Journal of Cyber Security and Mobility最新文献

{"title":"Deep Learning Based Hybrid Analysis of Malware Detection and Classification: A Recent Review","authors":"Syed Shuja Hussain, M. Razak, Ahmad Firdaus","doi":"10.13052/jcsm2245-1439.1314","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1314","url":null,"abstract":"Globally extensive digital revolutions involved with every process related to human progress can easily create the critical issues in security aspects. This is promoted due to the important factors like financial crises and geographical connectivity in worse condition of the nations. By this fact, the authors are well motivated to present a precise literature on malware detection with deep learning approach. In this literature, the basic overview includes the nature of nature of malware detection i.e., static, dynamic, and hybrid approach. Another major component of this articles is the investigation of the backgrounds from recently published and highly cited state-of-the-arts on malware detection, prevention and prediction with deep learning frameworks. The technologies engaged in providing solutions are utilized from AI based frameworks like machine learning, deep learning, and hybrid frameworks. The main motivations to produce this article is to portrait clear pictures of the option challenging issues and corresponding solution for developing robust malware-free devices. In the lack of a robust malware-free devices, highly growing geographical and financial disputes at wide globes can be extensively provoked by malicious groups. Therefore, exceptionally high demand of the malware detection devices requires a very strong recommendation to ensure the security of a nation. In terms preventing and recovery, Zero-day threats can be handled by recent methodology used in deep learning. In the conclusion, we also explored and investigated the future patterns of malware and how deals with in upcoming years. Such review may extend towards the development of IoT based applications used many fields such as medical devices, home appliances, academic systems.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"53 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139010420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network Malware Detection Using Deep Learning Network Analysis","authors":"Peng Xiao","doi":"10.13052/jcsm2245-1439.1312","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1312","url":null,"abstract":"Malware, short for malicious software, is designed for harmful purposes and threatens network security because it can propagate without human interaction by exploiting user’s vulnerabilities and carelessness. Having your system regularly scanned for malicious software is essential for keeping hackers at bay and avoiding the disclosure of sensitive data. The major drawbacks are the rapid creation of new malware variants, and it may become difficult to detect existing threats. With the ever-increasing volume of Android malware, the sophistication with which it can hide, and the potentially enormous value of data assets stored on Android devices, detecting or classifying Android malware is a big data problem. Security researchers have developed various malware detection and prevention programs for servers, gateways, user workstations, and mobile devices. Some offer centralized monitoring for malware detection software deployed on many systems or computers. The purpose of this essay is to critically examine the research that has been done specifically on malware detection. This paper proposes the Anti-Virus Software Detection for Malware with Deep Learning Network (AVSD-MDLN) framework to explore the possible threats. The two methods help in finding the threats. Dynamic Analysis for the Detection of Spyware (DA-DS) framework is framed to detect malicious malware, while the other is for classifying Android malware which is helped out through the Category in an Ensemble (CE) method. Prior malware detection methods are compared with the results of the proposed method. According to the research findings, the proposed approach achieves a higher projected time (0.5 sec) and detection accuracy (97.47%) than the existing situation machine learning and deep learning methodologies. Performance, correlation coefficient, and recall rate all improved in the suggested framework. Likewise, the negative rate (MPR) and the positive rate (PPR) also improved.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"147 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138978532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Efficient Intrusion Detection and Prevention System for DDOS Attack in WSN Using SS-LSACNN and TCSLR","authors":"Vikash Kumar Singh, D. Sivashankar, Kishlay Kundan, Sushmita Kumari","doi":"10.13052/jcsm2245-1439.1315","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1315","url":null,"abstract":"Sensor Nodes (SNs) are utilized by Wireless Sensor Networks (WSNs) to recognize their environment; in addition, the WSN delivers data from sensing nodes to the sink. The WSNs are exposed to several security threats owing to the broadcast performance of transmission along with the increase in the growth of application regions. Countermeasures like Intrusion Detection and Prevention Systems (IDPS) should be adopted to overcome the aforementioned attacks. By implementing these systems, several intrusions can be detected in WSN; also, WSN can be prevented from various security attacks. Therefore, identifying the general attack that influences the SNs mentioned as Distributed Denial of Service (DDoS) attack and recuperating the data utilizing Soft Swish (SS)-Linear Scaling-centered Adam Convolution Neural Network (SS-LSACNN) along with Two’s Compliment Shift Reverse (TCSLR) operation are the intentions of this work. Firstly, for extracting the vital features, the data gathered as of the dataset are utilized. After that, the extracted features are pre-processed. It is then utilized for attack detection. The null features and the redundant data are removed in preprocessing. By employing the Correlation Coefficient-centered Synthetic Minority Oversampling Technique (CC-SMOTE) methodology, data separation regarding classes and data balancing was performed to prevent the imbalance issue. Subsequently, to provide the preprocessed data for attack detection, the Numeralization and feature scaling are executed. After that, by utilizing Chebyshev Distance (CD)-centric K-Means Algorithm (KMA), the real-time SNs are initialized as well as clustered. The data gathered as of the SNs are utilized for attack detection following the clustering phase. Following the detection phase, the data being attacked are amassed in the log file; similarly, the non-attacked data are inputted into the prevention phase. Next, the experiential analysis is carried out for examining the proposed system’s efficacy. The outcomes revealed that the proposed model exhibits 98.15% accuracy, 97.59% sensitivity, 95.72% specificity, and 95.48% F-measure, which displays the proposed model’s efficacy.