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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 30783

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Prof. Dr. Yushu Zhang

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Guest Editor

College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: multimedia security; AI security; blockchain
Special Issues, Collections and Topics in MDPI journals

Dr. Zhongyun Hua

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Guest Editor

School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518000, China
Interests: cyberspace security; multimedia security; chaos theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cyber security is the protection of internet-connected systems such as hardware, software, and data from cyberthreats. These cyberattacks are usually aimed at accessing, changing, or destroying sensitive information, extorting money from users, or interrupting normal business processes. Recently, with the fast development of artificial intelligence (AI), some classical problems in cyber security have been solved. However, new technologies are gradually being used by criminals, which causes new security issues for cyberspace. The vulnerabilities of the system can be easily found, which makes the system easier to be attacked. When AI is applied to large-scale network attacks, attackers can adaptively generate attack programs and pass a large number of automated attacks. Using AI, attackers can hide attack behaviors, attack paths, etc., making it more difficult for defenders to detect and detect such attacks. AI technology may also be used to steal secret data from users, thereby causing more serious security problems. AI technology can be used to improve cyber security, and can also cause many new security issues for cyberspace. This Special Issue of Sensors is dedicated to original research and recent developments on cyber security and AI.

This Special Issue covers all topics relating to cyber security and AI.

Prof. Dr. Yushu Zhang
Dr. Zhongyun Hua
Guest Editors

Manuscript Submission Information

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Keywords

AI and machine learning for cybersecurity
adversairal attack for cybersecurity
generative adversarial networks for cybersecurity
automated and intelligent and data-driven cybersecurity model
AI security and neural networks
cloud computing security and AI
AI for multimedia networks security

Published Papers (13 papers)

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Research

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20 pages, 4136 KiB

Open AccessArticle

Scalable Learning Framework for Detecting New Types of Twitter Spam with Misuse and Anomaly Detection

by Jaeun Choi, Byunghwan Jeon and Chunmi Jeon

Sensors 2024, 24(7), 2263; https://doi.org/10.3390/s24072263 - 02 Apr 2024

Viewed by 446

Abstract

The growing popularity of social media has engendered the social problem of spam proliferation through this medium. New spam types that evade existing spam detection systems are being developed continually, necessitating corresponding countermeasures. This study proposes an anomaly detection-based framework to detect new Twitter spam, which works by modeling the characteristics of non-spam tweets and using anomaly detection to classify tweets deviating from this model as anomalies. However, because modeling varied non-spam tweets is challenging, the technique’s spam detection and false positive (FP) rates are low and high, respectively. To overcome this shortcoming, anomaly detection is performed on known spam tweets pre-detected using a trained decision tree while modeling normal tweets. A one-class support vector machine and an autoencoder with high detection rates are used for anomaly detection. The proposed framework exhibits superior detection rates for unknown spam compared to conventional techniques, while maintaining equivalent or improved detection and FP rates for known spam. Furthermore, the framework can be adapted to changes in spam conditions by adjusting the costs of detection errors. Full article

(This article belongs to the Special Issue Cyber Security and AI)

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20 pages, 16168 KiB

Open AccessArticle

A Synthetic Time-Series Generation Using a Variational Recurrent Autoencoder with an Attention Mechanism in an Industrial Control System

by Seungho Jeon and Jung Taek Seo

Sensors 2024, 24(1), 128; https://doi.org/10.3390/s24010128 - 26 Dec 2023

Viewed by 813

Abstract

Data scarcity is a significant obstacle for modern data science and artificial intelligence research communities. The fact that abundant data are a key element of a powerful prediction model is well known through various past studies. However, industrial control systems (ICS) are operated in a closed environment due to security and privacy issues, so collected data are generally not disclosed. In this environment, synthetic data generation can be a good alternative. However, ICS datasets have time-series characteristics and include features with short- and long-term temporal dependencies. In this paper, we propose the attention-based variational recurrent autoencoder (AVRAE) for generating time-series ICS data. We first extend the evidence lower bound of the variational inference to time-series data. Then, a recurrent neural-network-based autoencoder is designed to take this as the objective. AVRAE employs the attention mechanism to effectively learn the long-term and short-term temporal dependencies ICS data implies. Finally, we present an algorithm for generating synthetic ICS time-series data using learned AVRAE. In a comprehensive evaluation using the ICS dataset HAI and various performance indicators, AVRAE successfully generated visually and statistically plausible synthetic ICS data. Full article

