Explore the vast range of titles available.

With the fast growth of artificial intelligence (AI) and a novel generation of network technology, the Internet of Things (IoT) has become global. Malicious agents regularly utilize novel technical vulnerabilities to use IoT networks in essential industries, the military, defence systems, and medical diagnosis. The IoT has enabled well-known connectivity by connecting many services and objects. However, it has additionally made cloud and IoT frameworks vulnerable to cyberattacks, production cybersecurity major concerns, mainly for the growth of trustworthy IoT networks, particularly those empowering smart city systems. Federated Learning (FL) offers an encouraging solution to address these challenges by providing a privacy-preserving solution for investigating and detecting cyberattacks in IoT systems without negotiating data privacy. Nevertheless, the possibility of FL regarding IoT forensics remains mostly unexplored. Deep learning (DL) focused cyberthreat detection has developed as a powerful and effective approach to identifying abnormal patterns or behaviours in the data field. This manuscript presents an Advanced Artificial Intelligence with a Federated Learning Framework for Privacy-Preserving Cyberthreat Detection (AAIFLF-PPCD) approach in IoT-assisted sustainable smart cities. The AAIFLF-PPCD approach aims to ensure robust and scalable cyberthreat detection while preserving the privacy of IoT users in smart cities. Initially, the AAIFLF-PPCD model utilizes Harris Hawk optimization (HHO)-based feature selection to identify the most related features from the IoT data. Next, the stacked sparse auto-encoder (SSAE) classifier is employed for detecting cyberthreats. Eventually, the walrus optimization algorithm (WOA) is used for hyperparameter tuning to improve the parameters of the SSAE approach and achieve optimal performance. The simulated outcome of the AAIFLF-PPCD technique is evaluated using a benchmark dataset. The performance validation of the AAIFLF-PPCD technique exhibited a superior accuracy value of 99.47% over existing models under diverse measures.

Cybersecurity is becoming an increasingly important aspect in ensuring maritime data protection and operational continuity. Ships, ports, surveillance and navigation systems, industrial technology, cargo, and logistics systems all contribute to a complex maritime environment with a significant cyberattack surface. To that aim, a wide range of cyberattacks in the maritime domain are possible, with the potential to infect vulnerable information and communication systems, compromising safety and security. The use of navigation and surveillance systems, which are considered as part of the maritime OT sensors, can improve maritime cyber situational awareness. This survey critically investigates whether the fusion of OT data, which are used to provide maritime situational awareness, may also improve the ability to detect cyberincidents in real time or near-real time. It includes a thorough analysis of the relevant literature, emphasizing RF but also other sensors, and data fusion approaches that can help improve maritime cybersecurity.

Cyber threat hunting early hunts for cyberattacks hidden by conventional defence tools. It inspects extreme to recognize mischievous programs (i.e., malware) that evade recognition. It is significant because complicated cyberattacks can evade the mechanisms of cyber security. The performance of cyberattack hunting is enhanced over artificial intelligence (AI), particularly explainable AI (XAI), which includes a trust module to the cyberattack hunting procedure. Owing to the addition of XAI, security specialists obtain complete descriptions of perceived attacks as the recognition method in XAI is recognized. Information, like attack, how it was noticed, and why it was identified, is attained very effortlessly owing to XAI in the hunting process of cyberattack. AI, mainly over machine learning (ML) and deep learning (DL) approaches, has exposed promising latent in progressing cybersecurity measures. Recently, the growth of the blockchain (BC) method has indicated a route value in solving the distributed trusted problem in the Internet of Things (IoT) platform. So, this manuscript presents a novel Two-Tier Optimization Algorithms for Cyberthreat Detection and Mitigation Using Explainable Artificial Intelligence with Recurrent Neural Networks (TTOCDM-XAIRNN) methodology. The main intention of the TTOCDM-XAIRNN algorithm framework is to improve the detection and mitigation of cyber threats in dynamic environments. The BC technology is utilized for safe inter-cluster data transmission methods. The presented TTOCDM-XAIRNN model initially employs data preprocessing with a linear scaling normalization (LSN) model to standardize the input features for improved model performance. The pelican optimization algorithm (POA) model is employed for dimensionality reduction to identify the most relevant data attributes. Furthermore, the hybrid attention-based long short-term memory and bidirectional gated recurrent unit (A-LSTM-BiGRU) technique is utilized for cyber threat detection. Finally, the earthworm optimization algorithm (EOA) is implemented to tune the hyperparameters and ensure the model’s parameters are optimized for superior detection and mitigation capabilities. Finally, XAI with SHAP presents transparent insights into model decisions, ensuring high performance and a clear understanding of the threat mitigation process. A wide range of simulation studies of the TTOCDM-XAIRNN approach is examined under the NSLKDD and CICIDS 2017 datasets. The comparison study of the TTOCDM-XAIRNN approach portrayed a superior accuracy value of 98.34% and 98.87% under dual datasets.

