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Future Internet
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Information and Future Internet Security, Trust and Privacy II
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Special Issue "Information and Future Internet Security, Trust and Privacy II"

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: 10 September 2023 | Viewed by 2850

Special Issue Editors

Dr. Weizhi Meng

E-Mail Website
Guest Editor
Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
Interests: cyber security; intrusion detection; mobile security and authentication; HCI security; malware analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Christian D. Jensen

E-Mail Website
Guest Editor
Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
Interests: security in ubiquitous computing; secure collaboration in open dynamic systems; pervasive computing environments; sensor networks and the Internet of Things (IoT)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, the Internet of things (IoT) enables billions of Internet-connected devices, e.g., smart sensors, to communicate and interact with each other over the network/Internet worldwide. It can offer remote monitoring and control, and is being adopted in many domains. For example, it is the basis for smart cities, helping to achieve better quality of life and lower consumption of resources. In addition, smartphones are the most commonly used IoT devices, and can help control washing machines, refrigerators, or cars. However, the IoT also faces many challenges concerning information and Internet security. For example, attackers can impersonate a relay node to compromise the integrity of information during communications. When they control or infect several internal nodes in an IoT network, the security of the whole distributed environment would be greatly threatened. Hence, there is a need to safeguard information and the Internet environment against the plethora of modern external and internal threats.

This Special Issue will focus on information and Internet security in an attempt to solicit the latest technologies, solutions, case studies, and prototypes on this topic.

Dr. Weizhi Meng
Dr. Christian D. Jensen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Related Special Issue

Published Papers (5 papers)

