying attention to and analyzing. Research on the influence of hot events can help us better understand the public attention and response to an event, and help industries and social organizations to judge market trends and predict future development direction. Social phenomena and trends reflected by hot events can be used as basic data and research objects for scientific research to promote the development and progress of the discipline. Aiming at social events in social networks, this paper analyzes the relevant user behavior data such as retweets, comments, likes, topic characteristics such as event duration, and other key characteristics and proposes an influence effectiveness analysis model for events, referred to as the caloric value model. This model is based on factors such as user interactivity, event activity, duration of the event, freshness of events,media attention, and the event heat decay ratio. It evaluates event heat, calculates the influence of events, identifies hot events with a high level of discussion, selects these hot events, and validates the findings through experiments. The model proposed in this paper has a good effect on the rationality and feasibility of heat evaluation.

Using machine learning for the optimisation of operations and management in electric systems and networks

This research employs the Random Forest Machine Learning model to predict electricity consumption and detect anomalies in electrical networks. Addressing the energy sector’s challenges, such as supply reliability and renewable energy integration, this model processes historical electricity consumption data, weather conditions, and network events to efficiently forecast demand and identify anomalies. Data cleansing and normalisation preceded the training phase, where the model was fine-tuned using historical data to balance forecast accuracy and overfitting avoidance. The dataset was divided into training (80%) and testing (20%) sets for performance evaluation. Through cross-validation, optimal model hyperparameters were determined. The findings highlight the model’s efficacy in accurately predicting daily electricity consumption in a small, homogenous town. The model achieved a Mean Absolute Error (MAE) of 198.73 MWh and a coefficient of determination (R²) of 0.9387. Temperature, humidity, and wind speed were identified as key influencing factors on consumption levels. Conclusively, the Random Forest model presents a valuable tool for energy management, offering precise consumption forecasting and anomaly detection capabilities. Future work will address computational demands and enhance model integration with other Machine Learning methods for improved performance. This contribution is significant for efficient energy system planning and operation.

Just be real with me: Perceived partner authenticity promotes relationship initiation via shared reality

Relationships are a critical component of professional life, yet people often experience difficulties forming workplace bonds. We examine the impact of perceiving one’s interaction partner as authentic in an initial encounter as a key driver of relationship initiation through shared reality. Study 1, a longitudinal field study of professional networking events, revealed that perceived partner authenticity predicted relationship initiation four weeks later. Study 2 found pre-registered experimental evidence for the relationship between perceived partner authenticity and relationship initiation. Study 3 replicated these effects between pairs of new acquaintances working on a collaborative task and found that shared reality mediated the link between perceived partner authenticity and relationship initiation. In Study 4, these effects persisted for observable authenticity behaviors in conversations. Finally, Studies 5a-5b tested the causal effect of perceived partner authenticity on relationship initiation through increased shared reality. Overall, our results suggest that perceiving one’s partner as authentic during initial professional encounters promotes relationship initiation by fostering shared reality.

Digital Design of Film and Television Scenes Based on Interactive Ritual Chain in the New Media Era

Abstract In this paper, we utilize group event prediction techniques for film and television communication to explore the effects of interactive cinema and scene design on participants’ feelings and the impact of film and television communication. A five-dimensional feature vector is used for quantization, and the dimensions and categories of the feature vectors can be changed to make them more consistent with the characteristics of the research object for different types of network events in the interaction scene. For the analysis and prediction of event attention, the data of the budding period before the outbreak is extracted in the preprocessing stage for the prediction of future outbreaks of the event, as the data for the analysis of attention, and the event attention model is constructed through differential equations to realize the prediction of the impact of attention. The results showed that during the video content exploration, respondents had a higher demand for experiencing the festival ritual process (5.73) and preferred the institution of making five-color glutinous rice (2.53). The interactive video scenes were designed with comforting colors (1.86), beautiful interface aesthetics, and a simple operation flow (3.11). Of the eight secondary indicators found in the documentary dissemination effect, five indicators, including content theme and video duration, significantly impacted the number of interactions with the video. There is a significant difference between interactive behavior and emotional experience, and interactive respondents can feel the emotional experience more.

