Toward Intent-Based Network Management: Intent-Optimized Cross-Shard Transactions and Malicious Node Detection in Blockchain System

Intent-driven networks are an essential stepping stone in the evolution of network and service management towards a truly autonomous paradigm. User centric intents provide an abstracted means of impacting the design, provisioning, deployment and assurance of network infrastructure and services with the help of service level agreements and minimum network capability exposure. The concept of Intent Based Networking (IBN) poses several challenges in terms of the contextual definition of intents, role of different stakeholders, and a generalized architecture. In this review, we provide a comprehensive analysis of the state-of-the-art in IBN including the intent description models, intent lifecycle management, significance of IBN and a generalized architectural framework along with challenges and prospects for IBN in future cellular networks. An analytical study is performed on the data collected from relevant studies primarily focusing on the inter-working of IBN with softwarized networking based on NFV/SDN infrastructures. Critical functions required in the IBN management and service model design are explored with different abstract modeling techniques and a converged architectural framework is proposed. The key findings include: (1) benefits and role of IBN in autonomous networking, (2) improvements needed to integrate intents as fundamental policies for service modeling and network management, (3) need for appropriate representation models for intents in domain agnostic abstract manner, and (4) need to include learning as a fundamental function in autonomous networks. These observations provide the basis for in-depth investigation and standardization efforts for IBN as a fundamental network management paradigm in beyond 5G cellular networks.

Federated Edge Learning (FEL) allows edge nodes to train a global deep learning model collaboratively for edge computing in the Industrial Internet of Things (IIoT), which significantly promotes the development of Industrial 4.0. However, FEL faces two critical challenges: communication overhead and data privacy. FEL suffers from expensive communication overhead when training large-scale multi-node models. Furthermore, due to the vulnerability of FEL to gradient leakage and label-flipping attacks, the training process of the global model is easily compromised by adversaries. To address these challenges, we propose a communication-efficient and privacy-enhanced asynchronous FEL framework for edge computing in IIoT. First, we introduce an asynchronous model update scheme to reduce the computation time that edge nodes wait for global model aggregation. Second, we propose an asynchronous local differential privacy mechanism, which improves communication efficiency and mitigates gradient leakage attacks by adding well-designed noise to the gradients of edge nodes. Third, we design a cloud-side malicious node detection mechanism to detect malicious nodes by testing the local model quality. Such a mechanism can avoid malicious nodes participating in training to mitigate label-flipping attacks. Extensive experimental studies on two real-world datasets demonstrate that the proposed framework can not only improve communication efficiency but also mitigate malicious attacks while its accuracy is comparable to traditional FEL frameworks.

Intent-based networking (IBN) has emerged as a transformative paradigm in network management, revolutionizing how networks are configured, monitored, and secured. By leveraging artificial intelligence (AI) and machine learning (ML), IBN translates high-level business objectives into automated network configurations, ensuring that operational intents are consistently achieved. This paper explores the profound impact of IBN on network configuration management and security. Firstly, we examine how IBN streamlines network configuration through automation, reducing manual intervention and mitigating configuration errors, which are among the leading causes of network outages. IBN’s ability to validate intents against real-time network states ensures that configurations align with business policies, enabling agile and reliable network operations. Secondly, the role of IBN in enhancing network security is analyzed. By continuously monitoring network behavior against predefined intents, IBN systems can detect and respond to anomalies or potential threats in real time. This proactive approach minimizes the window of vulnerability and ensures compliance with security policies. Furthermore, the use of AI-driven insights facilitates predictive threat management and adaptive security measures. Finally, we discuss the challenges and future prospects of adopting IBN, including the integration with legacy systems, the reliance on accurate intent definitions, and the need for robust AI models. The findings underscore that IBN not only simplifies network management but also fortifies network defenses, making it a cornerstone of modern, resilient network architectures.

