Network management is facing a great challenge to analyze and identify encrypted network traffic with specific applications and protocols. A significant number of network users applying different encryption techniques to network applications and services to hide the true nature of the network communication. These challenges attract the network community to improve network security and enhance network service quality. Network managers need novel techniques to cope with the failure and shortcomings of the port-based and payload-based classification methods of encrypted network traffic due to emergent security technologies. Mainly, the famous network hopping mechanisms used to make network traffic unknown and anonymous are VPN (virtual private network) and TOR (Onion Router). This paper presents a novel scheme to unveil encrypted network traffic and easily identify the tunneled and anonymous network traffic. The proposed identification scheme uses the highly desirable deep learning techniques to easily and efficiently identify the anonymous network traffic and extract the Voice over IP (VoIP) and Non VoIP ones within encrypted traffic flows. Finally, the captured traffic has been classified into four different categories, i-e., VPN VoIP, VPN Non-VoIP, TOR VoIP, and TOR Non-VoIP. The experimental results show that our identification engine is extremely robust to VPN and TOR network traffic.

The convergence of blockchain with Industry 5.0 technologies presents significant opportunities for healthcare data management; however, current systems face challenges related to scalability, privacy, and energy efficiency. This paper introduces an innovative framework that leverages ciphertext-policy attribute-based encryption (CP-ABE), Ethereum smart contracts, and decentralized IPFS storage to address these challenges. The framework presents three main innovations: i) a human-centric authentication system that ensures security without sacrificing cryptographic integrity; ii) post-quantum Kyber-786 algorithms paired with CP-ABE, which minimizes computational overhead by 27% while facilitating 32 ms key generation; and iii) an energy-efficient Proof-of-Stake (PoS) consensus mechanism that reduces energy consumption by 98% (0.05 kW/transaction) compared to traditional blockchain systems. Thorough testing demonstrates 99.5% resistance to man-in-the-middle attacks, a throughput of 15.6 MB/min at scale, and an emergency access latency of under 120 ms, which is essential for practical healthcare applications. By consolidating decentralized pseudo-identities for patient anonymity, secure audit logs, and GDPR/HIPAA-compliant data governance, this research establishes a new standard for secure, sustainable, and patient-focused health data ecosystems in the Industry 5.0 landscape.

Human emotions are variant with time, non-stationary, complex in nature, and are invoked as a result of human reactions during our daily lives. Continuously detecting human emotions from one-dimensional EEG signals is an arduous task. This paper proposes an advanced signal processing mechanism for emotion detection from EEG signals using continuous wavelet transform. The space and time components of the raw EEG signals are converted into 2D spectrograms followed by feature extraction. A hybrid spatio-temporal deep neural network is implemented to extract rich features. A differential-based entropy feature selection technique adaptively differentiates features based on entropy, based on low and high information regions. Bag of Deep Features (BoDF) is applied to create clusters of similar features and computes the features vocabularies for reduction of feature dimensionality. Extensive experiments are performed on the SEED dataset, which shows the significance of the proposed method compared to state-of-the-art methods. Specifically, the proposed model achieved 96.7%, 96.2%, 95.8%, and 95.3% accuracy with the SJTU SEED dataset, for SVM, ensemble, tree, and KNN classifiers, respectively.

The evergrowing diversity of encrypted and anonymous network traffic makes network management more formidable to manage the network traffic. An intelligent system is essential to analyse and identify network traffic accurately. Network management needs such techniques to improve the Quality of Service and ensure the flow of secure network traffic. However, due to the usage of non‐standard ports and encryption of data payloads, the classical port‐based and payload‐based classification techniques fail to classify the secured network traffic. To solve the above‐mentioned problems, this paper proposed an effective deep learning‐based framework employed with flow‐time‐based features to predict heterogeneous secure network traffic best. The state‐of‐the‐art machine learning strategies (C4.5, random forest, and K‐nearest neighbour) are investigated for comparison. The proposed 1D‐CNN model achieved higher accuracy in classifying the heterogeneous secure network traffic. In the next step, the proposed deep learning model characterises the major categories (virtual private network traffic, the onion router network traffic, and plain encrypted network traffic) into several application types. The experimental results show the effectiveness and feasibility of the proposed deep learning framework, which yields improved predictive power compared to the state‐of‐the‐art machine learning techniques employed for secure network traffic analysis.

Firewall configuration auditing across enterprise environments remains manual and time-intensive. Commercial platforms are proprietary, expensive and provide pass or fail verdicts without Large Language Model (LLM)-driven reasoning or Security Operations Centre (SOC) integration. Published systems do not offer multi-vendor configuration retrieval, LLM-based auditing, NIST SP 800-53 mapping and SOC dispatch within a single automated pipeline. We propose Network Security Configuration Audit Tool (NSCAT), an LLM-driven methodology that normalises six-vendor configurations into a 34-field Canonical Data Model (CDM), audits them with 36 LLMs across six providers and maps every finding to the 1,014-control NIST SP 800-53 Rev 5 catalogue. A Verified or AI-inferred tagging mechanism marks each finding by line-level match against its source configuration, surfacing unverifiable LLM outputs. Completed audits auto-dispatch severity-tagged alerts to Wazuh Security Information & Event Management (SIEM) and email network-team reports via Shuffle Security Orchestration, Automation & Response (SOAR) under auditor and operations access separation. Across 303 audits, NSCAT surfaces 2,231 unique findings with zero score-extraction failures, 99.34% PDF generation, 99.96% RAG retrieval success rate (a control returned for every finding), and 100% per-attempt SOC delivery. On a 100-finding ground truth, top-1 mapping precision (correct-control rate) is 27% strict, 39% lenient; 37.7% are Verified and 62.3% are AI-inferred (n=231, post-deployment). Median audit-to-notification latency is 21 seconds, six orders of magnitude below the 180-day PCI DSS v4.0.1 Requirement 1.2.7 Network Security Control (NSC) review cadence. NSCAT is the first to measure NIST SP 800-53 mapping precision against ground truth and compress this cadence on open-source infrastructure.