Simulation and Evaluation of Distributed consensus Network for Multi-Agent Systems for Sybil Attacks

Distributed average consensus represents the amount of computing of inputs held by multiple agents that communicate through peer-to-peer networks. Collaboration among operators is essential for any distributed standard consensus protocol as every specialist needs to contribute to different operators, typically the adjacent (neighbouring) operators. Internet-of-Things (IoT) implementation is challenging because of its heterogeneous, massively distributed nature. The challenges of this challenge can be addressed with blockchain-based platforms and technologies. Testing and evaluation platforms are required for Blockchain deployments in IoT. A realistic and configurable network environment is presented in this paper to evaluate consensus algorithms. Many blockchainevaluation platforms do not provide a configurable and realistic network environment or are tied to a specific consensus protocol. With our simulator, practitioners can evaluate how consensus algorithms affect network events in congested or contested scenarios to determine the best consensus algorithm. It is proposed to achieve this task by generalizing consensus methods. The Blockchain simulator employs Discrete event network simulations for increased fidelity and scalability. In addition to evaluating the time, state block rate (%), estimation error, average throughput, and simulation time, we evaluate the performance of the proposed techniques based on the number of peer nodes. A comparison of the average transaction delivery rate with a traditional protocol is shown. The proposed protocol has a higher throughput average than the traditional one.

Gated graph neural networks for identifying contamination sources in water distribution systems

Contamination events in water distribution networks (WDN) pose significant threats to water supply and public health. Rapid and accurate contamination source identification (CSI) can facilitate the development of remedial measures to reduce impacts. Though many machine learning (ML) methods have been proposed for fast detection, there is a critical need for approaches capturing complex spatial dynamics in WDNs to enhance prediction accuracy. This study proposes a gated graph neural network (GGNN) for CSI in the WDN, incorporating both spatiotemporal water quality data and flow directionality between network nodes. Evaluated across various contamination scenarios, the GGNN demonstrates high prediction accuracy even with limited sensor coverage. Notably, directional connections significantly enhance the GGNN CSI accuracy, underscoring the importance of network topology and flow dynamics in ML-based WDN CSI approaches. Specifically, the method achieves a 92.27% accuracy in narrowing the contamination source to 5 points using just 2 h of sensor data. The GGNN showcases resilience under model and measurement uncertainties, reaffirming its potential for real-time implementation in practice. Moreover, our findings highlight the impact of sensor sampling frequency and measurement accuracy on CSI accuracy, offering practical insights for ML methods in water network applications.

Extreme synchronization events in a model neuron-astrocyte network

The examination of synchronization in neural networks is essential to comprehend how the brain functions in normal and pathological states. The synchronization of signals between groups of neurons is paramount for brain activity and associated with various brain functions, including memory, movement, sleep, and attention. However, a balance between synchronization and desynchronization must be maintained for proper brain function. During epilepsy, spontaneous transitions occur between two states of brain activity: synchronous and asynchronous. These transitions can be induced by various factors, including imbalances in chemical signals, changes in network activity, and other mechanisms that are not yet fully understood. Understanding these spontaneous transitions and the underlying mechanisms is a crucial aspect of epilepsy research. It will lead to the development of novel treatment approaches and strategies for inhibiting synchronized activity and averting seizure episodes. A neuron-astrocyte network model was constructed in this study, which displayed spontaneous and induced transitions between asynchronous and synchronous states triggered by external stimuli. The network model utilized 1000 Izhikevich neurons [1] interconnected with excitatory synapses and organized according to a scale-free graph. Each neuron had a bidirectional interaction with a single astrocyte, resulting in a total of 1000 astrocytes in the network. The Ullah model [2] simulated intracellular calcium ion concentration dynamics in a single astrocyte. The action potential generated by a neuron causes the release of neurotransmitters into the synaptic cleft, which triggers the corresponding astrocyte to release calcium ions from its endoplasmic reticulum into the cytoplasm, generating a calcium impulse. The calcium pulse within the astrocyte led to the release of a gliotransmitter. This gliotransmitter has the potential to regulate the synaptic transmission efficiency of both presynaptic and postsynaptic terminals associated with the respective astrocyte. The study simulated the effect of astrocytic suppression on neurotransmitter release. The strength of the synaptic input connection to the neuron that interacts with the astrocyte decreased in proportion to the amplitude of the calcium pulse in the astrocyte. Within the context of the implemented model study, we analyzed the global order parameter’s dependence on the maximum synaptic weight. Our bifurcation analysis showed the presence of hysteresis within a specific range of parameter values for connection weight maximums. In this range, the system under consideration is capable of displaying both synchronous and asynchronous regimes. However, an asynchronous regime was the only observed outcome below the lower boundary of the range, and only a synchronous regime was observed above the upper boundary. A statistical analysis was carried out on the duration of the model’s asynchronous states, specifically for the parameter value of the maximum synaptic weight near the upper boundary of the hysteresis region. The network dynamics were simulated for a prolonged period, and a histogram of the asynchronous state durations was plotted on a double logarithmic scale. The resulting data points were approximated using linear regression, which yielded a power-law exponent of –3/2. Based on the obtained results, the neuron-astrocyte network can exhibit spontaneous transitions between two states: synchronous and asynchronous, similar to pathological processes in the brain. More precisely, the power-law exponent of –3/2 aligns with values discovered in experimental recordings of epileptic activity in rodent brains [3–5]. Detailed modeling of biophysical processes revealed the spontaneous emergence of global order in the network induced by noise. Astrocytic effects mediated the disruption of synchronization in the neural network by reducing the efficiency of synaptic transmission.

