Systematic Security Guideline Framework through Intelligently Automated Vulnerability Analysis

This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world software.The existing analysis of software security vulnerabilities often focuses on specific features or modules.This partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the software.The key novelty lies in overcoming the constraints of partial approaches.The proposed framework utilizes data from various sources to create a comprehensive functionality profile,facilitating the derivation of real-world security guidelines.Security guidelines are dynamically generated by associating functional security vulnerabilities with the latest Common Vulnerabilities and Exposure(CVE)and Common Vulnerability Scoring System(CVSS)scores,resulting in automated guidelines tailored to each product.These guidelines are not only practical but also applicable in real-world software,allowing for prioritized security responses.The proposed framework is applied to virtual private network(VPN)software,wherein a validated Level 2 data flow diagram is generated using the Spoofing,Tampering,Repudiation,Information Disclosure,Denial of Service,and Elevation of privilege(STRIDE)technique with references to various papers and examples from related software.The analysis resulted in the identification of a total of 121 vulnerabilities.The successful implementation and validation demonstrate the framework’s efficacy in generating customized guidelines for entire systems,subsystems,and selected modules.

A New Method of Image Restoration Technology Based on WGAN

With the development of image restoration technology based on deep learning,more complex problems are being solved,especially in image semantic inpainting based on context.Nowadays,image semantic inpainting techniques are becoming more mature.However,due to the limitations of memory,the instability of training,and the lack of sample diversity,the results of image restoration are still encountering difficult problems,such as repairing the content of glitches which cannot be well integrated with the original image.Therefore,we propose an image inpainting network based on Wasserstein generative adversarial network(WGAN)distance.With the corresponding technology having been adjusted and improved,we attempted to use the Adam algorithm to replace the traditional stochastic gradient descent,and another algorithm to optimize the training used in recent years.We evaluated our algorithm on the ImageNet dataset.We obtained high-quality restoration results,indicating that our algorithm improves the clarity and consistency of the image.

Intelligent SLAM Algorithm Fusing Low-Cost Sensors at Risk of Building Collapses

When firefighters search inside a building that is at risk of collapse due to abandonment or disasters such as fire,they use old architectural drawings or a simple monitoring method involving a video device attached to a robot.However,using these methods,the disaster situation inside a building at risk of collapse is difficult to detect and identify.Therefore,we investigate the generation of digital maps for a disaster site to accurately analyze internal situations.In this study,a robot combined with a low-cost camera and twodimensional light detection and ranging(2D-lidar)traverses across a floor to estimate the location of obstacles while drawing an internal map of the building.We propose an algorithm that detects the floor and then determines the possibility of entry,tracks collapses,and detects obstacles by analyzing patterns on the floor.The robot’s location is estimated,and a digital map is created based on Hector simultaneous localization and mapping(SLAM).Subsequently,the positions of obstacles are estimated based on the range values detected by 2D-lidar,and the position of the obstacles are identified on the map using the map update method in semantic SLAM.All equipment are implemented using low-specification devices,and the experiments are conducted using a low-cost robot that affords near-real-time performance.The experiments are conducted in various actual internal environments of buildings.In terms of obstacle detection performance,almost all obstacles are detected,and their positions identified on the map with a high accuracy of 89%.

Intelligent Risk-Identification Algorithm with Vision and 3D LiDAR Patterns at Damaged Buildings

