The rapid proliferation of Internet of Things (IoT) devices has transformed modern infrastructures, yet their inherently distributed and dynamic nature poses significant challenges for cybersecurity. Traditional intrusion detection systems (IDS) often rely on static models or linear temporal analysis, which makes them insufficient to identify and respond to evolving threats that manifest across both spatial and temporal dimensions. Furthermore, existing attention-based mechanisms tend to treat spatial and temporal dependencies separately and apply fixed attention weights, limiting their adaptability in complex IoT environments. To address these limitations, we propose, a novel IDS framework incorporating a Spatio-Temporal Conditioning with Dynamic Multi-Head Attention (STC-MHA-DW) mechanism. This module captures contextual interdependencies across both spatial features and temporal windows by leveraging head-wise adaptive projections, enabling the model to dynamically reweight attention based on surrounding threat context. The temporal encoder is built using a dual-stage gated mechanism that processes both short-term fluctuations and long-term dependencies, while the attention layer refines representations through localized spatio-temporal salience. We also introduce a scalable, cloud-native deployment architecture using microservices and containerization to ensure efficient performance under dynamic network loads. Experimental evaluations show that the proposed ids achieves the highest detection accuracy of 99.84%, precision of 99.47%, recall of 98.83%, and f1-score of 99.14% with a very low false alarm rate, outperforming existing models.

The increasing adoption of Unmanned Aerial Ve-hicles (UAV s) in various critical applications necessitates robust security measures to protect these systems from cyber threats. In response, this research introduces an innovative Intrusion Detection System (IDS) specifically tailored for UAV s. The proposed IDS leverages Hierarchical Attention-based Long Short-Term Memory (H-LSTM) networks to effectively model the intricate temporal dependencies in UAV data. This architecture allows for comprehensive surveillance of UAV behavior, capturing both short-term anomalies and long-term deviations from expected patterns. The hierarchical attention mechanism enables the system to focus on salient features within the data, enhancing detection accuracy and robustness. To address the critical need for interpretable AI in cybersecurity, we incorporate Shapley Ad-ditive Explanations (SHAP) into our IDS. SHAP values provide a coherent and intuitive explanation of the IDS's decisions by emphasizing the specific features and their contributions to the intrusion detection process. The performance of the proposed system is rigorously evaluated using the N-BaIoT dataset. Our experiments demonstrate that the H-LSTM-based IDS outper-forms traditional methods, achieving a higher detection rate while minimizing false positives. Moreover, the incorporation of SHAP explanations facilitates rapid incident analysis, allowing security professionals to discern between genuine threats and benign anomalies effectively.

The deployment of Internet of Things (IoT) systems in Smart Agriculture (SA) operates in extreme environments including wind, snowfall, flooding, landscape, and so on for collecting and processing real-time data. The increased connectivity and broad adoption of IoT devices with low-power communications on farmland support farmers in making data-driven decisions using various Artificial Intelligence (AI) techniques. Furthermore, in such an environment, edge computing is also utilized to provide computationally intensive, latency-sensitive, and bandwidth-demanding services at the edge of the network. However, protecting edge-to-Things in the extreme environment of SA is challenging, due to the volume of data, and also attackers exploit network gateways to perform Distributed Denial of Service (DDoS) attacks. Motivated by the aforementioned challenges, we develop a novel deep learning-based Intrusion Detection System (IDS) for edge-envisioned SA in extreme environments. Specifically, a hybrid approach is developed by combining bidirectional gated recurrent unit, long-short term memory with softmax classifier to detect attacks at the edge of the network. To allow faster learning, the proposed IDS employs the Truncated Backpropagation through Time (TBPTT) approach to handle lengthy sequences of network data. Furthermore, we suggest an attack scenario with deployment architecture for the proposed IDS in the extreme environment of SA. Extensive experiments using three publicly available datasets namely, CIC-IDS2018, ToN-IoT, and Edge-IIoTset prove the effectiveness of the proposed IDS over some traditional and contemporary state-of-the-art techniques.

