The Impact of Domain Name Server(DNS)over Hypertext Transfer Protocol Secure(HTTPS)on Cyber Security:Limitations,Challenges,and Detection Techniques

The DNS over HTTPS(Hypertext Transfer Protocol Secure)(DoH)is a new technology that encrypts DNS traffic,enhancing the privacy and security of end-users.However,the adoption of DoH is still facing several research challenges,such as ensuring security,compatibility,standardization,performance,privacy,and increasing user awareness.DoH significantly impacts network security,including better end-user privacy and security,challenges for network security professionals,increasing usage of encrypted malware communication,and difficulty adapting DNS-based security measures.Therefore,it is important to understand the impact of DoH on network security and develop newprivacy-preserving techniques to allowthe analysis of DoH traffic without compromising user privacy.This paper provides an in-depth analysis of the effects of DoH on cybersecurity.We discuss various techniques for detecting DoH tunneling and identify essential research challenges that need to be addressed in future security studies.Overall,this paper highlights the need for continued research and development to ensure the effectiveness of DoH as a tool for improving privacy and security.

Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection

Glaucoma is a progressive eye disease that can lead to blindness if left untreated.Early detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized expertise.This study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine(EGWO-SVM)method.The proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global features.The enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for classification.To evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,respectively.The results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of Glaucoma.This study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.

QoS-Aware Cloud Service Optimization Algorithm in Cloud Manufacturing Environment

In a cloud manufacturing environment with abundant functionally equivalent cloud services,users naturally desire the highest-quality service(s).Thus,a comprehensive measurement of quality of service(QoS)is needed.Opti-mizing the plethora of cloud services has thus become a top priority.Cloud ser-vice optimization is negatively affected by untrusted QoS data,which are inevitably provided by some users.To resolve these problems,this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantification mechanisms.Untrusted data are assessed by an information correction method.The weights discovered by the variable precision Rough Set,which mined the evaluation indicators from historical data,providing a comprehensive performance ranking of service quality.The manufacturing cloud service optimization algorithm thus provides a quantitative reference for service selection.In experimental simulations,this method recommended the optimal services that met users’needs,and effectively reduced the impact of dis-honest users on the selection results.

Multi-Scale Attention-Based Deep Neural Network for Brain Disease Diagnosis

Whole brain functional connectivity(FC)patterns obtained from resting-state functional magnetic resonance imaging(rs-fMRI)have been widely used in the diagnosis of brain disorders such as autism spectrum disorder(ASD).Recently,an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease classification.However,the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease classification.In this paper,we proposed a multi-scale attention-based deep neural network(MSA-DNN)model to classify FC patterns for the ASD diagnosis.The model was implemented by adding a flexible multi-scale attention(MSA)module to the auto-encoder based backbone DNN,which can extract multi-scale features of the FC patterns and change the level of attention for different FCs by continuous learning.Our model will reinforce the weights of important FC features while suppress the unimportant FCs to ensure the sparsity of the model weights and enhance the model interpretability.We performed systematic experiments on the large multi-sites ASD dataset with both ten-fold and leaveone-site-out cross-validations.Results showed that our model outperformed classical methods in brain disease classification and revealed robust intersite prediction performance.We also localized important FC features and brain regions associated with ASD classification.Overall,our study further promotes the biomarker detection and computer-aided classification for ASD diagnosis,and the proposed MSA module is flexible and easy to implement in other classification networks.

A Cross-Domain Trust Model of Smart City IoT Based on Self-Certification

Smart city refers to the information system with Intemet of things and cloud computing as the core tec hnology and government management and industrial development as the core content,forming a large scale,heterogeneous and dynamic distributed Internet of things environment between different Internet of things.There is a wide demand for cooperation between equipment and management institutions in the smart city.Therefore,it is necessary to establish a trust mechanism to promote cooperation,and based on this,prevent data disorder caused by the interaction between honest terminals and malicious temminals.However,most of the existing research on trust mechanism is divorced from the Internet of things environment,and does not consider the characteristics of limited computing and storage capacity and large differences of Internet of hings devices,resuling in the fact that the research on abstract trust trust mechanism cannot be directly applied to the Internet of things;On the other hand,various threats to the Internet of things caused by security vulnerabilities such as collision attacks are not considered.Aiming at the security problems of cross domain trusted authentication of Intelligent City Internet of things terminals,a cross domain trust model(CDTM)based on self-authentication is proposed.Unlike most trust models,this model uses self-certified trust.The cross-domain process of internet of things(IoT)terminal can quickly establish a trust relationship with the current domain by providing its trust certificate stored in the previous domain interaction.At the same time,in order to alleviate the collision attack and improve the accuracy of trust evaluation,the overall trust value is calculated by comprehensively considering the quantity weight,time attenuation weight and similarity weight.Finally,the simulation results show that CDTM has good anti collusion attack ability.The success rate of malicious interaction will not increase significantly.Compared with other models,the resource consumption of our proposed model is significantly reduced.