(This article belongs to the Special Issue Cyber Security and AI)

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12 pages, 483 KiB

Open AccessArticle

Mobile Payment Protocol with Deniably Authenticated Property

by Yunzhuo Liu, Wen Huang, Ming Zhuo, Shijie Zhou and Mengshi Li

Sensors 2023, 23(8), 3927; https://doi.org/10.3390/s23083927 - 12 Apr 2023

Cited by 1 | Viewed by 1259

Abstract

Mobile payment services have been widely applied in our daily life, where users can conduct transactions in a convenient way. However, critical privacy concerns have arisen. Specifically, a risk of participating in a transaction is the disclosure of personal privacy. This might occur if, for example, the user pays for some special medicine, such as AIDS medicine or contraceptives. In this paper, we propose a mobile payment protocol that is suitable for mobile devices only with limited computing resources. In particular, the user in a transaction can confirm the identity of others in the same transaction while the user cannot show convincing evidence to prove that others also take part in the same transactions. We implement the proposed protocol and test its computation overhead. The experiment results corroborate that the proposed protocol is suitable for mobile devices with limited computing resources. Full article

(This article belongs to the Special Issue Cyber Security and AI)

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17 pages, 1700 KiB

Open AccessArticle

Invertible Privacy-Preserving Adversarial Reconstruction for Image Compressed Sensing

by Di Xiao, Yue Li and Min Li

Sensors 2023, 23(7), 3575; https://doi.org/10.3390/s23073575 - 29 Mar 2023

Viewed by 1046

Abstract

Since the advent of compressed sensing (CS), many reconstruction algorithms have been proposed, most of which are devoted to reconstructing images with better visual quality. However, higher-quality images tend to reveal more sensitive information in machine recognition tasks. In this paper, we propose a novel invertible privacy-preserving adversarial reconstruction method for image CS. While optimizing the quality, the reconstructed images are made to be adversarial samples at the moment of generation. For semi-authorized users, they can only obtain the adversarial reconstructed images, which provide little information for machine recognition or training deep models. For authorized users, they can reverse adversarial reconstructed images to clean samples with an additional restoration network. Experimental results show that while keeping good visual quality for both types of reconstructed images, the proposed scheme can provide semi-authorized users with adversarial reconstructed images with a very low recognizable rate, and allow authorized users to further recover sanitized reconstructed images with recognition performance approximating that of the traditional CS. Full article

(This article belongs to the Special Issue Cyber Security and AI)

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19 pages, 816 KiB

Open AccessArticle

Cyber Attacker Profiling for Risk Analysis Based on Machine Learning

by Igor Kotenko, Elena Fedorchenko, Evgenia Novikova and Ashish Jha

Sensors 2023, 23(4), 2028; https://doi.org/10.3390/s23042028 - 10 Feb 2023

Cited by 2 | Viewed by 2212

Abstract

The notion of the attacker profile is often used in risk analysis tasks such as cyber attack forecasting, security incident investigations and security decision support. The attacker profile is a set of attributes characterising an attacker and their behaviour. This paper analyzes the research in the area of attacker modelling and presents the analysis results as a classification of attacker models, attributes and risk analysis techniques that are used to construct the attacker models. The authors introduce a formal two-level attacker model that consists of high-level attributes calculated using low-level attributes that are in turn calculated on the basis of the raw security data. To specify the low-level attributes, the authors performed a series of experiments with datasets of attacks. Firstly, the requirements of the datasets for the experiments were specified in order to select the appropriate datasets, and, afterwards, the applicability of the attributes formed on the basis of such nominal parameters as bash commands and event logs to calculate high-level attributes was evaluated. The results allow us to conclude that attack team profiles can be differentiated using nominal parameters such as bash history logs. At the same time, accurate attacker profiling requires the extension of the low-level attributes list. Full article

(This article belongs to the Special Issue Cyber Security and AI)

