Computer Networks Research Articles

A socio-cognitive perspective of knowledge integration in digital innovation networks

Digital innovation is a complex process in which actors seek to create new value pathways by combining digital resources in a layered modular architecture. While IS scholarship has a rich tradition of research on developing and implementing digital artefacts within intra-organisational contexts, our understanding of knowledge integration across distributed innovation networks is nascent and under-theorised. This is an important area of research given the rising importance of digital innovation networks and the challenges faced in integrating specialised knowledge, especially given the greater diversity, speed, reach, and scope made possible by digital technologies. Drawing on in-depth case study findings from a health IoT project involving multiple organisations and disciplines, we explore how knowledge is integrated across boundaries during the initiation stage of a digital innovation network. Our findings point to boundaries related to the digital platform’s organising vision, resource allocation, delivery roadmap, technical architecture, and intellectual property, to name but a few challenges. We then reveal five socio-cognitive modes of knowledge integration which actors strategically enact to cross syntactic, semantic, and pragmatic boundaries: Signalling, Assembling, Contesting, Discounting, and Finalising. The choice of mode depends on the perceived knowledge status (‘what they know’) and social status (‘who they are’) of network actors, which highlight the salience of both social and cognitive dependencies for knowledge integration. We further discuss the contribution of design objects for overcoming differences and distinctions between specialist actors in a digital innovation network.

Role of Linear Diophantine Equations in RSA Encryption

Abstract. Diophantine equations are mathematical equations that include only integer solutions and two or more unknown variables. To illustrate, the most elementary form of Diophantine equations are linear Diophantine equations when two different one-degree monomials added up to a constant value. Such equations were given the name of the extraordinary Greek mathematician Diophantus who made great contribution to algebra and number theory. In addition, they help in defining concepts in algebraic geometry and contribute to the development of algorithms for cryptography. They work in the same way as public keys do in cryptocurrencies within blockchain technology to curb cyber threats and scams targeting public systems. They serve as a means of securing transactions on a computer network when using cryptocurrencies such as bitcoins. These encryption techniques also permit cryptographers and mathematicians to create new ideas in relation to these number systems, thus making it possible for further cryptographic techniques to be put into place.

Reverse-Engineered Exact Control of Population Transfer in Lossy Nonlinear Three-State Systems

We introduce a reverse-engineered scheme for achieving the precise control of population transfer in nonlinear quantum systems characterized by a 1:2 resonance. This scheme involves the use of two resonant laser pulses that transition from initial and final states to an intermediate level exhibiting irreversible losses. In comparison to alternative techniques, our approach offers computational efficiency advantages. Notably, the analytically defined form of the pump pulse enables tailored control strategies, enhancing robustness against decoherence and imperfections. This flexibility extends to choosing dump pulses and designing time evolution scenarios. These features open doors for practical implementation and scalability in quantum technologies, with potential applications in quantum information processing, quantum computing, and quantum communication.

Blockchain-based fast handover authentication protocol for Internet of Vehicles in small industrial parks

Autonomous vehicles safeguard the security and efficiency of Internet of Vehicles systems in small industrial parks by authenticating and exchanging real-time information with transportation infrastructure. Deploying a multi-server framework reduces the risk of message blocking and privacy information leakage from centralized services. However, in traditional handover authentication protocols, there are still potential security risks such as high-overhead performance issues and single point of failure attacks. Therefore, it is considered challenging to realize efficient authentication while protecting the privacy of vehicles. In this paper, we propose a secure and efficient handover authentication protocol for autonomous vehicles in a small industrial park to address the challenges. The protocol is based on blockchain and Pedersen verifiable secret sharing scheme, which not only ensures lightweight real-time interactions between autonomous vehicles and edge servers in multi-server environments, but also strictly protects the security and privacy of both vehicles and edge servers. We prove the semantic security of the protocol under the Real-Or-Random model and perform a informal analysis of its security attributes to show that it can withstand a wide range of malicious attacks. Performance evaluation shows that the proposed protocol satisfies more security requirements and has better computational efficiency and communication cost than other related protocols.