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"16 45","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138980839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Malware Cyber Threat Intelligence System for Internet of Things (IoT) Using Machine Learning","authors":"Peng Xiao","doi":"10.13052/jcsm2245-1439.1313","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1313","url":null,"abstract":"Cyber Intelligence (CI) is a sophisticated security solution that uses machine learning models to protect networks against cyber-attack. Security concerns to IoT devices are exacerbated because of their inherent weaknesses in memory systems, physical and online interfaces, and network services. IoT devices are vulnerable to attacks because of the communication channels. That raises the risk of spoofing and Denial-of-Service (DoS) attacks on the entire system, which is a severe problem. Since the IoT ecosystem does not have encryption and access restrictions, cloud-based communications and data storage have become increasingly popular. An IoT-based Cyber Threat Intelligence System (IoT-CTIS) is designed in this article to detect malware and security threads using a machine learning algorithm. Because hackers are continuously attempting to get their hands on sensitive information, it is important that IoT devices have strong authentication measures in place. Multifactor authentication, digital certificates, and biometrics are just some of the methods that may be used to verify the identity of an Internet of Things device. All devices use Machine Learning (ML) assisted Logistic Regression (LR) techniques to address memory and Internet interface vulnerabilities. System integrity concerns, such as spoofing and Denial of Service (DoS) attacks, must be minimized using the Random Forest (RF) Algorithm. Default passwords are often provided with IoT devices, and many users don’t bother to change them, making it simple for cybercriminals to get access. In other instances, people design insecure passwords that are easy to crack. The results of the experiments show that the method outperforms other similar strategies in terms of identification and wrong alarms. Checking your alarm system’s functionality both locally and in terms of its connection to the monitoring centre is why you do it. Make sure your alarm system is working properly by checking it on a regular basis. It is recommended that you do system tests at least once every three months. The experimental analysis of IoT-CTIS outperforms the method in terms of accuracy (90%), precision (90%), F-measure (88%), Re-call (90%), RMSE (15%), MSE (5%), TPR (89%), TNR (8%), FRP (89%), FNR (8%), Security (93%), MCC (92%).","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"27 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138981189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Update Algorithm of Secure Computer Database Based on Deep Belief Network","authors":"Liusuo Huang, Yan Song","doi":"10.13052/jcsm2245-1439.1311","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1311","url":null,"abstract":"In order to ensure the security of large-scale data transmission in a short time and in a wide range during online database updating, this paper presents a secure computer database updating algorithm based on DBN (Deep Belief Network). In this paper, the model adopts multi-layer depth structure for unsupervised feature learning, maps high-dimensional and nonlinear intrusion data to low-dimensional space, establishes the relationship mapping between high-dimensional and low-dimensional, and then uses fine-tuning algorithm to transform the model to achieve the best expression of features. At the same time, this method improves the data processing and method model without destroying the learned knowledge of the model and seriously affecting the real-time performance of detection. In order to overcome the problem of system instability caused by fixed empirical learning rate, this paper proposes a learning rate optimization strategy based on energy change. In the process of feature extraction, the features of different hidden layers are extracted to form combined features. Experiments show that the detection rate of this method can reach 95.31%, and the false alarm rate is 2.14%. This verifies the effectiveness of the secure computer database updating algorithm in this paper. Which can ensure the online update of the secure computer database.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"30 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138981153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of the Security of Internet of Multimedia Things in Wireless Environment","authors":"Nabeel Mahdy Haddad, Mustafa sabah Mustafa, H. S. Salih, M. Jaber, M. H. Ali","doi":"10.13052/jcsm2245-1439.1316","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1316","url":null,"abstract":"The Internet of Things (IoT) and real-time flexibility improve people’s lives, and IoT applications rely heavily on multimedia sensors and devices. An interconnected network of IoT multimedia devices has made the Internet of Medical Things (IoMT). It creates massive data distinct from what the Internet of Things (IoT) produced. Smart traffic monitoring and smart hospitals are only a few examples of real-time deployment applications. IoMT data and decision-making must be made quickly since it directly impacts human life. The security heterogeneity of optimization issues is a significant challenge for enabling multimedia applications on the IoT. The IoMT has difficulty achieving low-cost data collecting while maintaining data security. An Internet of Multimedia Things in a wireless environment (IoMT-WE) system decreases the bandwidth and privacy risk caused by the revocation list, ensures the integrity of batch verification information, and corresponds with Vehicular ad hoc network (VANET) security performance. The proposed method uses random subsampling and chaotic convolution to collect numerous images. The sampling method is safe since the measurement matrix is controlled by chaos. As part of the IoMT architecture, wireless multimedia sensor nodes can be more easily deployed over the long term for real-time multimedia. The Wireless Multimedia Sensor Network (WMSN) comprises nodes that can capture both multimedia and non-multimedia data. The ioMT-WE system has been tested and found to be secure and effective.