Abstract A031: Leveraging the Meharry-Vanderbilt alliance to develop a faculty affiliate program to expand cross-collaborative research

Abstract Background: Academic institutions struggle to break down research silos and boost interdisciplinary collaboration. The challenge is magnified when multiple partners seek to build research collaboratives. The Meharry-Vanderbilt-Tennessee State Cancer Partnership (MVTCP) is one of the longest-standing Comprehensive Partnerships to Advance Cancer Health Equity (U54 CPACHE) in the nation and includes faculty from 4 partner institutions, including 2 historically black institutions: Meharry Medical College (MMC), Vanderbilt University Medical Center (VUMC), Vanderbilt University, and Tennessee State University. While this partnership has been extremely productive, a 2022 MVTCP strategic planning session indicated opportunities for deeper scientific discovery and community engagement. The Meharry-Vanderbilt Alliance (MVA) is a 20+year collaboration between MMC and VUMC explicitly designed to advance clinical, translational and basic research focused on ending health disparities by developing and supporting mutually beneficial partnerships between academic centers and the community. A mixed-methods approach was employed to ascertain how the MVA could leverage its resources to further enhance the mission of the MVTCP. Methods: Semi-structured interviews were conducted with 10 MVA stakeholders (2 MMC; 5 VUMC, 3 community partners) to identify opportunities to grow cross-institutional collaborations. Emerging themes were used to develop exploratory questions for faculty-centric focus groups. Focus groups with MMC and VUMC faculty were held to elicit barriers to collaboration and explore possible solutions. A faculty survey was designed and deployed among faculty from all 4 MVTCP institutions from April to December 2022. Results: Themes from semi-structured interviews included limited networking opportunities between institutions, need for greater community engagement, and lack of awareness of faculty development resources, scholarly opportunities, and research funding. Focus group participants (6 MMC; 7 VUMC) recommended solutions that included hosting networking events, newsletters to highlight research and community partners, and developing a resource repository. Faculty respondents to the survey (n=52) indicated their willingness to support cross-institutional capacity building by mentoring students (86.5%), connecting research and clinical colleagues (63.5%), and peer mentorship (57.7%). Faculty requested connections to resources and community organizations (82.7%), support for research and grant development (63.5%), and career development opportunities (50%). Conclusion: This project informed the MVA to actualize the Faculty Affiliate Program (FAP). FAP membership is open to faculty across all MVTCP institutions and has grown to over 75 members. This centralized resource hub facilitates collaborative research and fosters cross-institutional engagement. It is hoped that with support from the FAP, the MVTCP will continue to develop more robust cross-institutional collaborations and community partnerships designed to eliminate cancer disparities. Citation Format: Karen M. Winkfield, Jessica L. Jones, Virginia Fuqua-Meadows, Freneka F. Minter, Calandra G. Whitted, Alecia M. Fair. Leveraging the Meharry-Vanderbilt alliance to develop a faculty affiliate program to expand cross-collaborative research [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A031.

