Security Analysis for a VANET Privacy Protection Scheme

Vehicular ad hoc network(VANET)is a self-organizing wireless sensor network model,which is extensively used in the existing traffic.Due to the openness of wireless channel and the sensitivity of traffic information,data transmission process in VANET is vulnerable to leakage and attack.Authentication of vehicle identitywhile protecting vehicle privacy information is an advantageous way to improve the security of VANET.We propose a scheme based on fair blind signature and secret sharing algorithm.In this paper,we prove that the scheme is feasible through security analysis.

Efficient Virtual Resource Allocation in Mobile Edge Networks Based on Machine Learning

The rapid growth of Internet content,applications and services require more computing and storage capacity and higher bandwidth.Traditionally,internet services are provided from the cloud(i.e.,from far away)and consumed on increasingly smart devices.Edge computing and caching provides these services from nearby smart devices.Blending both approaches should combine the power of cloud services and the responsiveness of edge networks.This paper investigates how to intelligently use the caching and computing capabilities of edge nodes/cloudlets through the use of artificial intelligence-based policies.We first analyze the scenarios of mobile edge networks with edge computing and caching abilities,then design a paradigm of virtualized edge network which includes an efficient way of isolating traffic flow in physical network layer.We develop the caching and communicating resource virtualization in virtual layer,and formulate the dynamic resource allocation problem into a reinforcement learning model,with the proposed self-adaptive and self-learning management,more flexible,better performance and more secure network services with lower cost will be obtained.Simulation results and analyzes show that addressing cached contents in proper edge nodes through a trained model is more efficient than requiring them from the cloud.

An Efficient Image Cipher Based on 2D Scrambled Image and Random Numbers

Security of images plays an import role in communication in current era due to the popularity and high usage ofmultimedia content in the Internet.Image security is described as applying an encryption algorithm over the given plaintext images to produce cipher images that can be transmitted safely over the open channel,the Internet.The problem which plagues these image ciphers is that they are too much time consuming,and that do not meet the dictates of the present times.In this paper,we aim to provide an efficient image cipher.The previous studies employed many constructs like Langton’s Ant,15 puzzle game and Castle in the 2D scrambled image based image ciphers,which had grave implications related to the high execution time of the ciphers.The current study directly made use of the 2D scrambled image to realize the purpose.Moreover,no compromise has been made over the security of the proposed image cipher.Random numbers have been generated by triggering the Intertwining Logistic Chaotic map.The cipher has been subjected to many important validation metrics like key space,information entropy,correlation coefficient,crop attack and lastly time complexity to demonstrate its immunity to the various attacks,and its realworld application.In this paper,our proposed image cipher exhibits an encryption speed of 0.1797 s,which is far better than many of the existing encryption ciphers.

Research on Protecting Information Security Based on the Method of Hierarchical Classification in the Era of Big Data

Big data is becoming increasingly important because of the enormous information generation and storage in recent years.It has become a challenge to the data mining technique and management.Based on the characteristics of geometric explosion of information in the era of big data,this paper studies the possible approaches to balance the maximum value and privacy of information,and disposes the Nine-Cells information matrix,hierarchical classification.Furthermore,the paper uses the rough sets theory to proceed from the two dimensions of value and privacy,establishes information classification method,puts forward the countermeasures for information security.Taking spam messages for example,the massive spam messages can be classified,and then targeted hierarchical management strategy was put forward.This paper proposes personal Information index system,Information management platform and possible solutions to protect information security and utilize information value in the age of big data.

A Multi-Agent System for Environmental Monitoring Using Boolean Networks and Reinforcement Learning

Distributed wireless sensor networks have been shown to be effective for environmental monitoring tasks,in which multiple sensors are deployed in a wide range of the environments to collect information or monitor a particular event,Wireless sensor networks,consisting of a large number of interacting sensors,have been successful in a variety of applications where they are able to share information using different transmission protocols through the communication network.However,the irregular and dynamic environment requires traditional wireless sensor networks to have frequent communications to exchange the most recent information,which can easily generate high communication cost through the collaborative data collection and data transmission.High frequency communication also has high probability of failure because of long distance data transmission.In this paper,we developed a novel approach to multi-sensor environment monitoring network using the idea of distributed system.Its communication network can overcome the difficulties of high communication cost and Single Point of Failure(SPOF)through the decentralized approach,which performs in-network computation.Our approach makes use of Boolean networks that allows for a non-complex method of corroboration and retains meaningful information regarding the dynamics of the communication network.Our approach also reduces the complexity of data aggregation process and employee a reinforcement learning algorithm to predict future event inside the environment through the pattern recognition.