The new era of decentralized, privacy-oriented social media platforms has brought us a set of related enforcement problems which include detecting cyberbullying, disinformation on a coordinated scale 5 , 14 . These centralized or unimodal systems are unable to work efficiently when faced with stringent privacy concerns or multimodal content. In this paper, we present Federated Cross-Modal Graph Transformer (FCMGT) to jointly model text, image and audio features and social graph structure in federated learning settings. Furthermore, the proposed approach is enhanced by a dynamic adversarial training to mitigate content perturbation, graph manipulation and model-poisoning attacks. On a large-scale synthetic decentralized dataset (2 M + interactions), the experiments reveal that FCMGT achieves an F1-Score of 0.927, outperforming the best baseline by 4.6%, and achieves an AUC of 0.963. Performance drop down under adversarial attacks is only 3.8%, in contrast to 15–30% for previous models. These findings position FCMGT as a reliable, scalable, and privacy-preserving system for safe guarding next-generation decentralized social networks.

In today’s dynamic complex cyber environments, Cyber Threat Intelligence (CTI) and the risk of cyberattacks are both increasing. This means that organizations need to have a strong understanding of both their internal CTI and their external CTI. The potential for cybersecurity knowledge graphs is evident in their ability to aggregate and represent knowledge about cyber threats, as well as their ability to manage and reason with that knowledge. While most existing research has focused on how to create a full knowledge graph, how to utilize the knowledge graph to tackle real-world industrial difficulties in cyberattack and defense situations is still unclear. In this article, we give a quick overview of the cybersecurity knowledge graph’s core concepts, schema, and building methodologies. We also give a relevant dataset review and open-source frameworks on the information extraction and knowledge creation job to aid future studies on cybersecurity knowledge graphs. We perform a comparative assessment of the many works that expound on the recent advances in the application scenarios of cybersecurity knowledge graph in the majority of this paper. In addition, a new comprehensive classification system is developed to define the linked works from 9 core categories and 18 subcategories. Finally, based on the analyses of existing research issues, we have a detailed overview of various possible research directions.

In the present digital era, malware defences and attacks are becoming more difficult, creating a progressing cyberthreat landscape. With the fast development in technology, cyberthreats have shown improved intricacy and potency that frequently exceed the abilities of conventional defence systems. The Internet of Things (IoT) is a technical development that allows machine-to-machine and human-to-human interaction for essential data exchange. The IoT provides numerous advantages but also builds several problems. Exposures in IoT methods are problematic and main to devices enduring various threats, with the danger of denial of service (DoS) and security challenges like privacy, confidentiality, and obtainability to assault. This manuscript proposes a cyberthreat defence mechanism using a Binary Ebola Optimization Search Algorithm and Ensemble Models (CDM-BEOSAEM) method. The main intention of the CDM-BEOSAEM method is to enhance the cyberattack detection method in an IoT environment. Initially, the min-max normalization is applied in the data normalization stage to convert input data into a beneficial format. Furthermore, the binary ebola optimization search algorithm (BEOSA) model recognizes the most appropriate features in the feature selection (FS) process. For the classification of cyberthreat defence, the proposed CDM-BEOSAEM model utilizes an ensemble of bidirectional gated recurrent unit (BiGRU), auto-encoders (AE), and graph convolutional network (GCN) techniques. Finally, the hyperparameter selection of ensemble models is performed by implementing the escape Coati Optimization Algorithm (eCOA) technique. The simulation of the CDM-BEOSAEM approach is accomplished under the ToN-IoT dataset, and the results are measured using various measures. The performance validation of the CDM-BEOSAEM approach portrayed a superior accuracy value of 99.29% over existing models.