Latency Reduction in Immersive Systems through Request Scheduling with Digital Twin Networks in Collaborative Edge Computing

Immersive systems, which offer immersive interactive experiences by tightly coupling the real and virtual worlds, encompassing Augmented Reality (AR) and Virtual Reality (VR), have undergone rapid development in recent years. The inherent latency issue of immersive systems poses considerable challenges for AR/VR applications that are highly sensitive to delay. To address this challenge, we propose utilizing edge collaboration as an efficient means to schedule AR/VR requests, owing to the inherent ability of edge nodes to seamlessly coordinate within the same geographical region and optimally allocate request resources to minimize latency. However, when it comes to edge nodes across different regions, a significant hurdle emerges. These nodes often lack real-time knowledge about cached resources across other regions, making cross-regional edge collaboration for AR/VR request scheduling an extremely challenging problem. We formally define and prove the NP-hardness of this problem. To tackle this complexity, we introduce a Federated Digital Twin Model Construction (FedDT), which establishes a global digital twin network connecting different edge nodes. This network simulates and reflects the resource status and availability of each edge node in real time. Subsequently, we devise an extended Dijkstra algorithm to determine the shortest path between AR/VR requests and edge nodes, taking into account current network conditions to optimize service delay. Both theoretical analysis and experimental results confirm that compared with baseline methods, our proposed solution consistently achieves an average delay reduction of 36.78% across various scenarios, effectively demonstrating its superior performance.

Enhancing Network Threat Detection with Random Forest-Based NIDS and Permutation Feature Importance

Network Intrusion Detection Systems (NIDS) are critical for protecting computer networks from unauthorized activities. Traditional NIDS rely on rule-based signatures, which can be limiting in detecting emerging threats. This study investigates the effectiveness of the random forest classifier in advancing NIDS capabilities through machine learning. Using the CICIDS-2017 dataset, the data are preprocessed to enhance their quality by removing redundancies. feature selection and permutation importance were employed to identify the most relevant features. The methodology involves rigorous testing and analysis of the random forest classifier’s performance, focusing on f1-score rates compared to other machine learning models. Results demonstrate that by optimizing class weights, applying a custom prediction function and leveraging 26 key features, the random forest classifier achieves an outstanding 99.8% in the weighted f1-score and 93.31% in the macro f1-score in various attack types. This research highlights the potential of machine learning to significantly enhance NIDS effectiveness, offering a robust defense mechanism against evolving cybersecurity threats in modern networks.

Explainable AI-based innovative hybrid ensemble model for intrusion detection

AbstractCybersecurity threats have become more worldly, demanding advanced detection mechanisms with the exponential growth in digital data and network services. Intrusion Detection Systems (IDSs) are crucial in identifying illegitimate access or anomalous behaviour within computer network systems, consequently opposing sensitive information. Traditional IDS approaches often struggle with high false positive rates and the ability to adapt embryonic attack patterns. This work asserts a novel Hybrid Adaptive Ensemble for Intrusion Detection (HAEnID), an innovative and powerful method to enhance intrusion detection, different from the conventional techniques. HAEnID is composed of a string of multi-layered ensemble, which consists of a Stacking Ensemble (SEM), a Bayesian Model Averaging (BMA), and a Conditional Ensemble method (CEM). HAEnID combines the best of these three ensemble techniques for ultimate success in detection with a considerable cut in false alarms. A key feature of HAEnID is an adaptive mechanism that allows ensemble components to change over time as network traffic patterns vary and new threats appear. This way, HAEnID would provide adequate protection as attack vectors change. Furthermore, the model would become more interpretable and explainable using Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). The proposed Ensemble model for intrusion detection on CIC-IDS 2017 achieves excellent accuracy (97-98%), demonstrating effectiveness and consistency across various configurations. Feature selection further enhances performance, with BMA-M (20) reaching 98.79% accuracy. These results highlight the potential of the ensemble model for accurate and reliable intrusion detection and, hence, is a state-of-the-art choice for accuracy and explainability.