False messages sent by malicious or selfish vehicle nodes will reduce the operation efficiency of the Internet of Vehicles, and can even endanger drivers in serious cases. Therefore, it is very important to detect malicious vehicle nodes in the network in a timely manner. At present, the existing research on detecting malicious vehicle nodes in the Internet of Vehicles has some problems, such as difficulties with identification and a low detection efficiency. Blockchain technology cannot be tampered with or deleted and has open and transparent characteristics. Therefore, as a shared distributed ledger in decentralized networking, blockchain can promote collaboration between transactions, processing and interaction equipment, and help to establish a scalable, universal, private, secure and reliable car networking system. This paper puts forward a block-network-based malicious node detection mechanism. Using blockchain technology in a car network for malicious node identification algorithm could create a security scheme that can ensure smooth communication between network vehicles. A consensus on legal vehicle identification, message integrity verification, false message identification and malicious vehicle node identification form the four parts of the security scheme. Based on the public–private key mechanism and RSA encryption algorithm, combined with the malicious node identification algorithm in the Internet of Vehicles, the authenticity of the vehicle’s identity and message is determined to protect the vehicle’s security and privacy. First, a blockchain-based, malicious node detection architecture is constructed for the Internet of vehicles. We propose a malicious node identification algorithm based on the blockchain consensus mechanism. Combined the above detection architecture with the consensus mechanism, a comprehensive and accurate verification of vehicle identity and message authenticity is ensured, looking at the four aspects of vehicle identification, accounting node selection, verification of transmission message integrity and identification of the authenticity of transmission messages. Subsequently, the verification results will be globally broadcast in the Internet of Vehicles to suppress malicious behavior, further ensure that reliable event messages are provided for the driver, improve the VANET operation environment, and improve the operation efficiency of the Internet of Vehicles. Comparing the proposed detection mechanism using simulation software, the simulation results show that the proposed blockchain-based trust detection mechanism can effectively improve the accuracy of vehicle node authentication and identification of false messages, and improve network transmission performance in the Internet of Vehicles environment.

Detection of malicious nodes in the internet of things (IoT) network consumes power, which is one of the main constraints of the IoT network performance. To evaluate the energy-security trade-off for malicious node detection, this paper proposes an Arduino-based system for dependent malicious nodes (DMN) detection. The experimental work using Arduino and radio frequency (RF) modules was implemented to detect dependent malicious nodes in an IoT network. The detection algorithms were evaluated in terms of energy efficiency. The experiment comprises a coordinator node with five sensor nodes and varying malicious nodes. The results assess the detection algorithms in terms of distinguishing between normal and malicious behaviors and their impact on energy efficiency. The experiment demonstrated that the detection system could identify the malicious nodes. Additionally, the effect of increasing the number of sensors or malicious nodes on the suggested detection algorithm’s energy usage is evaluated.

Security threaten is the primary issue in mobile ad hoc networks (MANET). The efficiency of the MANET system is affected by presence of malicious nodes. It is very difficult task to identify the malicious nodes from the trusty nodes in MANET system due to similar characteristics between malicious and trusty node. This paper proposes an efficient feature extraction based malicious node detection system using adaptive neuro fuzzy inference system (ANFIS) classification approach. In this paper, trust function features and service trust features are extracted from trusty and malicious nodes. These extracted features are trained and classified using ANFIS classifier. The performance of the proposed malicious node detection in MANET system is analyzed in terms of throughput, average packet loss ratio, energy consumption and detection ratio.

Adaptive security design with malicious node detection in cluster-based sensor networks

With the proliferation of blockchain technology, ensuring the security and integrity of permissionless Proof-of-Stake (PoS) blockchain networks has become imperative. This paper addresses the persistent need for an effective system to detect and mitigate malicious nodes in such environments. Leveraging Deep Learning (DL) techniques, specifically Multi-Layer Perceptron (MLP), a novel model is proposed for real-time identification and detection of malicious nodes in PoS blockchain networks. The model integrates components for data collection, feature extraction, and model training using MLP. The proposed model is trained on labelled data representing both benign and malicious node activities, utilising transaction volumes, frequencies, timestamps, and node reputation scores to identify anomalous behaviour indicative of malicious activity. The experimental results validate the efficacy of the proposed model in distinguishing between normal and malicious nodes within blockchain networks. The model demonstrates exceptional performance in classification tasks with an accuracy of 99%, precision, recall, and F1-score values hovering around 0.99 for both classes. The experimental results verify the proposed model as a dependable tool for enhancing the security and integrity of PoS blockchain networks, offering superior performance in real-time detection and mitigation of malicious activities.