The recent innovations in the network communication domain have entirely revolutionized the conventional industrial sector by introducing a new era of automatic communication. The Industrial Internet of Things (IIoT) is acknowledged as an exclusive space where Internet of Things (IoT) seems to divulge expressive manifestations. However, the expanded connectivity, more openness, and the widespread use of low-power communication devices of IIoT makes them vulnerable to malicious attacks, and criminal activities. Moreover, the heterogeneous and prevalent nature of the IIoT devices makes it very difficult to come up with a centralized threat detection mechanism, thus, its security remains a major concern. Motivated by this, we propose a fog-empowered augmented intelligence (IA)-based defensive mechanism to ensure secure communication in IoT-enabled smart industries. The designed framework incorporates the aggregated potentials of two highly acclaimed deep learning (DL) classifiers: 1) gated recurrent unit (GRU) and 2) bidirectional long short-term memory (BiLSTM), where the DL-based scheme detects anomalies in such industrial networks and the FOG-based scenario promotes the routing flexibility and interoperability of the heterogeneous devices of the IoT-enabled smart industrial network. The validity of the proposed mechanism is analytically investigated by conducting a comparison with the benchmark threat detection techniques. The proposed solution is also generically examined parallel to some state-of-the-art approaches. The Cu-GRU-BiLSTM framework achieved up to 99.91% accuracy with a low-false alarm rate and outperforms the baseline and recent threat detection approaches. The simulation and comparison results validate the effectiveness of the proposed mechanism and advocate it as a phenomenal choice to ensure efficacious and secure communication in IoT-based smart industries.

Attributes proof in anonymous credential systems is an effective way to balance security and privacy in user authentication; however, the linear complexity of attributes proof causes the existing anonymous credential systems far away from being practical, especially on resource-limited smart devices. For efficiency considerations, we present a novel pairing-based anonymous credential system which solves the linear complexity of attributes proof based on aggregate signature scheme. We propose two extended signature schemes, BLS+ and BGLS+, to be cryptographical building blocks for constructing anonymous credentials in the random oracle model. Identity-like information of message holder is encoded in a signature in order that the message holder can prove the possession of the input message along with the validity of a signature. We present issuance protocol for anonymous credentials embedding weak attributes which are referred to what cannot identify a user in a population. Users can prove any combination of attributes all at once by aggregating the corresponding individual credentials into one. The attributes proof protocols on AND and OR relation over multiple attributes are also given. The performance analysis shows that the aggregation-based anonymous credential system outperforms both the conventional Camenisch–Lysyanskaya pairing-based system and the accumulator-based system when prove AND and OR relation over multiple attributes, and the size of credential and public parameters are shorter as well.

It is important that during the execution of a business process built from composable Web services from multiple domains, the component service be able to verify the identity of the user to check it has the required permissions for accessing the services, while at the same time identity attributes need to be protected properly as they can be target of attacks. In such context, we propose a privacy-preserved multi-domain identity attributes verification model BPVrfy. It extends federate identity management with support for multiple identity verification policies and privacy enhancement. Identity attributes verification process is partitioned into three sub-procedures consisting of attribute provision, federation enrollment and attributes transfer, and then a series of protocols based on cryptographic schemes is proposed respectively. BPVrfy adopts Perdersen Commitment, Zero-Knowledge Proof of Knowledge, BGLS Aggregate Signature and Certificate-Based Signature (CBS) cryptographic schemes together to give a privacy-preserved federate identity attributes verification solution for multi-domain Web services-based business processes.

An aggregated privacy-preserving identity verification scheme is proposed for composite Web services. It aggregates multiple component providers' interactions of identity verification to a single one involving the user. Besides, it protects users from privacy disclosure through the adoption of zero-knowledge of proof of knowledge. This approach can dramatically reduce the computation time, independently on the number of identity attributes and component providers.

To objectively assess quality of web services, we propose a six-dimensional QoWS model which includes expected QoWS, agreed QoWS, delivered QoWS, perceived QoWS, transmitted QoWS, and statistic QoWS. Then a trusted QoWS monitoring model is presented. It adopts Simple Network Management Protocol (SNMP) to respectively capture delivered QoWS, perceived QoWS, and transmitted QoWS. A new style assessment method is proposed to have two capabilities, one is assessing the given service deliver from the view of delivered QoWS and perceived QoWS, the other is assessing the given service over a period of time from the view of reputation and statistic performance experienced by consumers.

We propose a six dimensional QoWS model including expected QoWS, agreed QoWS, delivered QoWS, perceived QoWS, transmitted QoWS, and statistic QoWS to assess quality of Web services comprehensively and objectively. Meanwhile, the proposed assessment mechanism evaluates Web services from the view of compliance, end-to-end performance, and long-term performance.