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15 pages, 1443 KiB

Open AccessArticle

A Novel Fractional Accumulative Grey Model with GA-PSO Optimizer and Its Application

by Ruixiao Huang, Xiaofeng Fu and Yifei Pu

Sensors 2023, 23(2), 636; https://doi.org/10.3390/s23020636 - 05 Jan 2023

Cited by 5 | Viewed by 1054

Abstract

The prediction of cyber security situation plays an important role in early warning against cyber security attacks. The first-order accumulative grey model has achieved remarkable results in many prediction scenarios. Since recent events have a greater impact on future decisions, new information should be given more weight. The disadvantage of first-order accumulative grey models is that with the first-order accumulative method, equal weight is given to the original data. In this paper, a fractional-order cumulative grey model (FAGM) is used to establish the prediction model, and an intelligent optimization algorithm known as particle swarm optimization (PSO) combined with a genetic algorithm (GA) is used to determine the optimal order. The model discussed in this paper is used for the prediction of Internet cyber security situations. The results of a comparison with the traditional grey model GM(1,1), the grey model GM(1,n), and the fractional discrete grey seasonal model FDGSM(1,1) show that our model is suitable for cases with insufficient data and irregular sample sizes, and the prediction accuracy and stability of the model are better than those of the other three models. Full article

(This article belongs to the Special Issue Cyber Security and AI)

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22 pages, 11097 KiB

Open AccessArticle

A Secrecy Transmission Protocol with Energy Harvesting for Federated Learning

by Ping Xie, Fan Li, Ilsun You, Ling Xing, Honghai Wu and Huahong Ma

Sensors 2022, 22(15), 5506; https://doi.org/10.3390/s22155506 - 23 Jul 2022

Viewed by 1210

Abstract

In federated learning (FL), model parameters of deep learning are communicated between clients and the central server. To better train deep learning models, the spectrum resource and transmission security need to be guaranteed. Toward this end, we propose a secrecy transmission protocol based on energy harvesting and jammer selection for FL, in which the secondary transmitters can harvest energy from the primary source. Specifically, a secondary transmitter

{ST}_{i}

is first selected, which can offer the best transmission performance for the secondary users to access the primary frequency spectrum. Then, another secondary transmitter

{ST}_{n}

, which has the best channel for eavesdropping, is also chosen as a friendly jammer to provide secrecy service. Furthermore, we use outage probability (OP) and intercept probability (IP) as metrics to evaluate performance. Meanwhile, we also derive closed-form expressions of OP and IP of primary users and OP of secondary users for the proposed protocol, respectively. We also conduct a theoretical analysis of the optimal secondary transmission selection (OSTS) protocol. Finally, the performance of the proposed protocol is validated through numerical experiments. The results show that the secrecy performance of the proposed protocol is better than the OSTS and OCJS, respectively. Full article

(This article belongs to the Special Issue Cyber Security and AI)

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17 pages, 718 KiB

Open AccessArticle

CTTGAN: Traffic Data Synthesizing Scheme Based on Conditional GAN

by Jiayu Wang, Xuehu Yan, Lintao Liu, Longlong Li and Yongqiang Yu

Sensors 2022, 22(14), 5243; https://doi.org/10.3390/s22145243 - 13 Jul 2022

Cited by 5 | Viewed by 2238

Abstract

Most machine learning algorithms only have a good recognition rate on balanced datasets. However, in the field of malicious traffic identification, benign traffic on the network is far greater than malicious traffic, and the network traffic dataset is imbalanced, which makes the algorithm have a low identification rate for small categories of malicious traffic samples. This paper presents a traffic sample synthesizing model named Conditional Tabular Traffic Generative Adversarial Network (CTTGAN), which uses a Conditional Tabular Generative Adversarial Network (CTGAN) algorithm to expand the small category traffic samples and balance the dataset in order to improve the malicious traffic identification rate. The CTTGAN model expands and recognizes feature data, which meets the requirements of a machine learning algorithm for training and prediction data. The contributions of this paper are as follows: first, the small category samples are expanded and the traffic dataset is balanced; second, the storage cost and computational complexity are reduced compared to models using image data; third, discrete variables and continuous variables in traffic feature data are processed at the same time, and the data distribution is described well. The experimental results show that the recognition rate of the expanded samples is more than 0.99 in MLP, KNN and SVM algorithms. In addition, the recognition rate of the proposed CTTGAN model is better than the oversampling and undersampling schemes. Full article