Random deployment, the absence of central authority, and the autonomous nature of the network make wireless sensor networks (WSNs) prone to security threats. Security, bandwidth, poor connectivity, intrusion, energy constraints, and other challenges are critical and could affect the performance of the WSN while considering the energy-efficient and secure routing protocols in WSNs. Security threats to WSNs are gradually being expanded. Thus, to improve the network’s performance, detection of anomalies (malicious and suspicious nodes, redundant data, bad connections, etc.) is important. This paper is aimed at introducing the malicious node detection algorithm based on the DBSCAN algorithm, which is a density-based unsupervised learning method for enabling wireless sensor networks to be much more secure and reliable. The prime objective of this algorithm is to develop a routing algorithm capable of detecting malicious nodes and having a prolonged network lifespan and higher stability period. Clustering and classification are two well-known methods in the field of machine learning that can be successfully used in various domains. Density-based clustering is a popular and extensively used approach in various domains. The DBSCAN is the utmost popular and best-known density-based clustering algorithm and is capable of determining arbitrary-shaped clusters. This paper addresses the two anomalies in the WSN, namely, spatial redundancy and malicious node identification. In this article, an algorithm has been suggested to reduce redundant data transmission along with the identification of suspicious nodes to conserve energy and to avoid falsification of data through malicious nodes. The analysis of simulation results and comparison of other algorithms that are in the same class shows that the SDBMND performs significantly better than EAMMH, TEEN, IC-ACO, and LEACH in dense networks.

In-network aggregation is an essential operation which reduces communication overhead and power consumption of resource-constrained sensor network nodes. Sensor nodes are typically organized into an aggregation tree, whereby aggregator nodes collect data from multiple data source nodes, and perform a reduction operation such as sum, average, minimum, etc. The result is then forwarded to other aggregators higher in the hierarchy toward a base station (or sink node) that receives the final outcome of the in-network computation. However, despite its performance benefits, aggregation introduces several difficult security challenges with respect to data confidentiality, integrity and authenticity. In today's outsource-centric computing environments, the aggregation task may be delegated to a third party that is not fully trusted. In addition, even in the absence of outsourcing, nodes may be compromised by a malicious adversary with the purpose of altering aggregation results. To defend against such threats, several mechanisms have been proposed, most of which devise aggregation schemes that rely on cryptography to detect that an attack has occurred. Although they prevent the sink from accepting an incorrect result, such techniques are vulnerable to denial-of-service if a compromised node alters the aggregation result in each round. Several more recent approaches also identify the malicious nodes and exclude them from future computation rounds. However, these incur high communication overhead as they require flooding or other expensive communication models to connect individual nodes with the base station. We propose a flexible aggregation structure (FAS) and an advanced ring structure (ARS) topology that allow secure aggregation and efficient identification of malicious aggregator nodes for the SUM operation. Our scheme uses only symmetric key cryptography, outperforms existing solutions in terms of performance, and guarantees that the aggregate result is correct and that malicious nodes are identified.

BTEM: Belief based trust evaluation mechanism for Wireless Sensor Networks

Recently, leading research communities have been investigating the use of blockchains for Artificial Intelligence (AI) applications, where multiple participants, or agents, collaborate to make consensus decisions. To achieve this, the data in the blockchain storage have to be transformed into blockchain knowledge. We refer to these types of blockchains as knowledge-based blockchains. Knowledge-based blockchains are potentially useful in building efficient risk assessment applications. An earlier work introduced probabilistic blockchain which facilitates knowledge-based blockchains. This paper proposes an extension for the probabilistic blockchain concept. The design of a reputation management framework, suitable for such blockchains, is proposed. The framework has been developed to suit the requirements of a wide range of applications. In particular, we apply it to the detection of malicious nodes and reduce their effect on the probabilistic blockchains' consensus process. We evaluate the framework by comparing it to a baseline using several adversarial strategies. Further, we analyze the collaborative decisions with and without the malicious node detection. Both results show a sustainable performance, where the proposed work outperforms others and achieves excellent results.