Landslide Monitoring and Warning System Based on Wireless Sensor Network

Landslides are common in Indonesia, often caused by factors like heavy rainfall and high soil moisture levels. The Batubrak mini hydro power plant intake area in Lampung province of Indonesia has experienced landslides, prompting the need for an early warning system. This study presents the development of a cost-effective and efficient real-time landslide monitoring and warning system. The system incorporates sensors to measure accelerometer and vibration parameters, enabling real-time data display through a wireless sensor network and SCADA software. Early warnings are issued when triggers are activated. Three sensor nodes were deployed in the intake area for testing, demonstrating their ability to detect, collect, process, and transmit accelerometer and vibration data to the gateway node.

Participatory research with youth with disabilities: Experiences from sub-Saharan Africa.

Disability inclusive youth research, involving youth with disabilities in the design, implementation and dissemination of study data, is still limited in Africa. To describe and reflect on the experiences of involving youth with disabilities in an exploratory research study, focused on disability-inclusive education and employment in 7 African countries. 12 youths with different impairments, aged 18 to 35, were employed as researchers in Ethiopia, Ghana, Kenya, Nigeria, Rwanda, Senegal and Uganda. Youth researchers contributed to the data collection and analysis of interviews with 210 youth with disabilities. 24 youth advisors with disabilities formed two youth advisory groups (YAG) of 12 advisors each in the regional hub countries Ghana and Uganda. The YAGs met 4 times during the project and contributed to the study design, data collection, data analysis and dissemination activities. In addition, 4 workshops were held with the Ugandan YAG to develop a participatory film. Together with the youth participants, we reflected on the experiences of involving youth with disabilities and conducting research with, by and on youth with disabilities. We highlighted ethics and safeguarding, recruitment and representation, exploring experiences and data quality, participatory dissemination, accessibility, capacity building and networking as key areas of consideration and benefit in this project. Participatory research with youth with disabilities is feasible, enriching, and key to inclusive research that informs education and employment policy and practices. Lessons learned from youth involvement in a disability inclusive research programme, focused on education and employment in 7 African countries.

Botnets Unveiled: A Comprehensive Survey on Evolving Threats and Defense Strategies

ABSTRACTBotnets have emerged as a significant internet security threat, comprising networks of compromised computers under the control of command and control (C&C) servers. These malevolent entities enable a range of malicious activities, from denial of service (DoS) attacks to spam distribution and phishing. Each bot operates as a malicious binary code on vulnerable hosts, granting remote control to attackers who can harness the combined processing power of these compromised hosts for synchronized, highly destructive attacks while maintaining anonymity. This survey explores botnets and their evolution, covering aspects such as their life cycles, C&C models, botnet communication protocols, detection methods, the unique environments botnets operate in, and strategies to evade detection tools. It analyzes research challenges and future directions related to botnets, with a particular focus on evasion and detection techniques, including methods like encryption and the use of covert channels for detection and the reinforcement of botnets. By reviewing existing research, the survey provides a comprehensive overview of botnets, from their origins to their evolving tactics, and evaluates how botnets evade detection and how to counteract their activities. Its primary goal is to inform the research community about the changing landscape of botnets and the challenges in combating these threats, offering guidance on addressing security concerns effectively through the highlighting of evasion and detection methods. The survey concludes by presenting future research directions, including using encryption and covert channels for detection and strategies to strengthen botnets. This aims to guide researchers in developing more robust security measures to combat botnets effectively.

Autonomous Attack Mitigation Through Firewall Reconfiguration

ABSTRACTPacket filtering firewalls represent a main defense line against cyber attacks that target computer networks daily. However, the traditional manual approaches for their configuration are no longer applicable to next‐generation networks, which have become much more complex after the introduction of virtualization paradigms. Some automatic strategies have been investigated in the literature to change that old‐fashioned configuration approach, but they are not fully autonomous and still require several human interventions. In order to overcome these limitations, this paper proposes an autonomous approach for firewall reconfiguration where all steps are automated, from the derivation of the security requirements coming from the logs of IDSs to the deployment of the automatically computed configurations. A core component of this process is React‐VEREFOO, which models the firewall reconfiguration problem as a Maximum Satisfiability Modulo Theories problem, allowing the combination of full automation, formal verification, and optimization in a single technique. An implementation of this proposal has undergone experimental validation to show its effectiveness and performance.

