Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 924-929· 0 citations· 16 references
Abstract
The rapid roll-out of 5G communication networks, 5G Users can experience high-speed connections, super low latency and wide-open connectivity with Internet of Things. The 5G infrastructures are based on the distributed architecture, which makes them vulnerable to advanced cyber-attacks such as Distributed Denial-of-Service, Sybil attack, Spoofing attack, Insider attack and Data manipulation attack. The prevailing security paradigm in recent years has been focusing on either intrusion detection or privacy protection, but little effort has been made to achieve a combined and integrated security strategy to construct a proper trust and base the decisions that are taken. In order to overcome the above challenge, the present work introduces the TrustChain-5G system, a safe and secure trust based system, that combines the advantages of Federated Learning, Adaptive Trust Evaluation, Blockchain and Machine Learning for detecting attacks. The trusted behavior economy model constantly monitors node communication patterns, past interactions, security compliance index to identify suspicious nodes in a system. Federated learning can be used to create a collaborative model, keeping privacy intact, while blockchain can be used to store security events (tamper-proof), and trust records. The simulated 5G environment for the evaluation of the framework consisted of 100 network nodes in a mixture of CICIDS2017, CICDDoS2019 and UNSW-NB15 datasets. This is experimentally demonstrated to provide 96.2% accuracy, 95.4% precision, 94.9% recall and a minimum detection delay in various attack scenarios. The results validate the success of the proposed architecture in increasing the efficiency of attacks detection, trust management system, and ensuring security and reliability in future 5G communication network.
The rapid proliferation of Internet of Things (IoT) devices under sixth-generation (6G) networks introduces a highly
dynamic, decentralized environment in which static, perimeter-based security models are no longer adequate. This paper
proposes AZTM-v3 an adaptive Zero Trust framework that couples behavior-driven trust management with a Random Forest
classifier to identify and isolate malicious nodes in real time. The framework is evaluated on an NS-3 simulation of a 150-node 6G
IoT network subjected to Sybil, Denial-of-Service (DoS), spoofing, replay and ON-OFF attacks. Unlike prior trust-management
proposals that report only qualitative or partial outcomes this work quantifies performance across five dimensions i.e detection
accuracy, F1-score, false-positive rate, end-to-end latency and consensus-convergence time and benchmarks AZTM-v3 against
PKI-based, centralized-trust and static-blockchain baselines. AZTM-v3 attains a 98.1% overall detection accuracy with a 1.6%
false-positive rate at 150 nodes and sustains 95.4% accuracy at 200 nodes outperforming the PKI baseline by 12–18 percentage
points across all tested loads. These results indicate that combining tiered trust evaluation with machine learning based
classification yields a measurably more scalable and resilient security layer for 6G-enabled IoT deployments than existing static
or purely cryptographic approaches.
Nelli Yaswanth Kumar, Singothu Jhansi Rani, Setti Sarika· International Journal for Re...· 0 citations
In the digital era, the prevalence of cyber threats within cloud-based infrastructures presents a formidable challenge. This study introduces a novel approach that combines the immutable nature of blockchain technology with advanced detection mechanisms to enhance the security of cloud environments. We propose a model that leverages the synergy of blockchain's distributed ledger capabilities and cutting-edge intrusion detection systems (IDS) to establish a dynamic and decentralized framework for cyber-attack detection and prevention. Our innovative method involves a multi-layered detection algorithm that operates in conjunction with a blockchain network to make sure the data integrity and veracity of application transmissions. With integration, the proposed system not only detects but also systematically records cyber attack patterns, thereby creating a robust database of digital signatures that can be used for future prevention measures. This proactive approach ensures a swift and secure method of identifying potential threats, which will significantly reduce the risk of data breaches along with system infiltrations. The implementation of this method is anticipated to provide a reliable and transparent mechanism for safeguarding sensitive information stored within cloud services. It advances cybersecurity, protecting service providers and end-users from changing cyber threats.
Eruguralla SatishBabu, Smitha Chowdary· International Conference Com...· 0 citations
The high mobility and decentralized nature of Vehicular Ad Hoc Networks (VANETs) present significant security challenges. Specifically, detecting attacks and establishing secure, reliable routing protocols are major critical concerns in the vehicular environment. These attacks can significantly degrade network performance and hinder communication between vehicles. Insider attacks, such as Blackhole attacks, have the potential to severely disrupt VANET systems. This study introduces a novel trust management scheme that incorporates cryptographic techniques to address the important issues of secure routing in VANETs, which also helps in the detection of attacks. In this work, nodes' trust scores are evaluated, and the forwarding node for packet dissemination is chosen based on these scores. Furthermore, an elliptic curve cryptographic (ECC) signcryption technique is added for providing security to the network by authenticating the nodes, which mitigates the misbehaving nodes from the network. The simulation and comparative analysis show the efficacy of the proposed scheme. The proposed approach attained a packet delivery ratio (PDR) of 92.8%, indicating high reliability in data dissemination. Furthermore, the achieved results of throughput and End‐to‐End (E2E) delay are 232.32 KBps and 0.02 s, respectively. The obtained outcomes show enhancements of 94.182%, 49.67%, and 6% in PDR, throughput, and E2E delay, respectively, with respect to the existing techniques.