A query-response causal analysis of reaction events in biochemical reaction networks

The stochastic kinetics of biochemical reaction networks is described by a chemical master equation (CME) and the underlying laws of mass action. Assuming network-free simulations of the rule-based models of biochemical reaction networks (BRNs), this paper departs from the usual analysis of network dynamics as the time-dependent distributions of chemical species counts, and instead considers statistically evaluating the sequences of reaction events generated from the stochastic simulations. The reaction event-time series can be used for reaction clustering, identifying rare events, and recognizing the periods of increased or steady-state activity. However, the main aim of this paper is to device an effective method for identifying causally and anti-causally related sub-sequences of reaction events using their empirical probabilities. This allows discovering some of the causal dynamics of BRNs as well as uncovering their short-term deterministic behaviors. In particular, it is proposed that the reaction sub-sequences that are conditionally nearly certain or nearly uncertain can be considered as being causally related. Moreover, since the time-ordering of reaction events is locally irrelevant, the reaction sub-sequences can be transformed into the reaction sets or multi-sets. The distance metrics can be then used to define the equivalences among the reaction events. The proposed method for identifying the causally related reaction sub-sequences has been implemented as a computationally efficient query-response mechanism. The method was evaluated for five models of genetic networks in seven defined numerical experiments. The models were simulated in BioNetGen using the open-source network-free simulator NFsim. This simulator had to be modified first to allow recording the traces of reaction events, and it is available in the Github repository, ploskot/nfsim_1.20. The generated event time-series were analyzed with Python and Matlab scripts. The whole process of data generation, analysis and visualization has been nearly fully automated using shell scripts. This demonstrates the opportunities for substantially increasing the research productivity by creating automated data generation and processing pipelines.

74 Setting Students Up for Success in the Agricultural Industry

Abstract When asked about future employment goals, many agricultural students express interest in being employed in the industry following graduation. Even though a large number of students have this career path in mind, students are often not prepared for industry roles. The agricultural industry favors employees who have good soft skills, such as communication, teamwork, and interpersonal skills, as it is often required that employees collaborate within a team or connect with clients and colleagues. Nevertheless, these soft skills are often not taught or are not emphasized enough during undergraduate or graduate education as they are often difficult to teach in a classroom setting. International students face unique challenges when seeking employment as industry professionals. Some of these challenges, such as language barriers or social and cultural differences, can make important tasks, like networking, more difficult. There are opportunities for university faculty to assist students wanting to seek employment in the agricultural industry, such as by organizing networking events and establishing industry-focused classes or lectures. Networking in a professional setting is a vital skill for students trying to enter the industry; therefore, providing students with opportunities to practice networking and professional social skills among their peers and superiors is critical. Courses or lectures focused on understanding the industry and its challenges would also be highly beneficial to students, particularly those who do not come from agricultural backgrounds and those that are international. Encouraging students to take part in student organizations and extension events, and to apply for teaching assistant positions for relevant production classes may also contribute to student preparation for industry employment. By providing students with an overall understanding of the agricultural industry and opportunities to improve skills that are relevant for industry positions, faculty members may be able to improve student preparedness for these roles and assist in making students more marketable to individual companies.

The Role of Chlorine in the Formation and Development of Tap Water Biofilms under Different Flow Regimes.

Water companies make efforts to reduce the risk of microbial contamination in drinking water. A widely used strategy is to introduce chlorine into the drinking water distribution system (DWDS). A subtle potential risk is that non-lethal chlorine residuals may select for chlorine resistant species in the biofilms that reside in DWDS. Here, we quantify the thickness, density, and coverage of naturally occurring multi-species biofilms grown on slides in tap water with and without chlorine, using fluorescence microscopy. We then place the slides in an annular rotating reactor and expose them to fluid-wall shears, which are redolent of those on pipe walls in DWDS. We found that biofilms in chlorine experiment were thicker, denser and with higher coverage than in non-chlorine conditions under all flow regimes and during incubation. This suggests that the formation and development of biofilms was promoted by chlorine. Surprisingly, for both chlorinated and non-chlorinated conditions, biofilm thickness, density and coverage were all positively correlated with shear stress. More differences were detected in biofilms under the different flow regimes in non-chlorine than in chlorine experiments. This suggests a more robust biofilm under chlorine conditions. While this might imply less mobilization of biofilms in high shear events in pipe networks, it might also provide refuge from chlorine residuals for pathogens.