A Certificateless Homomorphic Encryption Scheme for Protecting Transaction Data Privacy of Post-Quantum Blockchain

Blockchain has a profound impact on all areas of society by virtue of its immutability,decentralization and other characteristics.However,blockchain faces the problem of data privacy leakage during the application process,and the rapid development of quantum computing also brings the threat of quantum attack to blockchain.In this paper,we propose a lattice-based certificateless fully homomorphic encryption(LCFHE)algorithm based on approximate eigenvector firstly.And we use the lattice-based delegate algorithm and preimage sampling algorithm to extract part of the private key based on certificateless scheme,which is composed of the private key together with the secret value selected by the user,thus effectively avoiding the problems of certificate management and key escrow.Secondly,we propose a post-quantum blockchain transaction privacy protection scheme based on LCFHE algorithm,which uses the ciphertext calculation characteristic of homomorphic encryption to encrypt the account balance and transaction amount,effectively protecting the transaction privacy of users and having the ability to resist quantum attacks.Finally,we analyze the correctness and security of LCFHE algorithm,and the security of the algorithm reduces to the hardness of learning with errors(LWE)hypothesis.

Awareness about the Online Security Threat and Ways to Secure the Youths

This study aimed to find out the awareness about the online security threat and understanding of the preventive measures to secure the youths from online risks.For this,a quantitative method was applied and the survey questionnaire was instituted to collect the data randomly from the youths studying in class eleven and higher.A total of 264 youths,147 female and 117 male responded to the survey questionnaire.The data was organized and analyzed using Excel data analysis tool package,interpreted and represented in the form of graphs with some explanations.The awareness about the online security threat was found to be good with 20 percent completely aware and 58 percent aware of it.The knowledge about the two-factor authentication was found to be quite poor.There are 20.4 percent who knows a little and 14.7 percent who do not know anything and 29.5 percent who are not sure about it.The use of cloud encryption,security and protection with firewalls is not so familiar amongst the youth.However,the use of strong passwords for their mobile and other gadgets were mostly applied by the youths.The youths were also found to be concerned with the software and the system in their mobile and other gadgets as they responded that they update it frequently.

A Survey on Security Threats and Solutions of Bitcoin

Bitcoin is known as the first decentralized digital currency around the world.It uses blockchain technology to store transaction data in a distributed public ledger,is a distributed ledger that removes third-party trust institutions.Since its invention,bitcoin has achieved great success,has a market value of about$200 billion.However,while bitcoin has brought a wide and far-reaching impact in the financial field,it has also exposed some security problems,such as selfish mining attacks,Sybil attack,eclipse attacks,routing attacks,EREBUS attacks,and so on.This paper gives a comprehensive overview of various attack scenarios that the bitcoin network may be subjected to,and the methods used to implement the attacks,and reviews the solutions and countermeasures proposed against these attacks.Finally,we summarized other security challenges and proposed further optimizations for the security of the bitcoin network.

Credit Card Fraud Detection on Original European Credit Card Holder Dataset Using Ensemble Machine Learning Technique

The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising frompotentially fraudulent activities.However,a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations.While sampling techniques can significantly reduce computational time,the resulting dataset relies on generated data and the accuracy of the pre-processing machine learning models employed.Such datasets often lack true representativeness of realworld data,potentially introducing secondary issues that affect the precision of the results.For instance,undersampling may result in the loss of critical information,while over-sampling can lead to overfitting machine learning models.In this paper,we proposed a classification study of credit card fraud using fundamental machine learning models without the application of any sampling techniques on all the features present in the original dataset.The results indicate that Support Vector Machine(SVM)consistently achieves classification performance exceeding 90%across various evaluation metrics.This discovery serves as a valuable reference for future research,encouraging comparative studies on original dataset without the reliance on sampling techniques.Furthermore,we explore hybrid machine learning techniques,such as ensemble learning constructed based on SVM,K-Nearest Neighbor(KNN)and decision tree,highlighting their potential advancements in the field.The study demonstrates that the proposed machine learning models yield promising results,suggesting that pre-processing the dataset with sampling algorithm or additional machine learning technique may not always be necessary.This research contributes to the field of credit card fraud detection by emphasizing the potential of employing machine learning models directly on original datasets,thereby simplifying the workflow and potentially improving the accuracy and efficiency of fraud detection systems.