Existingfirefighting robots are focused on simple storage orfire sup-pression outside buildings rather than detection or recognition.Utilizing a large number of robots using expensive equipment is challenging.This study aims to increase the efficiency of search and rescue operations and the safety offirefigh-ters by detecting and identifying the disaster site by recognizing collapsed areas,obstacles,and rescuers on-site.A fusion algorithm combining a camera and three-dimension light detection and ranging(3D LiDAR)is proposed to detect and loca-lize the interiors of disaster sites.The algorithm detects obstacles by analyzingfloor segmentation and edge patterns using a mask regional convolutional neural network(mask R-CNN)features model based on the visual data collected from a parallelly connected camera and 3D LiDAR.People as objects are detected using you only look once version 4(YOLOv4)in the image data to localize persons requiring rescue.The point cloud data based on 3D LiDAR cluster the objects using the density-based spatial clustering of applications with noise(DBSCAN)clustering algorithm and estimate the distance to the actual object using the center point of the clustering result.The proposed artificial intelligence(AI)algorithm was verified based on individual sensors using a sensor-mounted robot in an actual building to detectfloor surfaces,atypical obstacles,and persons requiring rescue.Accordingly,the fused AI algorithm was comparatively verified.

Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of lesions.Thus,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images.The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases.In addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion boundaries.This study uses two databases in training and testing the proposed segmentation approach.The breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in Iraq.Results of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound images.The segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data set.For the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.

Image Hiding Algorithm in Discrete Cosine Transform Domain Based on Grey Prediction and Grey Relational Analysis

Traditional information hiding algorithms cannot maintain a good balance of capacity,invisibility and robustness.In this paper,a novel blind colour image information hiding algorithm based on grey prediction and grey relational analysis in the Discrete Cosine Transform(DCT) domain is proposed.First,this algorithm compresses the secret image losslessly based on the improved grey prediction GM(1,1)(IGM) model.It then chooses the blocks of rich texture in the cover image as the embedding regions using Double-dimension Grey Relational Analysis(DGRA).Finally,it adaptively embeds the compressed secret bits stream into the DCT domain mid-frequency coefficients,which are decided by those blocks' Double-Dimension Grey Correlation Degree(DGCD) and Human Visual System(HVS).This method can ensure an adequate balance between invisibility,capacity and robustness.Experimental results show that the proposed algorithm is robust against JPEG compression(46.724 6 dB when the compression quality factor is 90%),Gaussian noise(45.531 3 dB when the parameter is(0,0.000 5)) etc.,and it is a blind information hiding algorithm that can be extracted without an original carrier.

A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment.Classical methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related symptoms.As an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD conditions.However,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with HD.In this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)model.The MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD identication.The CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD features.We evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation criteria.The AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on average.Therefore,the AHDD system proposed herein has the highest capability to identify patients with HD.This system can be used by medical practitioners to diagnose HD efciently。

BEVGGC:Biogeography-Based Optimization Expert-VGG for Diagnosis COVID-19 via Chest X-ray Images

Purpose:As to January 11,2021,coronavirus disease(COVID-19)has caused more than 2 million deaths worldwide.Mainly diagnostic methods of COVID-19 are:(i)nucleic acid testing.This method requires high requirements on the sample testing environment.When collecting samples,staff are in a susceptible environment,which increases the risk of infection.(ii)chest computed tomography.The cost of it is high and some radiation in the scan process.(iii)chest X-ray images.It has the advantages of fast imaging,higher spatial recognition than chest computed tomography.Therefore,our team chose the chest X-ray images as the experimental dataset in this paper.Methods:We proposed a novel framework—BEVGG and three methods(BEVGGC-I,BEVGGC-II,and BEVGGC-III)to diagnose COVID-19 via chest X-ray images.Besides,we used biogeography-based optimization to optimize the values of hyperparameters of the convolutional neural network.Results:The experimental results show that the OA of our proposed three methods are 97.65%±0.65%,94.49%±0.22%and 94.81%±0.52%.BEVGGC-I has the best performance of all methods.Conclusions:The OA of BEVGGC-I is 9.59%±1.04%higher than that of state-of-the-art methods.

Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

Wireless Sensor Network(WSN)forms an essential part of IoT.It is embedded in the target environment to observe the physical parameters based on the type of application.Sensor nodes inWSN are constrained by different features such as memory,bandwidth,energy,and its processing capabilities.In WSN,data transmission process consumes the maximum amount of energy than sensing and processing of the sensors.So,diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN.In this view,the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation(T2FLCH-LCDA)technique for WSN.The presented model involves a two-stage process such as clustering and data aggregation.Initially,three input parameters such as residual energy,distance to Base Station(BS),and node centrality are used in T2FLCH technique for CH selection and cluster construction.Besides,the LCDA technique which follows Dictionary Based Encoding(DBE)process is used to perform the data aggregation at CHs.Finally,the aggregated data is transmitted to the BS where it achieves energy efficiency.The experimental validation of the T2FLCH-LCDAtechnique was executed under three different scenarios based on the position of BS.The experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency,lifetime,Compression Ratio(CR),and power saving than the compared methods.

Ontology-based Knowledge Extraction from Hidden Web

Hidden Web provides great amount of domain-specific data for constructing knowledge services. Most previous knowledge extraction researches ignore the valuable data hidden in Web database, and related works do not refer how to make extracted information available for knowledge system. This paper describes a novel approach to build a domain-specific knowledge service with the data retrieved from Hidden Web. Ontology serves to model the domain knowledge. Queries forms of different Web sites are translated into machine-understandable format, defined knowledge concepts, so that they can be accessed automatically. Also knowledge data are extracted from Web pages and organized in ontology format knowledge. The experiment proves the algorithm achieves high accuracy and the system facilitates constructing knowledge services greatly.

Survey on Research of RNN-Based Spatio-Temporal Sequence Prediction Algorithms

In the past few years,deep learning has developed rapidly,and many researchers try to combine their subjects with deep learning.The algorithm based on Recurrent Neural Network(RNN)has been successfully applied in the fields of weather forecasting,stock forecasting,action recognition,etc.because of its excellent performance in processing Spatio-temporal sequence data.Among them,algorithms based on LSTM and GRU have developed most rapidly because of their good design.This paper reviews the RNN-based Spatio-temporal sequence prediction algorithm,introduces the development history of RNN and the common application directions of the Spatio-temporal sequence prediction,and includes precipitation nowcasting algorithms and traffic flow forecasting algorithms.At the same time,it also compares the advantages and disadvantages,and innovations of each algorithm.The purpose of this article is to give readers a clear understanding of solutions to such problems.Finally,it prospects the future development of RNN in the Spatio-temporal sequence prediction algorithm.

Ensemble feature selection integrating elitist roles and quantum game model

To accelerate the selection process of feature subsets in the rough set theory （RST）, an ensemble elitist roles based quantum game （EERQG） algorithm is proposed for feature selec- tion. Firstly, the multilevel elitist roles based dynamics equilibrium strategy is established, and both immigration and emigration of elitists are able to be self-adaptive to balance between exploration and exploitation for feature selection. Secondly, the utility matrix of trust margins is introduced to the model of multilevel elitist roles to enhance various elitist roles＇ performance of searching the optimal feature subsets, and the win-win utility solutions for feature selec- tion can be attained. Meanwhile, a novel ensemble quantum game strategy is designed as an intriguing exhibiting structure to perfect the dynamics equilibrium of multilevel elitist roles. Finally, the en- semble manner of multilevel elitist roles is employed to achieve the global minimal feature subset, which will greatly improve the fea- sibility and effectiveness. Experiment results show the proposed EERQG algorithm has superiority compared to the existing feature selection algorithms.

Genetic algorithm for pareto optimum-based route selection

A quality of service （QoS） or constraint-based routing selection needs to find a path subject to multiple constraints through a network. The problem of finding such a path is known as the multi-constrained path （MCP） problem, and has been proven to be NP-complete that cannot be exactly solved in a polynomial time. The NPC problem is converted into a multiobjective optimization problem with constraints to be solved with a genetic algorithm. Based on the Pareto optimum, a constrained routing computation method is proposed to generate a set of nondominated optimal routes with the genetic algorithm mechanism. The convergence and time complexity of the novel algorithm is analyzed. Experimental results show that multiobjective evolution is highly responsive and competent for the Pareto optimum-based route selection. When this method is applied to a MPLS and metropolitan-area network, it will be capable of optimizing the transmission performance.