The NAVICARE Network for patient-oriented health services research - experiences and lessons learned

Abstract Background In 2017 NAVICARE was established as a Berlin/Brandenburg-based network for patient-oriented health services research. The overall aim is to reduce barriers for patients with age-associated diseases and multimorbidity in the fragmented German health care system. The networks structural goal was to develop a research environment in which scientifically developed regional health services interventions for better coordinated care are piloted. Methods During two funding periods, an overarching infrastructure was built in the network to connect and support participating researchers as well as external stakeholders from different disciplines, support education and coordinate the network's dissemination and events. In a critical reflection process between participating researchers, we collected experiences and lessons learned from work in the multidisciplinary research network. Results A shared coordinating infrastructure, including monthly meetings, to bring in different competencies, perspectives, methods and research questions has been described as beneficial. This includes expertise from participating researchers as well as connected external partners. Experienced challenges are high time effort of networking, bringing together different views as well as logistical-organizational barriers in building overarching and permanent structures. Conclusions Work in a research network can provide structural support by collating information and expertise to support better health services research and allowing the integration of different perspectives from internal as well as external partners involved in health care. An overarching coordinating infrastructure should provide an effective flow of information and connect all partners in a time efficient manner. Key messages • Multidisciplinary networking can support a patient-oriented health services research environment. • However, for the regional network to be efficient a goal-oriented coordination and time-balanced communication is key.

Role Of Institution In Field Of Entrepreneurial Development

Role Of Institution In The Field Of Entrepreneurial Development Abstract:- Institutions play a crucial role in the field of entrepreneurial development by providing a supportive ecosystem for entrepreneurs to thrive and succeed. This abstract will highlight the key roles institutions play in fostering entrepreneurship. Entrepreneurial development institutions, such as business incubators, accelerators, universities, government agencies, and non-profit organizations, act as catalysts in the entrepreneurial ecosystem. They provide various resources, guidance, and support to aspiring entrepreneurs, startup companies, and small businesses. Firstly, institutions offer education and training programs to enhance entrepreneurial skills and knowledge. They provide workshops, courses, and mentoring opportunities to equip individuals with the necessary business acumen, management techniques, and industry-specific expertise. These educational initiatives help entrepreneurs understand market dynamics, develop viable business models, and improve their chances of success. Secondly, institutions facilitate access to funding and financial resources. They connect entrepreneurs with potential investors, venture capitalists, and angel investors who are willing to provide capital to fuel business growth. Institutions may also offer grants, loans, or equity-based financing options to support entrepreneurs in their early stages when traditional funding may be challenging to obtain. By bridging the financial gap, institutions help entrepreneurs turn their innovative ideas into viable business ventures. Furthermore, institutions provide networking and collaboration opportunities. They organize networking events, conferences, and forums where entrepreneurs can connect with like-minded individuals, industry experts, and potential business partners. These platforms enable entrepreneurs to exchange ideas, share experiences, and forge valuable partnerships that can contribute to their business success. Institutions also play a crucial role in facilitating access to markets. They provide entrepreneurs with market intelligence, research, and guidance on market entry strategies. Additionally, they help entrepreneurs navigate regulatory frameworks, intellectual property rights, and other legal aspects, reducing barriers to market entry and facilitating business growth. Moreover, institutions foster a culture of innovation and risk-taking. They create an environment that encourages entrepreneurship, celebrates entrepreneurial success, and provides support during setbacks. Institutions promote an entrepreneurial mindset by organizing competitions, awards, and recognition programs that motivate individuals to pursue entrepreneurial opportunities. In summary, institutions in the field of entrepreneurial development play a multifaceted role in supporting and nurturing entrepreneurial ventures. They provide education, training, funding, networking, market access, and a conducive environment for entrepreneurs to thrive. By offering comprehensive support, institutions contribute to the growth of innovative startups, job creation, economic development, and societal progress.

4th Plant Microbiome Symposium

After successful editions in Sao Paulo, Amsterdam, and Dundee, and due to the great importance of the theme, Universidad San Francisco de Quito USFQ is proud to host the 4th Plant Microbiome Symposium in Quito, Ecuador, from August 1st to August 4th, 2023. The 4th edition of the Plant Microbiome Symposium is this year’s premier opportunity to connect with plant-microbiome colleagues from all over the world, where you can take advantage of outstanding plenary speakers, multiple networking events, poster fairs and receptions, in the conference and exhibition halls at the USFQ campus in Cumbaya in the valley situated East of Quito.