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Article
Intelligent Unsupervised Network Traffic Classification Method Using Adversarial Training and Deep Clustering for Secure Internet of Things
by
Weijie Zhang
,
Lanping Zhang
,
Xixi Zhang
,
Yu Wang
,
Pengfei Liu
and
Guan Gui
Future Internet 2023, 15(9), 298; https://doi.org/10.3390/fi15090298 (registering DOI) - 01 Sep 2023
Abstract
Network traffic classification (NTC) has attracted great attention in many applications such as secure communications, intrusion detection systems. The existing NTC methods based on supervised learning rely on sufficient labeled datasets in the training phase, but for most traffic datasets, it is difficult [...] Read more.
Network traffic classification (NTC) has attracted great attention in many applications such as secure communications, intrusion detection systems. The existing NTC methods based on supervised learning rely on sufficient labeled datasets in the training phase, but for most traffic datasets, it is difficult to obtain label information in practical applications. Although unsupervised learning does not rely on labels, its classification accuracy is not high, and the number of data classes is difficult to determine. This paper proposes an unsupervised NTC method based on adversarial training and deep clustering with improved network traffic classification (NTC) and lower computational complexity in comparison with the traditional clustering algorithms. Here, the training process does not require data labels, which greatly reduce the computational complexity of the network traffic classification through pretraining. In the pretraining stage, an autoencoder (AE) is used to reduce the dimension of features and reduce the complexity of the initial high-dimensional network traffic data features. Moreover, we employ the adversarial training model and a deep clustering structure to further optimize the extracted features. The experimental results show that our proposed method has robust performance, with a multiclassification accuracy of 92.2%, which is suitable for classification with a large number of unlabeled data in actual application scenarios. This paper only focuses on breakthroughs in the algorithm stage, and future work can be focused on the deployment and adaptation in practical environments. Full article
(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy II)
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Article
Applying Detection Leakage on Hybrid Cryptography to Secure Transaction Information in E-Commerce Apps
by
Mishall Al-Zubaidie
and
Ghanima Sabr Shyaa
Future Internet 2023, 15(8), 262; https://doi.org/10.3390/fi15080262 - 01 Aug 2023
Viewed by 379
Abstract
Technology advancements have driven a boost in electronic commerce use in the present day due to an increase in demand processes, regardless of whether goods, products, services, or payments are being bought or sold. Various goods are purchased and sold online by merchants [...] Read more.
Technology advancements have driven a boost in electronic commerce use in the present day due to an increase in demand processes, regardless of whether goods, products, services, or payments are being bought or sold. Various goods are purchased and sold online by merchants (M)s for large amounts of money. Nonetheless, during the transmission of information via electronic commerce, Ms’ information may be compromised or attacked. In order to enhance the security of e-commerce transaction data, particularly sensitive M information, we have devised a protocol that combines the Fernet (FER) algorithm with the ElGamal (ELG) algorithm. Additionally, we have integrated data leakage detection (DLD) technology to verify the integrity of keys, encryptions, and decryptions. The integration of these algorithms ensures that electronic-commerce transactions are both highly secure and efficiently processed. Our analysis of the protocol’s security and performance indicates that it outperforms the algorithms used in previous studies, providing superior levels of security and performance. Full article
(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy II)
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Article
KubeHound: Detecting Microservices’ Security Smells in Kubernetes Deployments
by
Giorgio Dell’Immagine
,
Jacopo Soldani
and
Antonio Brogi
Future Internet 2023, 15(7), 228; https://doi.org/10.3390/fi15070228 - 26 Jun 2023
Viewed by 617
Abstract
As microservice-based architectures are increasingly adopted, microservices security has become a crucial aspect to consider for IT businesses. Starting from a set of “security smells” for microservice applications that were recently proposed in the literature, we enable the automatic detection of such smells [...] Read more.
As microservice-based architectures are increasingly adopted, microservices security has become a crucial aspect to consider for IT businesses. Starting from a set of “security smells” for microservice applications that were recently proposed in the literature, we enable the automatic detection of such smells in microservice applications deployed with Kubernetes. We first introduce possible analysis techniques to automatically detect security smells in Kubernetes-deployed microservices. We then demonstrate the practical applicability of the proposed techniques by introducing KubeHound, an extensible prototype tool for automatically detecting security smells in microservice applications, and which already features a selected subset of the discussed analyses. We finally show that KubeHound can effectively detect instances of security smells in microservice applications by means of controlled experiments and by applying it to existing, third-party applications. Full article
(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy II)
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Article
Task-Aware Meta Learning-Based Siamese Neural Network for Classifying Control Flow Obfuscated Malware
by
Jinting Zhu
,
Julian Jang-Jaccard
,
Amardeep Singh
,
Paul A. Watters
and
Seyit Camtepe
Future Internet 2023, 15(6), 214; https://doi.org/10.3390/fi15060214 - 14 Jun 2023
Viewed by 565
Abstract
Malware authors apply different techniques of control flow obfuscation, in order to create new malware variants to avoid detection. Existing Siamese neural network (SNN)-based malware detection methods fail to correctly classify different malware families when such obfuscated malware samples are present in the [...] Read more.
