SECURITY AND PRIVACY最新文献_第3页

Anomaly detection in IoT environment using machine learning 利用机器学习检测物联网环境中的异常情况

SECURITY AND PRIVACY Pub Date : 2024-01-01 DOI: 10.1002/spy2.366

Harini Bilakanti, Sreevani Pasam, Varshini Palakollu, Sairam Utukuru

引用次数: 0

Privacy‐preserving in the smart healthcare system using steganography and chaotic functions based on DNA 利用基于 DNA 的隐写术和混沌函数在智能医疗系统中保护隐私

SECURITY AND PRIVACY Pub Date : 2023-12-28 DOI: 10.1002/spy2.363

Habib Esmaeelzadeh Rostam, Homayun Motameni, R. Enayatifar

{"title":"Privacy‐preserving in the smart healthcare system using steganography and chaotic functions based on DNA","authors":"Habib Esmaeelzadeh Rostam, Homayun Motameni, R. Enayatifar","doi":"10.1002/spy2.363","DOIUrl":"https://doi.org/10.1002/spy2.363","url":null,"abstract":"The smart healthcare system is one of the Internet of Things‐based applications that are increasingly used nowadays. One of the requirements of this system is privacy‐preserving, which should protect the disclosure of the patient record contents. In the present paper, a combination of chaotic functions and a new DNA‐based steganography method, and image blocking are suggested to protect patients' privacy‐preserving. First, the image is blocked, and the initial key for the chaotic function will be gained by using the centers of the blocks. Then the data and image are transformed into DNA sequences. Finally, one of the randomly chosen DNA strands of data is XOR with the center of one randomly selected block and will be placed in one of the block pixels. The failure to send the initial key separately to generate random numbers, as well as the random selection of secret data bits and image pixels for steganography has increased the security of the proposed method. The simulation results not only indicate the quality of the stego‐image but also show better performance of the proposed method than the existing methods.","PeriodicalId":506233,"journal":{"name":"SECURITY AND PRIVACY","volume":"143 5‐6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139149350","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}

引用次数: 0

On the security of a novel privacy‐preserving authentication scheme for V2G networks 论 V2G 网络新型隐私保护认证方案的安全性

SECURITY AND PRIVACY Pub Date : 2023-11-30 DOI: 10.1002/spy2.357

Mubeen Javed, Muhammad Arslan Akram, Adnan Noor Mian, Saru Kumari

引用次数: 0

Botnet‐based IoT network traffic analysis using deep learning 利用深度学习分析基于僵尸网络的物联网网络流量

SECURITY AND PRIVACY Pub Date : 2023-11-29 DOI: 10.1002/spy2.355

N. J. Singh, Nazrul Hoque, Kh Robindro Singh, D. K. Bhattacharyya

引用次数: 0

Detection of attacks on software defined networks using machine learning techniques and imbalanced data handling methods 利用机器学习技术和不平衡数据处理方法检测对软件定义网络的攻击