Nidhi Jaswani, Mou Dasgupta, Sangram Ray et al.· Security and Privacy· 0 citations
Acoustic communication constraints, high latency, and dynamic topology of underwater wireless sensor networks (UWSNs) make them highly susceptible to routing disruptions, Sybil behaviour, selective forwarding, and false data injection, making them highly vulnerable to routing disruption. Currently implemented cryptographic and learning-based defenses lack transparency and cannot justify their decisions due to the lack of centralized trust authorities or black-box intrusion detection systems. Consequently, they are unable to be accepted in the field. A novel protocol, Blockchain-empowered eXplainable AI-based Trust and Intrusion-handling (BEXAIT-Trust), is proposed to perform decentralized trust management, explainable intrusion detection, and attack-aware routing in UWSNs. A BEXAIT-Trust system differs from previous methods that sequentially combined blockchain and Intrusion Detection System (IDS). The system has three main parts: (i) a two-stage explainable artificial intelligence intrusion detection system that uses both supervised attack classification and unsupervised anomaly detection; (ii) a blockchain-integrated trust evolution mechanism that uses lightweight smart contracts to update trust between nodes.; and (iii) a trust-and-risk-adaptive routing framework that informs traffic routing decisions based on articulated risk rather than raw intrusion detection system labels. This methodology does not exist in current UWSN security frameworks, which typically lack either co-optimized trust–IDS coupling or decision explainability. Based on extensive simulations, it has been demonstrated that detection accuracy is 94–99%, false-positive rate is 1–6%, and precision/recall is 92–98%. To accomplish security improvements, mere latency increases of 5–12% are required, energy overhead increases of 6–15%, and throughput increases of 8–20%. With XAI, mission operators can interpret and trace forensic evidence based on blockchain-backed evidence to 90–97% accuracy. This study confirms that BEXAIT-Trust strengthens the security, transparency, and resilience of UWSNs, providing a deployable path toward accountable underwater cyber-physical systems.
V. P. Tharun Krisshna, M. Kishore, R. S. Vignesh et al.· IEEE Open Journal of the Com...· 0 citations
The rapid expansion of Internet of Things (IoT) edge networks has introduced significant cybersecurity challenges due to the increasing number of resource-constrained devices operating outside traditional security perimeters. Conventional perimeter-based defenses are inadequate against Advanced Persistent Threats (APTs), which exploit compromised edge devices through stealthy, multi-stage attacks involving reconnaissance, lateral movement, command-and-control communication, and data exfiltration. This study presents Edge-ZTA, a lightweight Zero-Trust Architecture specifically designed for securing Industrial IoT edge environments. The proposed framework integrates three complementary components: dynamic device identity verification based on trusted attestation and behavioral fingerprinting, continuous behavioral monitoring using a Federated Deep Autoencoder for privacy-preserving anomaly detection, and Software-Defined Networking (SDN)-based dynamic micro-segmentation for real-time isolation of compromised devices. A comprehensive hybrid experimental testbed comprising physical edge devices, virtualized nodes, and 500,000 network flow records derived from benchmark cybersecurity datasets was developed to evaluate the proposed architecture under realistic APT scenarios. Experimental results demonstrated a weighted macro-average F1-score of 97.1%, with detection rates of 98.9%, 97.9%, 96.8%, and 95.9% for reconnaissance, lateral movement, command-and-control, and exfiltration attacks, respectively. Furthermore, the decentralized edge-based policy decision mechanism maintained end-to-end latency below 50 ms, while CPU utilization remained below 17%, confirming the framework's suitability for resource-constrained IoT deployments. Scalability experiments involving up to 500 edge nodes further verified stable detection accuracy and predictable latency under heterogeneous operating conditions. These findings demonstrate that Edge-ZTA provides an efficient, privacy-preserving, and scalable cybersecurity framework capable of mitigating sophisticated multi-stage cyberattacks while satisfying the stringent performance requirements of next-generation Industrial IoT infrastructures.
Ahmed Ramzi Rashid, Zaydon L. Ali, Al-Doori, Ahmed Sedeeq Baker· Al-Noor Journal of Engineeri...· 0 citations
The implementation of 5G technology together with extensive Internet of Things systems provides three communication services which include ultra-reliable low-latency communication and improved mobile broadband and extensive machine-type communication. The diverse types of Internets of Things devices which operate at high densities create multiple new points of attack that enable networks to suffer from advanced persistent threats and distributed denial-of-service attacks and spoofing and data injection attacks. This paper presents a blockchain-based deep learning system which detects threats in 5G Internet of Things networks. The proposed architecture consists of three components which include convolutional neural networks and long short-term memory networks and smart-contract-based decentralized authentication mechanisms. The researchers tested the system by using an established object detection benchmark which showed that it worked successfully in real-world usage scenarios. The results showed that the system achieved better performance in scalability and reliability and compliance with QoS standards and computational efficiency and security and privacy protection and user experience enhancement. The system detects 98.4% of threats while keeping latency below 10 milliseconds which meets the requirements of URLLC standards.
P. Rahul Das, M. Kavya, D Anitha Kumari et al.· International journal of com...· 0 citations