A Hybrid Approach for Intrusion Detection by Dragonfly Optimization and Bayesian Neural Network Methodologies

The technique of keeping an eye out for malicious activity or policy violations on a computer network or system is known as intrusion detection. It entails examining system logs, network traffic, and other data sources to spot potential security breaches, misuse, and illegal access. Network-based and host-based intrusion detection systems (IDS) are the two types available. The former analyzes network traffic and packets, while the latter concentrates on specific hosts or devices. This paper investigates how to improve intrusion detection systems in cyber security by combining dragonfly optimization (DFO) and Bayesian neural network (BNN) methodologies. The NSL-KDD dataset, which is tested and trained in the system, is the source of the dataset used in the study. By utilizing the optimization skills of DFO and the probabilistic reasoning powers of BNN, the hybrid technique improves the accuracy and flexibility of detecting and addressing network intrusions. This research illustrates the effectiveness of the suggested methodology in navigating dynamic network environments and identifying emerging dangers through a thorough examination. In addition to improving detection performance, the integration of BNN and DFO gives enterprises insightful information about infiltration patterns and the ability to take proactive measures to thwart hostile activity. The created model's enhanced intrusion detection performance is demonstrated by the experimental findings. These results are validated in terms of accuracy, precision, recall, execution time, and F1 score by comparing them with models that are currently in use.

Integrating Artificial Intelligence into Cybercrime Investigation: Challenges and Future Directions

Computer and social networking whereby criminals use the Internet to propagate criminal activities are some of the major challenges faced by existing policing strategies. Modern-day crimes include hacking into computer systems and stealing money from consumers, ransomware, identity theft cases, and hacking, all of which use the dark web and encryption. In this regard, artificial intelligence (AI) is the most efficient solution for improving the manner of cybercrime investigation. This paper also analyses how AI technologies such as machine learning natural language processing, and deep learning can be incorporated in cybercrime investigations and how they can assist in dealing with difficulties concerning data volume, complexity, and encryption. The advantages of utilizing AI are numerous from pattern recognition to repetitive tasks cutting down the investigation time. However, the paper recognizes that applying AI in business brings legal, technical, and ethical concerns including; privacy, bias, and legal constrictions. This research analyses existing legal frameworks of India, the EU, and the United States while looking at how it would be possible to incorporate AI into cybercrime investigations without violating the rights of a citizen. Further, it reveals infringement and possible bias, as well as unlawful use for violations, and recounts drawbacks related to the lack of resources and expertise that police departments confront. In the final section of the paper, directions for future research focusing on the use of AI in the fight against cybercrime are given in addition to that, the practice of cooperation with different countries, legal regulation of such activities, protection of ethical issues, and training of personnel are described. They are useful in making sure that the levels of artificial intelligence benefits are achieved fully without compromising security and the basic rights of an individual

Heterogeneous multi-task allocation algorithm based on multi-round distributed auction

Aiming at the distributed decision-making needs of different combat tasks such as multi-machine collaborative detection, attack, and evaluation, a multi-round distributed auction algorithm for heterogeneous tasks with time constraints is established. The algorithm flexibly solves the distributed collaborative task allocation problem with complex constraints between drones with different capabilities. In the distributed task allocation framework, each drone can realize distributed asynchronous computing and asynchronous communication, and can ensure good solution efficiency and accuracy. The task allocation principle is mainly based on the nearest allocation, while taking into account the target value and time constraints, realizing the priority allocation of important targets under limited resources and making the total journey as short as possible. Simulation results show that: given any number of drones and targets and the configuration of drone combat capabilities, the heterogeneous multi-round distributed auction algorithm can obtain good allocation results.