Identity-Based Edge Computing Anonymous Authentication Protocol

With the development of sensor technology and wireless communication technology,edge computing has a wider range of applications.The privacy protection of edge computing is of great significance.In the edge computing system,in order to ensure the credibility of the source of terminal data,mobile edge computing(MEC)needs to verify the signature of the terminal node on the data.During the signature process,the computing power of edge devices such as wireless terminals can easily become the bottleneck of system performance.Therefore,it is very necessary to improve efficiency through computational offloading.Therefore,this paper proposes an identitybased edge computing anonymous authentication protocol.The protocol realizes mutual authentication and obtains a shared key by encrypting the mutual information.The encryption algorithm is implemented through a thresholded identity-based proxy ring signature.When a large number of terminals offload computing,MEC can set the priority of offloading tasks according to the user’s identity and permissions,thereby improving offloading efficiency.Security analysis shows that the scheme can guarantee the anonymity and unforgeability of signatures.The probability of a malicious node forging a signature is equivalent to cracking the discrete logarithm puzzle.According to the efficiency analysis,in the case of MEC offloading,the computational complexity is significantly reduced,the computing power of edge devices is liberated,and the signature efficiency is improved.

An Efficient Impersonation Attack Detection Method in Fog Computing

Fog computing paradigm extends computing,communication,storage,and network resources to the network’s edge.As the fog layer is located between cloud and end-users,it can provide more convenience and timely services to end-users.However,in fog computing(FC),attackers can behave as real fog nodes or end-users to provide malicious services in the network.The attacker acts as an impersonator to impersonate other legitimate users.Therefore,in this work,we present a detection technique to secure the FC environment.First,we model a physical layer key generation based on wireless channel characteristics.To generate the secret keys between the legitimate users and avoid impersonators,we then consider a Double Sarsa technique to identify the impersonators at the receiver end.We compare our proposed Double Sarsa technique with the other two methods to validate our work,i.e.,Sarsa and Q-learning.The simulation results demonstrate that the method based on Double Sarsa outperforms Sarsa and Q-learning approaches in terms of false alarm rate(FAR),miss detection rate(MDR),and average error rate(AER).

Authentication of Vehicles and Road Side Units in Intelligent Transportation System

Security threats to smart and autonomous vehicles cause potential consequences such as traffic accidents,economically damaging traffic jams,hijacking,motivating to wrong routes,and financial losses for businesses and governments.Smart and autonomous vehicles are connected wirelessly,which are more attracted for attackers due to the open nature of wireless communication.One of the problems is the rogue attack,in which the attacker pretends to be a legitimate user or access point by utilizing fake identity.To figure out the problem of a rogue attack,we propose a reinforcement learning algorithm to identify rogue nodes by exploiting the channel state information of the communication link.We consider the communication link between vehicle-to-vehicle,and vehicle-to-infrastructure.We evaluate the performance of our proposed technique by measuring the rogue attack probability,false alarm rate(FAR),mis-detection rate(MDR),and utility function of a receiver based on the test threshold values of reinforcement learning algorithm.The results show that the FAR and MDR are decreased significantly by selecting an appropriate threshold value in order to improve the receiver’s utility.

Power Allocation Strategy for Secret Key Generation Method in Wireless Communications

Secret key generation(SKG)is an emerging technology to secure wireless communication from attackers.Therefore,the SKG at the physical layer is an alternate solution over traditional cryptographic methods due to wireless channels’uncertainty.However,the physical layer secret key generation(PHY-SKG)depends on two fundamental parameters,i.e.,coherence time and power allocation.The coherence time for PHY-SKG is not applicable to secure wireless channels.This is because coherence time is for a certain period of time.Thus,legitimate users generate the secret keys(SKs)with a shorter key length in size.Hence,an attacker can quickly get information about the SKs.Consequently,the attacker can easily get valuable information from authentic users.Therefore,we considered the scheme of power allocation to enhance the secret key generation rate(SKGR)between legitimate users.Hence,we propose an alternative method,i.e.,a power allocation,to improve the SKGR.Our results show 72%higher SKGR in bits/sec by increasing power transmission.In addition,the power transmission is based on two important parameters,i.e.,epsilon and power loss factor,as given in power transmission equations.We found out that a higher value of epsilon impacts power transmission and subsequently impacts the SKGR.The SKGR is approximately 40.7%greater at 250 from 50 mW at epsilon=1.The value of SKGR is reduced to 18.5%at 250 mW when epsilonis 0.5.Furthermore,the transmission power is also measured against the different power loss factor values,i.e.,3.5,3,and 2.5,respectively,at epsilon=0.5.Hence,it is concluded that the value of epsilon and power loss factor impacts power transmission and,consequently,impacts the SKGR.

Emotion Recognition from Occluded Facial Images Using Deep Ensemble Model

Facial expression recognition has been a hot topic for decades,but high intraclass variation makes it challenging.To overcome intraclass variation for visual recognition,we introduce a novel fusion methodology,in which the proposed model first extract features followed by feature fusion.Specifically,RestNet-50,VGG-19,and Inception-V3 is used to ensure feature learning followed by feature fusion.Finally,the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification.The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy.To evaluate the efficiency of the proposed model,we use two wild benchmark datasets Real-world Affective Faces Database(RAF-DB)and AffectNet for facial expression recognition.The proposed model classifies the emotions into seven different categories namely:happiness,anger,fear,disgust,sadness,surprise,and neutral.Furthermore,the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost,convergence and accuracy based on a standard problem specific to classification applications.