An Amorphous 2-Dimensional Barcode

Most existing 2-dimensional barcodes are designed with a fixed shape and clear area.Having a fixed shape and clear area makes the barcode difficult to lay out with other text and pictures.To solve this problem,an amorphous 2-dimensional barcode is presented in this paper.The barcode uses encoding graph units to encode data.There are two key points in a 2-dimensional barcode:One is the encoding graph unit,the other is its encoding rules.Because encoding graph units of a 2-dimensional barcode are surrounded by other graphics,the units should be self-positioned and distinguished from other units.This paper presents an encoding graph unit generation algorithm,which is based on genetic algorithms.Encoding rules of the barcode are also given in this paper.Those rules include data encoding rules and encoding graph unit sequence arrangement rules.Data encoding rules are used to encode data to an encoding graph unit sequence.Encoding graph unit sequence arrangement rules are used to embed the unit sequence in the target picture.In addition to those rules,it also discussed the steps to restore encoding graph unit sequence from a picture.In the experiments section of this paper,an example is provided to encode a string and embed it in a car ad picture by the barcode.According to encoding rules of the barcode,those encoding graphic units can be scattered and embedded in a picture with other graphics,so amorphous 2-dimensional barcode has no fixed shape.Take advantage of this,designer can present a more elegant design to lay out barcodes with other graphic elements.

Multi-UAV Cooperative GPS Spoofing Based on YOLO Nano

In recent years,with the rapid development of the drone industry,drones have been widely used in many fields such as aerial photography,plant protection,performance,and monitoring.To effectively control the unauthorized flight of drones,using GPS spoofing attacks to interfere with the flight of drones is a relatively simple and highly feasible attack method.However,the current method uses ground equipment to carry out spoofing attacks.The attack range is limited and the flexibility is not high.Based on the existing methods,this paper proposes a multi-UAV coordinated GPS spoofing scheme based on YOLO Nano,which can launch effective attacks against target drones with autonomous movement:First,a single-attack drone based on YOLO Nano is proposed.The target tracking scheme achieves accurate tracking of the target direction on a single-attack drone;then,based on the single-UAV target tracking,a multi-attack drone coordinated target tracking scheme based on the weighted least squares method is proposed to realize the target drone Finally,a new calculation method for false GPS signals is proposed,which adaptively adjusts the flight trajectory of the attacking drone and the content of the false GPS signal according to the autonomous movement of the target drone.

Review of Access Control Model

Access control is one of the core problems in data management system.In this paper,the system requirements were described in three aspects:the traditional access control model,the access control model in the Internet era and the access control model in the cloud computing environment.Meanwhile,the corresponding major models were listed and their characteristics and problems were analyzed.Finally,the development trend of the corresponding model was proposed.

Privacy-Preserving Genetic Algorithm Outsourcing in Cloud Computing

Genetic Algorithm(GA)has been widely used to solve various optimization problems.As the solving process of GA requires large storage and computing resources,it is well motivated to outsource the solving process of GA to the cloud server.However,the algorithm user would never want his data to be disclosed to cloud server.Thus,it is necessary for the user to encrypt the data before transmitting them to the server.But the user will encounter a new problem.The arithmetic operations we are familiar with cannot work directly in the ciphertext domain.In this paper,a privacy-preserving outsourced genetic algorithm is proposed.The user’s data are protected by homomorphic encryption algorithm which can support the operations in the encrypted domain.GA is elaborately adapted to search the optimal result over the encrypted data.The security analysis and experiment results demonstrate the effectiveness of the proposed scheme.

A New Adaptive Regularization Parameter Selection Based on Expected Patch Log Likelihood

Digital images have been applied to various areas such as evidence in courts.However,it always suffers from noise by criminals.This type of computer network security has become a hot issue that can’t be ignored.In this paper,we focus on noise removal so as to provide guarantees for computer network security.Firstly,we introduce a well-known denoising method called Expected Patch Log Likelihood(EPLL)with Gaussian Mixture Model as its prior.This method achieves exciting results in noise removal.However,there remain problems to be solved such as preserving the edge and meaningful details in image denoising,cause it considers a constant as regularization parameter so that we denoise with the same strength on the whole image.This leads to a problem that edges and meaningful details may be oversmoothed.Under the consideration of preserving edges of the image,we introduce a new adaptive parameter selection based on EPLL by the use of the image gradient and variance,which varies with different regions of the image.Moreover,we add a gradient fidelity term to relieve staircase effect and preserve more details.The experiment shows that our proposed method proves the effectiveness not only in vision but also on quantitative evaluation.