SECURITY AND PRIVACY Pub Date : 2023-11-29 DOI: 10.1002/spy2.350

Heba A. Hassan, E. E. Hemdan, W. El-shafai, Mona Shokair, F. A. Abd El‐Samie

{"title":"Detection of attacks on software defined networks using machine learning techniques and imbalanced data handling methods","authors":"Heba A. Hassan, E. E. Hemdan, W. El-shafai, Mona Shokair, F. A. Abd El‐Samie","doi":"10.1002/spy2.350","DOIUrl":"https://doi.org/10.1002/spy2.350","url":null,"abstract":"Software‐defined networks (SDNs) have gained popularity in recent years as a solution for the fundamental issues that affect traditional dispersed networks. The primary advantage of SDNs is the decoupling of the control plane from the data plane, which increases the flexibility of the network. The SDN represents a network architecture of the next generation, however, its configuration options are centralized, leaving it open for cyber‐attacks. This paper concentrates on the early identification of attacks in an SDN environment. When malicious traffic is affecting in an SDN topology, an artificial intelligence (AI) module in the topology is used to detect the attack and stop the attack source using machine learning (ML) techniques. The architecture presented in this research allows for the comparison of several ML classification techniques that are used to identify different sorts of network attacks. For attack detection, eight ML techniques are used, namely logistic regression (LR), linear discriminant analysis (LDA), Naïve Bayes (NB), k‐nearest neighbor (KNN), classification and regression tree (CART), AdaBoost (AB), random forest (RF), and support‐vector machine (SVM) classifiers. These techniques are tested on the InSDN dataset, which is a novel attack‐specific SDN dataset. The results show that the highest accuracy of 98.6% is achieved with the LDA classifier. Further improvement in the accuracy of classification models is observed when random over‐sampling, synthetic minority oversampling technique (SMOTE), random under‐sampling, and under‐sampling with Tomek links and near‐miss concept are applied to address the class imbalance problem. After applying these methods, the LDA classifier showed an accuracy of 98.79%.","PeriodicalId":506233,"journal":{"name":"SECURITY AND PRIVACY","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139214093","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}

引用次数: 0

Secure data access using blockchain technology through IoT cloud and fabric environment 通过物联网云和结构环境使用区块链技术确保数据访问安全

SECURITY AND PRIVACY Pub Date : 2023-11-23 DOI: 10.1002/spy2.356

Sangeeta Gupta, Premkumar Chithaluru, May El Barachi, Manoj Kumar

{"title":"Secure data access using blockchain technology through IoT cloud and fabric environment","authors":"Sangeeta Gupta, Premkumar Chithaluru, May El Barachi, Manoj Kumar","doi":"10.1002/spy2.356","DOIUrl":"https://doi.org/10.1002/spy2.356","url":null,"abstract":"In the current landscape, staying abreast of the latest technological advancements is a formidable challenge, especially given the deluge of data inundating the internet. The realization has dawned that effectively managing the surge in emerging data necessitates the integration of multiple technologies. In pursuit of this objective, the Internet of Things (IoT), renowned for its sensor‐based data capture capabilities, is frequently coupled with blockchain technology to ensure secure data storage and access. This amalgamation, in turn, leverages the cloud environment when data volume surpasses a machine's processing capacity, thus mitigating infrastructure and maintenance costs. This study endeavors to optimize data storage within the blocks of the blockchain (BCT) by storing an index that points to the actual data. This innovative approach not only conserves storage space but also enhances operational efficiency. Furthermore, it simplifies the task of identifying malicious or faulty nodes deployed at different locations for data capture within the prescribed time frame. To exemplify this implementation, a case study is presented, focusing on securing user votes through the creation of contracts. The results showcased underscore the preference for a permissioned blockchain, such as Fabric, over a permissionless one, like Ethereum, particularly in the context of security considerations. The findings reveal that as the number of operations (in this case, votes cast) increases, Ethereum's performance deteriorates, while Fabric exhibits exceptional robustness. Additionally, the study analyzes sensor data simulated via IoT nodes before and after the application of security algorithms to underscore the significance of the proposed Secure Cloud‐Based Blockchain (SCB2) model. The analysis encompasses various facets, including the creation, validation, and computation times of transactions and blocks within a node, and positions the model favorably in comparison to existing literature.","PeriodicalId":506233,"journal":{"name":"SECURITY AND PRIVACY","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139245161","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}

引用次数: 0

Safeguarding the IoT: Taxonomy, security solutions, and future research opportunities 保护物联网：分类、安全解决方案和未来研究机会

SECURITY AND PRIVACY Pub Date : 2023-11-20 DOI: 10.1002/spy2.354

Alay Patel, Devam Patel, Riya Kakkar, P. Oza, Smita Agrawal, S. Tanwar, Ravi Sharma, Nagendar Yamsani

引用次数: 0