Privacy enhanced secure compact attribute-based signature from MQ problem for monotone span program

The rapid advancement of computer networks has led to an increase in the exposure of messages within an open environment (cloud). Therefore, the confidentiality of the user's signing information is extremely essential to handle unauthorized access and alterations. Attribute-based signature (ABS) scheme is a significant primitive that ensures the privacy of the user's signing information. To endorse a message, the signer can generate a signature with his/her attributes that satisfy a policy without revealing any other information. Post-quantum attribute-based signature schemes are attractive construction options whose safety do not collapse in presence of quantum computers. This article presents the first attribute-based signature scheme in multivariate quadratic (MQ) setting. To generate the secret signing key, the salted-UOV technique is employed in our protocol. While signing, the well-known 3-pass identification protocol is coupled with the Fiat-Shamir transformation. More positively, our candidate relies on presenting the policy as a monotone span program. We also study existential unforgeability and the perfect privacy feature which ensures that a signature cannot be linked to any signing information. Moreover, our scheme is compact in the sense that it performs efficiently in terms of storage when contrasted to the existing post-quantum attribute-based signature schemes.

POI VizNet: New QGIS Tool to construct Visibility Networks in Cities

Abstract. Network science and graph theory offer attractive models to describe urban phenomena and examine cases of urban planning and design. However, none of the existing network analysis tools consider simultaneously human visual perception and locations of urban activities. In this paper we introduce POI VizNet, a new QGIS plug-in that constructs various 2-dimentianal undirected graphs of unobstructed lines of sight. The plug-in integrates an increasing amount of available GIS-based data of Point Of Interest (POI) and visibility into one readily accessible analytical framework. Graphs are created between two types of decision points during urban travel – street intersections and POIs, origins, and destinations of travel, by connecting these potential observer’s decision locations in an open space between buildings. In addition, POI VizNet provides advanced options to build graphs using a predefined viewing distance and perceptual perspective. Visibility graphs are constructed and visualised as new layers in QGIS and delivered as network files suitable for further exploration, analysis, and visualisation in various network software packages.

DIGITAL TRANSFORMATION AS A TRIGGER FOR MODIFICATION OF DEVELOPMENT STRATEGIES AND BUSINESS MODELS OF COMPANIES IN THE CONTEXT OF THE FORMATION OF GLOBAL DIGITAL PLATFORMS AND HYPERCOMPETITION

The article describes the problem that in the context of digital transformation enterprises, especially those with a traditional business model, face both great opportunities and challenges. The competition is intensifying, turning into hyper-competition, which implies the search for new tools to win customer loyalty and accelerate innovation processes. The article provides a generalization of adequate strategies for the development of enterprises based on the use of information technology, studying the effects of digital transformation on competition and on various forms of digital inequality at the micro and meso levels, including the background of the proliferation of global digital platforms. Digital transformation involves 3 stages - digitization, digitalization, digital transformation itself; it involves changes in enterprise strategies, organizational structure, information technology, supply chains, marketing, development of own digital platforms, market diversification, co-creation. For a successful digital transformation, companies need to build a fundamentally new set of assets, including IT infrastructure, Big Data, AI, IoT and blockchain, digital flexibility, digital network capabilities, cloud solutions, etc. Different types of companies have their own peculiarities of digital transformation. In a hypercompetitive environment, it is more difficult to maintain the company's performance trends due to the change in competition patterns to rivalry patterns, which are characterized by shorter product life cycles and an accelerated pace of innovation. This means that hypercompetition reduces the ability of firms to create sustainable competitive advantages, calling into question, if not the validity, then at least the usefulness of using a resource-based strategy. This thesis is especially important in the context of digital transformation, because it quickly depreciates the strategic assets of traditional companies. A global challenge for all businesses is the development of digital platforms that have evolved into global social and technical intermediaries and business organizations that not only connect users, but also provide the basic infrastructure for doing business. Their control over access to technology allows them to create global networks for the transfer of data and capital, controlling access to important resources, while strengthening their dominance through increased control over technology and data, which digital platforms turn into a key asset that drives the financialization process. Digital platforms are contributing to the globalization of competition between employees and governments and increasing hypercompetitive conditions, which is challenging companies with traditional business models to survive and grow, as the risks of massive bankruptcies increase, which in turn exacerbates digital inequality at both the micro and meso levels.

