Abstracts:The massive growth of diversified smart devices and continuous data generation poses a challenge to communication architectures. To deal with this problem, communication networks consider fog computing as one of promising technologies that can improve overall communication performance. It brings on-demand services proximate to the end devices and delivers the requested data in a short time. Fog computing faces several issues such as latency, bandwidth, and link utilization due to limited resources and the high processing demands of end devices. To this end, fog caching plays an imperative role in addressing data dissemination issues. This study provides a comprehensive discussion of fog computing, Internet of Things (IoTs) and the critical issues related to data security and dissemination in fog computing. Moreover, we determine the fog-based caching schemes and contribute to deal with the existing issues of fog computing. Besides, this paper presents a number of caching schemes with their contributions, benefits, and challenges to overcome the problems and limitations of fog computing. We also identify machine learning-based approaches for cache security and management in fog computing, as well as several prospective future research directions in caching, fog computing, and machine learning.

Abstracts:The blockchain provides a reliable and scalable method for enabling source-tracing functionality in large-scale Internet of Things (IoT) systems. Traditional blockchain-based source tracing applications are generally based on the hypothesis that the raw data collected by each IoT node are credible and consistent, which however may not always be the truth. As no mechanism ensures the reliability of the original data collected from the IoT devices, these data may be accidently screwed up or maliciously tampered with before they are uploaded on-chain. To address this issue, we propose the Multi-dimensional Certificates of Origin (MCO) method to filter out the potentially incredible data-till all the data uploaded to the chain are credible. To achieve this, we devise the Multi-dimensional Information Cross-Verification (MICV) and Multi-source Data Matching Calculation (MDMC) methods. MICV verifies whether a to-be-uploaded datum is consistent or credible, and MDMC determines which data should be discarded and which data should be kept to retain the most likely credible/untampered ones in the circumstance when data inconsistency appears. Large-scale experiments show that our scheme ensures on the credibility of data and off the chain with an affordable overhead.

Abstracts:The ongoing data explosion introduced unprecedented challenges to the information security of communication networks. As images are one of the most commonly used information transmission carriers; therefore, their data redundancy analysis and screening are of great significance. However, most of the current research focus on the algorithm improvement of commonly used image datasets. Thus, we should consider an important question: Is there data redundancy in the open datasets? Considering the factors of model structures and data distribution to ensure the generalization, we conducted extensive experiments to compare the average accuracy based on few random data to the baseline accuracy based on all data. The results show serious data redundancy in the open datasets from different domains. For instance, with the aid of deep model, only 20% data can achieve more than 90% of the baseline accuracy. Further, we proposed a novel entropy-based information screening method, which outperforms the random sampling under many experimental conditions. In particular, considering 20% of data, for the shallow model, the improvement is approximately 10%, and for the deep model, the ratio to the baseline accuracy increases to greater than 95%. Moreover, this work can also serve as a new way of learning from a few valuable samples, compressing the size of existing datasets and guiding the construction of high-quality datasets in the future.

Abstracts:A secure operating system in the communication network can provide the stable working environment, which ensures that the user information is not stolen. The micro-kernel operating system in the communication network retains the core functions in the kernel, and unnecessary tasks are implemented by calling external processes. Due to the small amount of code, the micro-kernel architecture has high reliability and scalability. Taking the micro-kernel operating system in the communication network prototype VSOS as an example, we employ the objdump tool to disassemble the system source code and get the assembly layer code. On this basis, we apply the Isabelle/HOL, a formal verification tool, to model the system prototype. By referring to the mathematical model of finite automata and taking the process scheduling module as an example, the security verification based on the assembly language layer is developed. Based on the Hoare logic theory, each assembly statement of the module is verified in turn. The verification results show that the scheduling module of VSOS has good functional security, and also show the feasibility of the refinement framework.

Abstracts:The Internet of Medical Things (IoMT) is a collection of smart healthcare devices, hardware infrastructure, and related software applications, that facilitate the connection of healthcare information technology system via the Internet. It is also called IoT in healthcare, facilitating secure communication of remote healthcare devices over the Internet for quick and flexible analysis of healthcare data. In other words, IoMT is an amalgam of medical devices and applications, which improves overall healthcare outcomes. However, this system is prone to security- and privacy-related attacks on healthcare data. Therefore, providing a robust security mechanism to prevent the attacks and vulnerability of IoMT is essential. To mitigate this, we proposed a new Artificial-Intelligence envisioned secure communication scheme for IoMT. The discussed network and threat models provide details of the associated network arrangement of the IoMT devices and attacks relevant to IoMT. Furthermore, we provide the security analysis of the proposed scheme to show its security against different possible attacks. Moreover, a comparative study of the proposed scheme with other similar schemes is presented. Our results show that the proposed scheme outperforms other similar schemes in terms of communication and computation costs, and security and functionality attributes. Finally, we provide a pragmatic study of the proposed scheme to observe its impact on various network performance parameters.