Automated and intelligent systems now manage networks, unlike in the past when static protocols and manual configuration dominated the field. This review looks at the transformations network management has undergone, starting from manual methods to evolving to Policy‐Based Network Management (PBNM), Software‐Defined Networking (SDN), and eventually Intent‐Based Networking (IBN). It also analyzes the key enabling technologies, foundational architectures, and representative implementations of each network management system. By presenting the operational strategies, technological shifts, and motivations for each network management phase, the article articulates the reasons change happens in the approach taken to manage a network. This review also focuses on the advantages of SDN and IBN, especially concerning automation, threat management, policy enforcement, and scalability. Furthermore, the review explores emerging trends like AI‐powered networks, Zero Trust security, integration of 5G–6G, blockchain uses, and the possibilities offered by quantum networking. By synthesizing technological insights and real‐world adoption scenarios, the paper offers a comprehensive perspective on the future trajectory of intelligent and autonomous network management systems.

In wireless, every device can moves anywhere without any infrastructure also the information can be maintained constantly for routing the traffic. The open issues of wireless Adhoc network the attacks which are chosen the forwarding attack that is dropped by malicious node to corrupt the network performance then the information integrity exposure. Aim of the problem that existing methods in Adhoc network for malicious node detection which cannot assure the traceability of the node as well as the fairness of node detection. In this paper, the proposed heterogeneous cluster based secure routing scheme provides trust based secure network for detection of attacks such as wormhole and black hole caused by malicious nodes presence in wireless Adhoc network. The simulation result shows that the proposed model is detect the malicious nodes effectively in wireless Adhoc networks. The malicious node detection efficiency can be achieved 96% also energy consumption also 10% better than existing method.

The integration of Software-Defined Networking (SDN) and block chain technology represents a groundbreaking approach in addressing contemporary challenges in network management and security. The dynamic and centralized control offered by SDN, combined with the decentralized and secure nature of block chain, provides a robust framework for enhancing network performance and security.SDN simplifies network management by decoupling the control plane from the data plane, allowing for more flexible and efficient network configuration. However, this centralization can become a single point of failure and a target for cyber-attacks. Block chain technology mitigates these vulnerabilities by providing a decentralized ledger that enhances security and transparency. By recording network events on a block chain, the integrity and authenticity of network transactions are ensured, reducing the risk of malicious activities. The integration of Software-Defined Networking (SDN) and block chain technology is a significant advancement in the field of network management and security, offering innovative solutions to contemporary challenges. By leveraging the dynamic control capabilities of SDN and the decentralized, secure nature of block chain, this research contributes to the development of more resilient, efficient, and secure network infrastructures. One of the critical areas of impact is enhanced network security. Traditional networks are vulnerable to various cyber threats, and the centralized nature of SDN can become a target for attacks. Block chain technology mitigates these risks by providing a decentralized ledger system that ensures the integrity and authenticity of network transactions. This integration can prevent unauthorized access and reduce the risk of data tampering, thereby creating a more secure network environment. Alternative taken as SDN with Ethereal Block chain. SDN with Hyper ledger Fabric. SDN with Corda Block chain. SDN with Cord Block chain. SDN with Multichip Block chain. Evaluation Preference taken as Security Enhancement (SE) Network Performance (NP) Implementation Cost (IC) Operational Complexity (OC) The results indicate that SDN with Quorum Block chain D Attained the top position, while SDN with Ethereal Block chain A had The lowest position achieved. The dataset's significance regarding Integration of SDN and Block chain Technology, according to the wpm Method, Company D achieves the highest ranking.