Extracting Campus’Road Network from Walking GPS Trajectories

Road network extraction is vital to both vehicle navigation and road planning.Existing approaches focus on mining urban trunk roads from GPS trajectories of floating cars.However,path extraction,which plays an important role in earthquake relief and village tour,is always ignored.Addressing this issue,we propose a novel approach of extracting campus’road network from walking GPS trajectories.It consists of data preprocessing and road centerline generation.The patrolling GPS trajectories,collected at Hunan University of Science and Technology,were used as the experimental data.The experimental evaluation results show that our approach is able to effectively and accurately extract both campus’trunk roads and paths.The coverage rate is 96.21%while the error rate is 3.26%.

Analysis of Security Aspects in LoRaWAN

Nowadays,emerging trends in the field of technology related to big data,cognitive computing,and the Internet of Things(IoT)have become closely related to people’s lives.One of the hottest areas these days is transforming traditional cities into smart cities,using the concept of IoT depending on several types of modern technologies to develop and manage cities in order to improve and facilitate the quality of life.The Internet of Things networks consist of a huge number of interconnected devices and sensors that process and transmit data.Such Activities require efficient energy to be performed at the highest quality and range,hence the concept of Long-Range Wide Area Network(LoRaWAN)introduced,which concerns about delivering lower energy consumption,supporting large networks and mobility.In this paper,the security mechanisms in LoRaWAN will be evaluated by literature review from many authors.The expected outcomes are to study and evaluate the LoRaWAN mechanism and class and protocol stacks.

Image Feature Computation in Encrypted Domain Based on Mean Value

In smart environments,more and more teaching data sources are uploaded to remote cloud centers which promote the development of the smart campus.The outsourcing of massive teaching data can reduce storage burden and computational cost,but causes some privacy concerns because those teaching data(especially personal image data)may contain personal private information.In this paper,a privacy-preserving image feature extraction algorithm is proposed by using mean value features.Clients use block scrambling and chaotic map to encrypt original images before uploading to the remote servers.Cloud servers can directly extract image mean value features from encrypted images.Experiments show the effectiveness and security of our algorithm.It can achieve information search over the encrypted images on the smart campus.

CenterPicker:An Automated Cryo-EM Single-Particle Picking Method Based on Center Point Detection

Cryo-electron microscopy(cryo-EM)has become one of the mainstream techniques for determining the structures of proteins andmacromolecular complexes,with prospects for development and significance.Researchers must select hundreds of thousands of particles from micrographs to acquire the database for single-particle cryo-EM reconstruction.However,existing particle picking methods cannot ensure that the particles are in the center of the bounding box because the signal-to-noise ratio(SNR)of micrographs is extremely low,thereby directly affecting the efficiency and accuracy of 3D reconstruction.We propose an automated particle-picking method(CenterPicker)based on particle center point detection to automatically select a large number of high-quality particles from low signal-to-noise,low-contrast refrigerated microscopy images.The method uses a fully convolutional neural network to generate a keypoint heatmap.The heatmap value represents the probability that a micrograph pixel belongs to a particle center area.CenterPicker can process images of any size and can directly predict the center point and size of the particle.The network implements multiscale feature fusion and introduces an attention mechanism to improve the feature fusion part to obtain more accurate selection results.We have conducted a detailed evaluation of CenterPicker on a range of datasets,and results indicate that it excels in single-particle picking tasks.

A Hybrid Algorithm Based on PSO and GA for Feature Selection

One of the main problems of machine learning and data mining is to develop a basic model with a few features,to reduce the algorithms involved in classification’s computational complexity.In this paper,the collection of features has an essential importance in the classification process to be able minimize computational time,which decreases data size and increases the precision and effectiveness of specific machine learning activities.Due to its superiority to conventional optimization methods,several metaheuristics have been used to resolve FS issues.This is why hybrid metaheuristics help increase the search and convergence rate of the critical algorithms.A modern hybrid selection algorithm combining the two algorithms;the genetic algorithm(GA)and the Particle Swarm Optimization(PSO)to enhance search capabilities is developed in this paper.The efficacy of our proposed method is illustrated in a series of simulation phases,using the UCI learning array as a benchmark dataset.

A Survey on Visualization-Based Malware Detection

In computer security,the number of malware threats is increasing and causing damage to systems for individuals or organizations,necessitating a new detection technique capable of detecting a new variant of malware more efficiently than traditional anti-malware methods.Traditional antimalware software cannot detect new malware variants,and conventional techniques such as static analysis,dynamic analysis,and hybrid analysis are time-consuming and rely on domain experts.Visualization-based malware detection has recently gained popularity due to its accuracy,independence from domain experts,and faster detection time.Visualization-based malware detection uses the image representation of the malware binary and applies image processing techniques to the image.This paper aims to provide readers with a comprehensive understanding of malware detection and focuses on visualization-based malware detection.