Malware authors apply different techniques of control flow obfuscation, in order to create new malware variants to avoid detection. Existing Siamese neural network (SNN)-based malware detection methods fail to correctly classify different malware families when such obfuscated malware samples are present in the training dataset, resulting in high false-positive rates. To address this issue, we propose a novel task-aware few-shot-learning-based Siamese Neural Network that is resilient against the presence of malware variants affected by such control flow obfuscation techniques. Using the average entropy features of each malware family as inputs, in addition to the image features, our model generates the parameters for the feature layers, to more accurately adjust the feature embedding for different malware families, each of which has obfuscated malware variants. In addition, our proposed method can classify malware classes, even if there are only one or a few training samples available. Our model utilizes few-shot learning with the extracted features of a pre-trained network (e.g., VGG-16), to avoid the bias typically associated with a model trained with a limited number of training samples. Our proposed approach is highly effective in recognizing unique malware signatures, thus correctly classifying malware samples that belong to the same malware family, even in the presence of obfuscated malware variants. Our experimental results, validated by N-way on N-shot learning, show that our model is highly effective in classification accuracy, exceeding a rate >91%, compared to other similar methods. Full article
(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy II)
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Article
Protecting Function Privacy and Input Privacy in the Publicly Verifiable Outsourcing Computation of Polynomial Functions
by
Beibei Song
,
Dehua Zhou
,
Jiahe Wu
,
Xiaowei Yuan
,
Yiming Zhu
and
Chuansheng Wang
Future Internet 2023, 15(4), 152; https://doi.org/10.3390/fi15040152 - 21 Apr 2023
Cited by 1 | Viewed by 813
Abstract
With the prevalence of cloud computing, the outsourcing of computation has gained significant attention. Clients with limited computing power often outsource complex computing tasks to the cloud to save on computing resources and costs. In outsourcing the computation of functions, a function owner [...] Read more.
With the prevalence of cloud computing, the outsourcing of computation has gained significant attention. Clients with limited computing power often outsource complex computing tasks to the cloud to save on computing resources and costs. In outsourcing the computation of functions, a function owner delegates a cloud server to perform the function’s computation on the input received from the user. There are three primary security concerns associated with this process: protecting function privacy for the function owner, protecting input privacy for the user and guaranteeing that the cloud server performs the computation correctly. Existing works have only addressed privately verifiable outsourcing computation with privacy or publicly verifiable outsourcing computation without input privacy or function privacy. By using the technologies of homomorphic encryption, proxy re-encryption and verifiable computation, we propose the first publicly verifiable outsourcing computation scheme that achieves both input privacy and function privacy for matrix functions, which can be extended to arbitrary multivariate polynomial functions. We additionally provide a faster privately verifiable method. Moreover, the function owner retains control over the function. Full article
(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy II)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: IntruNet: A Hybrid Deep Learning Framework for Network Intrusion Detection
Authors: Roseline Oluwaseun Ogundokun (1); Chinecherem Umezuruike (2)
Affiliation: 1 Department of Multimedia Engineering, Kaunas University of Technology, Kaunas, Lithuania 2 Department of Software Engineering, Bowen University Iwo, Osun State, Nigeria
Abstract: Given the ever-changing nature of cyber-attacks, network security identification and prevention of network breaches offer significant difficulties. The current research introduces IntruNet, an innovative hybrid deep learning (DL) model developed for network intrusion detection (NID). IntruNet seeks to improve the reliability, effectiveness, and versatility of intrusion detection systems (IDS) by integrating the potential of multiple methodologies for DLs, such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNN). IntruNet uses the spatially distributed feature extraction (FE) powers of convolutional neural networks (CNNs) to identify tiny patterns in network connection data. LSTM models concurrently permit the simulation of time-dependent variations in network usage, which facilitates the recognition of complicated attack patterns. By incorporating each of these elements within a single structure and supplementing them with DNNs for classification, IntruNet provides an in-depth method for dependable intrusion detection. The suggested model undergoes training and evaluation employing broad datasets that include both normal and malicious communication over the network, permitting IntruNet to acquire distinguishing characteristics and make inferences to fresh arising instances of attack. The findings from experiments indicate that IntruNet surpasses conventional IDSs, with enhanced accuracy in detecting attacks and lowered false favourable rates (FPR). The deep hybrid learning (DL) framework of IntruNet facilitates instantaneous analysis of massive communication over the network, thus allowing the prompt identification and resolution of possible security violations. The framework's comprehensibility permits security specialists to obtain knowledge about the key characteristics that help with intrusion detection, thereby facilitating the examination and remediation of detected attacks. The contribution made by this research is the creation of IntruNet, a hybrid DL framework that overcomes the drawbacks associated with traditional IDSs. IntruNet demonstrates the possible use of DL approaches to improve network intrusion detection powers by integrating CNN, LSTM, and DNN. The suggested model presents an exciting possibility for enhancing the security posture of network facilities, thereby minimizing the potential hazards associated with constantly evolving cyber threats.

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