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"24 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139010598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Priori Algorithm Based Network Security Situational Awareness Multi-Source Data Correlation Analysis Method","authors":"Wei Li, Jianjun Li, Chengting Zhang, Guang Yao, Xue Xu","doi":"10.13052/jcsm2245-1439.1263","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1263","url":null,"abstract":"In the context of the information age, the Internet has developed rapidly, but the accompanying network security threats have also become an issue that cannot be ignored. In order to effectively respond to these threats and improve the data processing capabilities of network security situational awareness, the study focuses on the challenges of multi-source data processing and proposes a multi-source data association analysis method based on the A priori algorithm. This method aims to deeply explore the implicit relationships between data and provide stronger support for network attack detection. In addition, the study also designed a multi-level evaluation method based on coefficient of variation indicators, aiming to provide a more objective and comprehensive evaluation of the detection results. After a series of experimental verification, the proposed correlation analysis method has achieved significant results in detecting phishing attacks and DOS attacks, with detection rates of 90.3% and 93.8%, respectively. At the same time, the multi-level evaluation method has also been experimentally proven to provide more reasonable and accurate results for data evaluation. The methods and technologies proposed in the study can not only improve the multi-source data processing ability of network security situational awareness, but also provide valuable references for future network security research and practice.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"30 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139264283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Anomaly Detection in Vehicular CAN Based on Bi-LSTM","authors":"Xiaopeng Kan, Zhihong Zhou, Lihong Yao, Yuxin Zuo","doi":"10.13052/jcsm2245-1439.1251","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1251","url":null,"abstract":"Controller Area Network (CAN) is one of the most widely used in-vehicle networks in modern vehicles. Due to the lack of security mechanisms such as encryption and authentication, CAN is vulnerable to external hackers in the intelligent network environment. In the paper, a lightweight CAN bus anomaly detection model based on the Bi-LSTM model is proposed. The Bi-LSTM model learns ID sequence correlation features to detect anomalies. At the same time, the Attention mechanism is introduced to improve the model’s efficiency. The paper focuses on replay attacks, denial of service attacks and fuzzing attacks. The experimental results show that the anomaly detection model based on Bi-LSTM can detect three attack types quickly and accurately.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"252 1","pages":"629-652"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85187481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Security Access Control Systems in Fog Computing Environment","authors":"Junlin Zhang","doi":"10.13052/jcsm2245-1439.1252","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1252","url":null,"abstract":"Fog computing is a computing environment that can respond to user operational needs in real time. Aiming at the shortcomings of user privacy protection performance and structural performance, a method of completely hiding access structures is proposed under the framework of cloud and mist computing. The cuckoo filter is applied to the fog computing environment, and users are detected through fog nodes. If an attribute is detected to exist in the fully hidden access structure, the mapping function between the attribute and the access structure line number is returned. The research results show that with the increase of the number of attributes, the advantage of attribute confirmation time for fog servers is gradually obvious; The overall delay of fog computing is shorter, the Time To Live (TTL) is longer, the average delay is only 3 ms, and the delay is lower; The completely hidden access structure constructed by the cuckoo algorithm occupies only 1% of the total system steps, which can more effectively achieve user privacy protection without increasing overhead. The proposed scheme greatly reduces the amount of computation while fully protecting user privacy, and meets the needs of users for fast and secure access.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48606815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum Image Encryption Algorithm Incorporating Bit-plane Color Representation and Real Ket Model","authors":"Xv Zhou, Jinwen He","doi":"10.13052/jcsm2245-1439.1257","DOIUrl":"https://doi.org/10.13052/jcsm2245-1439.1257","url":null,"abstract":"Image is one of the most important carriers of information that humans transmit on a daily basis. Therefore, the security of images in the transmission process has been a key study subject. A quantum bit-plane representation of the Real Ket model (QBRK) is proposed, which requires 2n+4 and 2n+6 quantum bits to represent gray-scale and color images of 22n−k×2k size, respectively. On the basis of the QBRK model and chaotic system, an image encryption algorithm is proposed according to pixel position encoding for slice dislocation and quantum bit-plane XOR operation. First, we use a modified logistics chaos system to generate two matrices that perform matrix determinant transformations in the bit-plane. Then, we perform an XOR operation on the pixel values based on the parity bit-plane. Finally, the pixel diffusion is completed by permutation with each cut encoding in the QBRK model. According to the simulation outcomes and security analysis, the encryption algorithm is very efficient and well resists state-of-the-art attacks.","PeriodicalId":37820,"journal":{"name":"Journal of Cyber Security and Mobility","volume":"113 1","pages":"757-784"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79079828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}