Ejection of Real-Time Malicious Intrusion and Attacks in IoT Empowered Infrastructure

The project recognizes the pervasive threat of computer viruses, malware, and hostile attacks on computer networks, highlighting the critical role of intrusion detection as a proactive defense technology. The project introduces a novel approach based on deep learning to identify and mitigate cybersecurity vulnerabilities and breaches in IoT-driven cyber-physical systems, aiming for enhanced security measures. The project's objective is to elevate intrusion detection beyond the limitations of traditional systems by addressing issues like accuracy, detection effectiveness, and reducing false positives. This emphasizes the advancement and innovation in cybersecurity. To achieve the project's goals, the method employs a generative adversarial network, a cutting-edge deep learning technique. Additionally, it distinguishes itself by contrasting unsupervised and deep learning-based discriminative approaches, showcasing a comprehensive and effective approach to cybersecurity. In our project, we successfully implemented an ensemble method to boost predictive accuracy by integrating multiple individual models. Particularly noteworthy is the inclusion of a hybrid architecture, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), denoted as CNN+LSTM. This hybrid model achieved an impressive accuracy of 99% when applied to the KDD-Cup dataset, underscoring the efficacy of our ensemble technique for intrusion detection in IoT- based cybersecurity infrastructures.

Photonic neuromorphic accelerators for event-based imaging flow cytometry

In this work, we present experimental results of a high-speed label-free imaging cytometry system that seamlessly merges the high-capturing rate and data sparsity of an event-based CMOS camera with lightweight photonic neuromorphic processing. This combination offers high classification accuracy and a massive reduction in the number of trainable parameters of the digital machine-learning back-end. The event-based camera is capable of capturing 1 Gevents/sec, where events correspond to pixel contrast changes, similar to the retina’s ganglion cell function. The photonic neuromorphic accelerator is based on a hardware-friendly passive optical spectrum slicing technique that is able to extract meaningful features from the generated spike-trains using a purely analogue version of the convolutional operation. The experimental scenario comprises the discrimination of artificial polymethyl methacrylate calibrated beads, having different diameters, flowing at a mean speed of 0.1 m/sec. Classification accuracy, using only lightweight digital machine-learning schemes has topped at 98.2%. On the other hand, by experimentally pre-processing the raw spike data through the proposed photonic neuromorphic spectrum slicer at a rate of 3 × 106 images per second, we achieved an accuracy of 98.6%. This performance was accompanied by a reduction in the number of trainable parameters at the classification back-end by a factor ranging from 8 to 22, depending on the configuration of the digital neural network. These results confirm that neuromorphic sensing and neuromorphic computing can be efficiently merged to a unified bio-inspired system, offering a holistic enhancement in emerging bio-imaging applications.

Research on a cloud model intelligent computing platform for water resource management

ABSTRACT As the demand for water management information systems continues to increase, addressing issues such as poor generalizability, low reusability, and difficulties in updating and maintaining water resource planning cloud model service platforms becomes crucial. To achieve goals like business-oriented functionality, high availability, and reliability, this study proposes constructing a cloud model service platform for basin water resource planning based on cloud computing technology and business workflows. This study couples water cycle models with multi-objective optimization models for water resource allocation, using digital topological water networks to achieve dynamic regional water resource allocation. The cloud service platform adopts a business-oriented modeling method based on B/S development architecture. This paper takes the Weihe River Basin as an example to simulate and analyze the evolution of the water cycle pattern and optimize the annual water resources allocation plan. Results show that: (1) the water cycle model of the cloud model service platform can better describe the runoff change process in the verification period; (2) through the cloud platform service model, the water shortage rate of the Weihe River Basin in 2025 is 7.95%. The research findings provide technical references and insights for intelligent water management and refined allocation of water resources in the Weihe River Basin.