(This article belongs to the Special Issue Cyber Security and AI)

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19 pages, 5298 KiB

Open AccessArticle

Sparse Adversarial Video Attacks via Superpixel-Based Jacobian Computation

by Zhenyu Du, Fangzheng Liu and Xuehu Yan

Sensors 2022, 22(10), 3686; https://doi.org/10.3390/s22103686 - 12 May 2022

Cited by 2 | Viewed by 1354

Abstract

Adversarial examples have aroused great attention during the past years owing to their threat to the deep neural networks (DNNs). Recently, they have been successfully extended to video models. Compared with image cases, the sparse adversarial perturbations in the videos can not only reduce the computation complexity, but also guarantee the crypticity of adversarial examples. In this paper, we propose an efficient attack to generate adversarial video perturbations with large sparsity in both the temporal (inter-frames) and spatial (intra-frames) domains. Specifically, we select the key frames and key pixels according to the gradient feedback of the target models by computing the forward derivative, and then add the perturbations on them. To overcome the problem of dimensional explosion in the video, we introduce super-pixels to decrease the number of pixels that need to compute gradients. The proposed method is finally verified under both the white-box and black-box settings. We estimate the gradients using natural evolution strategy (NES) in the black-box attacks. The experiments are conducted on two widely used datasets: UCF101 and HMDB51 versus two mainstream models: C3D and LRCN. Results show that compared with the state-of-the-art method, our method can achieve the similar attacking performance, but it pollutes only <1% pixels and costs less time to finish the attacks. Full article

(This article belongs to the Special Issue Cyber Security and AI)

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15 pages, 645 KiB

Open AccessArticle

Wet Paper Coding-Based Deep Neural Network Watermarking

by Xuan Wang, Yuliang Lu, Xuehu Yan and Long Yu

Sensors 2022, 22(9), 3489; https://doi.org/10.3390/s22093489 - 04 May 2022

Cited by 1 | Viewed by 2181

Abstract

In recent years, the wide application of deep neural network models has brought serious risks of intellectual property rights infringement. Embedding a watermark in a network model is an effective solution to protect intellectual property rights. Although researchers have proposed schemes to add watermarks to models, they cannot prevent attackers from adding and overwriting original information, and embedding rates cannot be quantified. Therefore, aiming at these problems, this paper designs a high embedding rate and tamper-proof watermarking scheme. We employ wet paper coding (WPC), in which important parameters are regarded as wet blocks and the remaining unimportant parameters are regarded as dry blocks in the model. To obtain the important parameters more easily, we propose an optimized probabilistic selection strategy (OPSS). OPSS defines the unimportant-level function and sets the importance threshold to select the important parameter positions and to ensure that the original function is not affected after the model parameters are changed. We regard important parameters as an unmodifiable part, and only modify the part that includes the unimportant parameters. We selected the MNIST, CIFAR-10, and ImageNet datasets to test the performance of the model after adding a watermark and to analyze the fidelity, robustness, embedding rate, and comparison schemes of the model. Our experiment shows that the proposed scheme has high fidelity and strong robustness along with a high embedding rate and the ability to prevent malicious tampering. Full article

(This article belongs to the Special Issue Cyber Security and AI)

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28 pages, 2317 KiB

Open AccessArticle

Classification and Explanation for Intrusion Detection System Based on Ensemble Trees and SHAP Method