Random Number Generation Based on Heterogeneous Entropy Sources Fusion in Multi-Sensor Networks.

The key system serves as a vital foundation for ensuring the security of information systems. In the presence of a large scale of heterogeneous sensors, the use of low-quality keys directly impacts the security of data and user privacy within the sensor network. Therefore, the demand for high-quality keys cannot be underestimated. Random numbers play a fundamental role in the key system, guaranteeing that generated keys possess randomness and unpredictability. To address the issue of random number requirements in multi-sensor network security, this paper introduces a new design approach based on the fusion of chaotic circuits and environmental awareness for the entropy pool. By analyzing potential random source events in the sensor network, a high-quality entropy pool construction is devised. This construction utilizes chaotic circuits and sensor device awareness technology to extract genuinely random events from nature, forming a heterogeneous fusion of a high-quality entropy pool scheme. Comparatively, this proposed scheme outperforms traditional random entropy pool design methods, as it can meet the quantity demands of random entropy sources and significantly enhance the quality of entropy sources, ensuring a robust security foundation for multi-sensor networks.

FedChain-Hunter: A reliable and privacy-preserving aggregation for federated threat hunting framework in SDN-based IIoT

In the development of the Industrial Internet of Things (IIoT), cyber threats and attacks have become major issues and concerns in Industry 4.0 due to the negative impacts on the infrastructures and services across organizations. Nevertheless, due to the issues in preserving privacy and transparency, there is a lack of threat intelligence sharing among parties, leading to the low performance in uncovering malicious actors. In fact, the method of gathering and exploiting such data has been getting more crucial in a trend of machine learning (ML) adoption in cybersecurity. In this scenario, Federated Learning (FL) can assume a significant role in constructing an ML-based threat hunting solution for IIoT networks. This can be achieved by harnessing data resources from diverse parties, utilizing a local training strategy that eliminates the need for centralized data collection. Hence, this paper proposes FedChain-Hunter, a blockchain and FL-based threat-hunting framework to mutually seek cyber threats while ensuring data privacy and the transparency in the contribution of data owners. Specifically, Software Defined Networking (SDN) with programmable and flexible security orchestration is used to easily monitor and gather appropriate security events in the IIoT network. In addition, the Fully Homomorphic Encryption (HE) and Differential Privacy (DP) are integrated into the FL scheme to provide strong security and privacy-preserving aggregation for each ML model update. Also, the blockchain adoption offers the transparency, auditability for collaboration and contribution management through a decentralized platform. The experimental results on 5 datasets indicate that FedChain-Hunter can achieve high performance for cyber threat detection with security, reliability, and privacy guarantee.

An Analysis of Temporal Features in Multivariate Time Series to Forecast Network Events

Analyzing network traffic over time is crucial for understanding the changes in network activity. To properly examine network traffic patterns over time, multiple network events in each timestamp need to be converted to time series data. In this study, we propose a new approach to transform network traffic data into time series formats by extracting temporal features to analyze normal/attack patterns. The normal patterns indicate network traffic occurred without any intrusion-related activities, whereas the attack patterns denote potential threats that deviate from the normal patterns. To evaluate the features, long short-term memory (LSTM) is applied to forecast multi-step network normal and attack events. Visual analysis is also performed to enhance the understanding of key features in the network. We compared the performance differences using time scales of 60 and 120 s. Upon evaluation, we found that the temporal features extracted with the 60 s time scale exhibited better performance in forecasting future network events.

The pros and cons of virtual networking events: online exploratory survey of psychiatrists' opinions.

We conducted an online questionnaire-based cross-sectional study to clarify psychiatrists' perspectives on virtual networking events. We compared two groups of respondents: those who had participated in virtual networking events (experienced group, n = 85) and those who had not (inexperienced group, n = 13). The experienced group had a greater level of agreement than the inexperienced group that virtual events were generally useful and helped with forming professional relationships and improving professional skills. Respondents in the experienced group considered the ease of participation and low financial burden to be advantages of virtual networking meetings and difficulties in building friendships and socialising to be disadvantages.