In Industry 4.0, manufacturing and critical systems require high levels of flexibility and resilience for dynamic outcomes. Industrial Control Systems (ICS), specifically Supervisory Control and Data Acquisition (SCADA) systems, are commonly used for operation and control of Critical Infrastructure (CI). However, due to the lack of security controls, standards, and proactive security measures in the design of these systems, they have security risks and vulnerabilities. Therefore, efficient and effective security solutions are needed to secure the conjunction between CI and I4.0 applications. This paper predicts potential cyberattacks and threats against CI systems by considering attacker motivations and using machine learning models. The approach presents a novel cybersecurity prediction technique that forecasts potential attack methods, depending on specific CI and attacker motivations. The proposed model’s accuracy in terms of False Positive Rate (FPR) reached 66% with the trained and test datasets. This proactive approach predicts potential attack methods based on specific CI and attacker motivations, and doubling the trained data sets will improve the accuracy of the proposed model in the future.

The Internet of Things (IoT) is a technological revolution that enables human-to-human and machine-to-machine communication for virtual data exchange. The IoT allows us to identify, locate, and access the various things and objects around us using low-cost sensors. The Internet of Things offers many benefits but also raises many issues, especially in terms of privacy and security. Appropriate solutions must be found to these challenges, and privacy and security are top priorities in the IoT. This study identifies possible attacks on different types of networks as well as their countermeasures. This study provides valuable insights to vulnerability researchers and IoT network protection specialists because it teaches them how to avoid problems in real networks by simulating them and developing proactive solutions. IoT anomalies were detected by simulating message queuing telemetry transport (MQTT) over a virtual network. Utilizing DDoS attacks and some machine learning algorithms such as support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN) and logistic regression (LR), as well as an artificial neural network, multilayer perceptron (MLP), naive Bayes (NB) and decision tree (DT) are used to detect and mitigate the attack. The proposed approach uses a dataset of 4998 records and 34 features with 8 classes of network traffic. The classifier RF showed the best performance with 99.94% accuracy. An intrusion detection system using Snort was implemented. The results provided theoretical proof of applicability and feasibility.

Cybersecurity is a growing area of research due to the constantly emerging new types of cyberthreats. Tools and techniques exist to keep systems secure against certain known types of cyberattacks, but are insufficient for others that have recently appeared. Therefore, research is needed to design new strategies to deal with new types of cyberattacks as they arise. Existing tools that harness artificial intelligence techniques mainly use artificial neural networks designed from scratch. In this paper, we present a novel approach for cyberattack detection using an encoder–decoder pre-trained Large Language Model (T5), fine-tuned to adapt its classification scheme for the detection of cyberattacks. Our system is anomaly-based and takes statistics of already finished network flows as input. This work makes significant contributions by introducing a novel methodology for adapting its original task from natural language processing to cybersecurity, achieved by transforming numerical network flow features into a unique abstract artificial language for the model input. We validated the robustness of our detection system across three datasets using undersampling. Our model achieved consistently high performance across all evaluated datasets. Specifically, for the CIC-IDS-2017 dataset, we obtained an accuracy, precision, recall, and F-score of more than 99.94%. For CSE-CIC-IDS-2018, these metrics exceeded 99.84%, and for BCCC-CIC-IDS-2017, they were all above 99.90%. These results collectively demonstrate superior performance for cyberattack detection, while maintaining highly competitive false-positive rates and false-negative rates. This efficacy is achieved by relying exclusively on real-world network flow statistics, without the need for synthetic data generation.

Cybersecurity challenges are increasing in the rail industry because of constant technological evolution that includes the Internet of Things, blockchains, automation, and artificial intelligence. Consequently, many railroads and supply chain stakeholders have implemented strategies and practices to address these challenges. However, the pace of cybersecurity implementation in the railroad industry is slow even as cyberthreats escalate. This study uniquely integrates bibliometric analysis with a systematic literature review to provide a holistic view of cybersecurity trends in rail freight. The study analyzes 70 articles focusing on cybersecurity practices in the rail freight industry, structured around four research questions relating to: (1) challenges, (2) measures, (3) emerging trends, and (4) innovations. Key findings are that implementing cybersecurity practices in the rail freight industry comes with numerous challenges and risks. The study concludes that new threats will constantly emerge with technological advancements. Therefore, there is a need for continuous human training, collaboration, and coordination with stakeholders. This study also highlights research gaps and recommends how stakeholders can most appropriately execute cybersecurity strategies and best coordinate them with the various technological functions in the rail freight industry.