by Thi-Thu-Huong Le, Haeyoung Kim, Hyoeun Kang and Howon Kim

Sensors 2022, 22(3), 1154; https://doi.org/10.3390/s22031154 - 03 Feb 2022

Cited by 67 | Viewed by 6369

Abstract

In recent years, many methods for intrusion detection systems (IDS) have been designed and developed in the research community, which have achieved a perfect detection rate using IDS datasets. Deep neural networks (DNNs) are representative examples applied widely in IDS. However, DNN models are becoming increasingly complex in model architectures with high resource computing in hardware requirements. In addition, it is difficult for humans to obtain explanations behind the decisions made by these DNN models using large IoT-based IDS datasets. Many proposed IDS methods have not been applied in practical deployments, because of the lack of explanation given to cybersecurity experts, to support them in terms of optimizing their decisions according to the judgments of the IDS models. This paper aims to enhance the attack detection performance of IDS with big IoT-based IDS datasets as well as provide explanations of machine learning (ML) model predictions. The proposed ML-based IDS method is based on the ensemble trees approach, including decision tree (DT) and random forest (RF) classifiers which do not require high computing resources for training models. In addition, two big datasets are used for the experimental evaluation of the proposed method, NF-BoT-IoT-v2, and NF-ToN-IoT-v2 (new versions of the original BoT-IoT and ToN-IoT datasets), through the feature set of the net flow meter. In addition, the IoTDS20 dataset is used for experiments. Furthermore, the SHapley additive exPlanations (SHAP) is applied to the eXplainable AI (XAI) methodology to explain and interpret the classification decisions of DT and RF models; this is not only effective in interpreting the final decision of the ensemble tree approach but also supports cybersecurity experts in quickly optimizing and evaluating the correctness of their judgments based on the explanations of the results. Full article

(This article belongs to the Special Issue Cyber Security and AI)

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Review

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18 pages, 3140 KiB

Open AccessReview

Detecting Malware by Analyzing App Permissions on Android Platform: A Systematic Literature Review

by Adeel Ehsan, Cagatay Catal and Alok Mishra

Sensors 2022, 22(20), 7928; https://doi.org/10.3390/s22207928 - 18 Oct 2022

Cited by 3 | Viewed by 3327

Abstract

Smartphone adaptation in society has been progressing at a very high speed. Having the ability to run on a vast variety of devices, much of the user base possesses an Android phone. Its popularity and flexibility have played a major role in making it a target of different attacks via malware, causing loss to users, both financially and from a privacy perspective. Different malware and their variants are emerging every day, making it a huge challenge to come up with detection and preventive methodologies and tools. Research has spawned in various directions to yield effective malware detection mechanisms. Since malware can adopt different ways to attack and hide, accurate analysis is the key to detecting them. Like any usual mobile app, malware requires permission to take action and use device resources. There are 235 total permissions that the Android app can request on a device. Malware takes advantage of this to request unnecessary permissions, which would enable those to take malicious actions. Since permissions are critical, it is important and challenging to identify if an app is exploiting permissions and causing damage. The focus of this article is to analyze the identified studies that have been conducted with a focus on permission analysis for malware detection. With this perspective, a systematic literature review (SLR) has been produced. Several papers have been retrieved and selected for detailed analysis. Current challenges and different analyses were presented using the identified articles. Full article

(This article belongs to the Special Issue Cyber Security and AI)

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17 pages, 2085 KiB

Open AccessReview

Deep Learning for Encrypted Traffic Classification and Unknown Data Detection

by Madushi H. Pathmaperuma, Yogachandran Rahulamathavan, Safak Dogan and Ahmet M. Kondoz

Sensors 2022, 22(19), 7643; https://doi.org/10.3390/s22197643 - 09 Oct 2022

Cited by 6 | Viewed by 3311

Abstract

Despite the widespread use of encryption techniques to provide confidentiality over Internet communications, mobile device users are still susceptible to privacy and security risks. In this paper, a novel Deep Neural Network (DNN) based on a user activity detection framework is proposed to identify fine-grained user activities performed on mobile applications (known as in-app activities) from a sniffed encrypted Internet traffic stream. One of the challenges is that there are countless applications, and it is practically impossible to collect and train a DNN model using all possible data from them. Therefore, in this work, we exploit the probability distribution of a DNN output layer to filter the data from applications that are not considered during the model training (i.e., unknown data). The proposed framework uses a time window-based approach to divide the traffic flow of activity into segments so that in-app activities can be identified just by observing only a fraction of the activity-related traffic. Our tests have shown that the DNN-based framework has demonstrated an accuracy of 90% or above in identifying previously trained in-app activities and an average accuracy of 79% in identifying previously untrained in-app activity traffic as unknown data when this framework is employed. Full article

(This article belongs to the Special Issue Cyber Security and AI)

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