Abstracts:The traditional malware research is mainly based on its recognition and detection as a breakthrough point, without focusing on its propagation trends or predicting the subsequently infected nodes. The complexity of network structure, diversity of network nodes, and sparsity of data all pose difficulties in predicting propagation. This paper proposes a malware propagation prediction model based on representation learning and Graph Convolutional Networks(GCN) to address the aforementioned problems. First, to solve the problem of the inaccuracy of infection intensity calculation caused by the sparsity of node interaction behavior data in the malware propagation network, a mechanism based on a tensor to mine the infection intensity among nodes is proposed to retain the network structure information. The influence of the relationship between nodes on the infection intensity is also analyzed. Second, given the diversity and complexity of the content and structure of infected and normal nodes in the network, considering the advantages of representation learning in data feature extraction, the corresponding representation learning method is adopted for the characteristics of infection intensity among nodes. This can efficiently calculate the relationship between entities and relationships in low dimensional space to achieve the goal of low dimensional, dense, and real-valued representation learning for the characteristics of propagation spatial data. We also design a new method, Tensor2vec, to learn the potential structural features of malware propagation. Finally, considering the convolution ability of GCN for non-Euclidean data, we propose a dynamic prediction model of malware propagation based on representation learning and GCN to solve the time effectiveness problem of the malware propagation carrier. The experimental results show that the proposed model can effectively predict the behaviors of the nodes in the network and discover the influence of different characteristics of nodes on the malware propagation situation.

Abstracts:With the rise of the Internet of Vehicles (IoV) and the number of connected vehicles increasing on the roads, Cooperative Intelligent Transportation Systems (C-ITSs) have become an important area of research. As the number of Vehicle to Vehicle (V2V) and Vehicle to Interface (V2I) communication links increases, the amount of data received and processed in the network also increases. In addition, networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient. Thus, there is a need to augment them with intelligent network intrusion detection techniques. Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times. However, given the expected large network size, there is a necessity for extensive data processing for use in such anomaly detection methods. Deep learning solutions are lucrative options as they remove the necessity for feature selection. Therefore, with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario, the need for deep learning-based techniques is all the more heightened. This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs). The proposed Deep Learning Classification Engines (DCLE) comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers. Vehicular data received by the Road Side Units (RSUs) is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper. The proposed classifiers identify 18 different vehicular behavior types, the F1-scores ranging from 95.58% to 96.75%, much higher than the existing works. By running the classifiers on testbeds emulating edge servers, the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.

Abstracts:Industrial Internet of Things (IIoT) systems depend on a growing number of edge devices such as sensors, controllers, and robots for data collection, transmission, storage, and processing. Any kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire IIoT. Moreover, they can allow malicious software installed on end nodes to penetrate the network. This paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge devices. The proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority voting. Experimental evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy, F1 score, recall, and precision.

Abstracts:The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS, SLAMSA, (p, k)-Angelization, and (p, l)-Angelization, but these were found to be insufficient in terms of robust privacy and performance. (p, l)-Angelization was successful against different privacy disclosures, but it was not efficient. To the best of our knowledge, no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple records. In this paper, we suggest an improved version of (p, l)-Angelization based on a hybrid AI approach and privacy-preserving approach like Generalization. Fuzz-classification (p, l)-Angel uses artificial intelligence based fuzzy logic for classification, a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive attributes. We demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri Nets. The results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility.

Abstracts:For networking of big data applications, an essential issue is how to represent networks in vector space for further mining and analysis tasks, e.g., node classification, clustering, link prediction, and visualization. Most existing studies on this subject mainly concentrate on monoplex networks considering a single type of relation among nodes. However, numerous real-world networks are naturally composed of multiple layers with different relation types; such a network is called a multiplex network. The majority of existing multiplex network embedding methods either overlook node attributes, resort to node labels for training, or underutilize underlying information shared across multiple layers. In this paper, we propose Multiplex Network Infomax (MNI), an unsupervised embedding framework to represent information of multiple layers into a unified embedding space. To be more specific, we aim to maximize the mutual information between the unified embedding and node embeddings of each layer. On the basis of this framework, we present an unsupervised network embedding method for attributed multiplex networks. Experimental results show that our method achieves competitive performance on not only node-related tasks, such as node classification, clustering, and similarity search, but also a typical edge-related task, i.e., link prediction, at times even outperforming relevant supervised methods, despite that MNI is fully unsupervised.