Current Issue : July-September Volume : 2023 Issue Number : 3 Articles : 5 Articles
As the network is closely related to people’s daily life, network security has become an important factor affecting the physical and mental health of human beings. Network flow classification is the foundation of network security. It is the basis for providing various network services such as network security maintenance, network monitoring, and network quality of service (QoS). Therefore, this field has always been a hot spot of academic and industrial research. Existing studies have shown that through appropriate data preprocessing techniques, machine learning methods can be used to classify network flows, most of which, however, are based on manually and expert-originated feature sets; it is a time-consuming and laborious work. Moreover, only features extracted by a single model can be used in classification tasks, which can easily make the model inefficient and prone to overfitting. In order to solve the abovementioned problems, this study proposes a multimodal automatic analysis framework based on spatial and sequential features. The framework is completely based on the deep learning method and realizes automatic extraction of two types of features, which is very suitable for processing large-flow information; this improves the efficiency of network flow classification. There are two types of frameworks based on pretraining and joint-training, respectively, with analyzing the advantages and disadvantages of them in practice. In terms of evaluation, compared with the previous methods, the experimental results show that the framework has good performance in both accuracy and stability....
The development of mobile Internet and the popularization of intelligent sensor devices greatly facilitate the generation and transmission of massive multimedia data including medical images and pathological models on the open network. The popularity of artificial intelligence (AI) technologies has greatly improved the efficiency of medical image recognition and diagnosis. However, it also poses new challenges to the security and privacy of medical data. The leakage of medical images related to users’ privacy is emerging one after another. The existing privacy protection methods based on cryptography or watermarking often bring a burden to image transmission. In this paper, we propose a privacy-preserving recognition network for medical images (called MPVCNet) to solve these problems. MPVCNet uses visual cryptography (VC) to transmit images by sharing. Benefiting from the secret-sharing characteristics of VC, MPVCNet can securely transmit images in clear text, which can both protect privacy and mitigate performance loss. Aiming at the problem that VC is easy to forge, we combine trusted computing environments (TEE) and blind watermarking technologies to embed verification information into sharing images. We further leverage the transfer learning technology to abate the side effect resulting from the use of visual cryptography. The results of the experiment show that our approach can maintain the trustworthiness and recognition performance of the recognition networks while protecting the privacy of medical images....
Message authentication and conditional privacy preservation are two critical security issues in VANETs (vehicular ad hoc networks). To achieve the corresponding security goals, many security technologies have been proposed so far. Identity-based pseudonyms and group signature-based schemes are two of the main technologies in recently published literature. However, the key escrow is difficult to achieve and pseudonym identities may reveal the physical location of the vehicle in the identity-based scheme. The global manager TA of VANETs knows the full keys given to the vehicles and can forge signatures under the vehicle’s key. Therefore, the exculpability cannot be satisfied in the group signature scheme. To address these security issues, a privacypreserving authentication scheme for VANETs with exculpability is proposed in this paper, which applies double key approach to realize the trusted communication between vehicle and road side units and TA by combining the advantage of group-based methods and identity-based methods. Security analysis shows that the security of our scheme can resist stronger attacks than previous schemes....
The attacks of cyber are rapidly increasing due to advanced techniques applied by hackers. Furthermore, cyber security is demanding day by day, as cybercriminals are performing cyberattacks in this digital world. So, designing privacy and security measurements for IoT-based systems is necessary for secure network. Although various techniques of machine learning are applied to achieve the goal of cyber security, but still a lot of work is needed against intrusion detection. Recently, the concept of hybrid learning gives more attention to information security specialists for further improvement against cyber threats. In the proposed framework, a hybrid method of swarm intelligence and evolutionary for feature selection, namely, PSO-GA (PSO-based GA) is applied on dataset named CICIDS-2017 before training the model. The model is evaluated using ELM-BA based on bootstrap resampling to increase the reliability of ELM. This work achieved highest accuracy of 100% on PortScan, Sql injection, and brute force attack, which shows that the proposed model can be employed effectively in cybersecurity applications....
This work aims to solve the specific problem in the Power Internet of Things (PIoT). PIoT is vulnerable to monitoring, tampering, forgery, and other attacks during frequent data interaction under the background of big data, leading to a severe threat to the power grid’s Information Security (ISEC). Cryptosystems can solve ISEC problems, such as confidentiality, data integrity, authentication, identity recognition, data control, and nonrepudiation. Thereupon, this work expounds on cryptography from public-key encryption and digital signature and puts forward the model of network information attack. Then, the security of the two cryptograms is certified against the two cyberattack modes. On this basis, an Identity-based Combined Encryption and Signature (IBCES) ensemble scheme is proposed by combining public-key encryption with the digital signature. Finally, the security of the proposed IBCES’s encryption and the signature schemes is verified, and the results prove their feasibility. The results show that the proposed IBCEs are effective and feasible, fully meeting the information confidentiality requirements. Additionally, smart grid against Information Security (ISEC) algorithms must comprehensively consider network resources and computing power. This work creatively combines the two cryptosystems. The proposal breaks the traditional key segmentation principle by applying the same key to different cryptosystems and ensures the independent security of the two cryptosystems. The conclusion provides technical support for future research on cryptography....
Loading....