LBC-IoT: Lightweight Block Cipher for IoT Constraint Devices

With the new era of the Internet of Things(IoT)technology,many devices with limited resources are utilized.Those devices are susceptible to a signicant number of new malware and other risks emerging rapidly.One of the most appropriate methods for securing those IoT applications is cryptographic algorithms,as cryptography masks information by eliminating the risk of collecting any meaningful information patterns.This ensures that all data communications are private,accurate,authenticated,authorized,or nonrepudiated.Since conventional cryptographic algorithms have been developed specically for devices with limited resources;however,it turns out that such algorithms are not ideal for IoT restricted devices with their current conguration.Therefore,lightweight block ciphers are gaining popularity to meet the requirements of low-power and constrained devices.A new ultra-lightweight secret-key block-enciphering algorithm named“LBC-IoT”is proposed in this paper.The proposed block length is 32-bit supporting key lengths of 80-bit,and it is mainly based on the Feistel structure.Energy-efcient cryptographic features in“LBC-IoT”include the use of simple functions(shift,XOR)and small rigid substitution boxes(4-bit-S-boxes).Besides,it is immune to different types of attacks such as linear,differential,and side-channel as well as exible in terms of implementation.Moreover,LBC-IoT achieves reasonable performance in both hardware and software compared to other recent algorithms.LBC-IoT’s hardware implementation results are very promising(smallest ever area“548”GE)and competitive with today’s leading lightweight ciphers.LBC-IoT is also ideally suited for ultra-restricted devices such as RFID tags.

Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment

Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern era.IoT generally refers to a network of gadgets linked via wireless network and communicates via internet.Resource management,especially energy management,is a critical issue when designing IoT devices.Several studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT environment.In this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT environment.The proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance phase.The proposed EECBRMmodel was validated through a series of experiments and the results were verified under several aspects.EECBRM model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network lifetime.When simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant manner.Thus,the efficiency of the proposed model is established.

Survey on the Application of Deep Reinforcement Learning in Image Processing

feature representations from a large amount of data,and use reinforcement learning to learn the best strategy to complete the task.Through the combination of deep learning and reinforcement learning,end-to-end input and output can be achieved,and substantial breakthroughs have been made in many planning and decision-making systems with infinite states,such as games,in particular,AlphaGo,robotics,natural language processing,dialogue systems,machine translation,and computer vision.In this paper we have summarized the main techniques of deep reinforcement learning and its applications in image processing.

Vision-based Recognition Algorithm for Up-To-Date Indoor Digital Map Generations at Damaged Buildings

When firefighters are engaged in search and rescue missions inside a building at a risk of collapse,they have difficulty in field command and rescue because they can only simplymonitor the situation inside the building utilizing old building drawings or robots.To propose an efficient solution for fast search and rescue work of firefighters,this study investigates the generation of up-to-date digital maps for disaster sites by tracking the collapse situation,and identifying the information of obstacles which are risk factors,using an artificial intelligence algorithm based on low-cost robots.Our research separates the floor by using the mask regional convolutional neural network(R-CNN)algorithm,and determines whether the passage is collapsed or not.Then,in the case of a passage that can be searched,the floor pattern of the obstacles that exist on the floor that has not collapsed is analyzed,and obstacles are searched utilizing an image processing algorithm.Here,we can detect various unknown as well as known obstacles.Furthermore,the locations of obstacles can be estimated using the pixel values up to the bounding box of an existing detected obstacle.We conduct experiments using the public datasets collected by Carnegie Mellon university(CMU)and data collected by manipulating a low-cost robot equipped with a smartphone while roaming five buildings in a campus.The collected data have various floor patterns for objectivity and obstacles that are different from one another.Based on these data,the algorithm for detecting unknown obstacles of a verified study and estimating their sizes had an accuracy of 93%,and the algorithm for estimating the distance to obstacles had an error rate of 0.133.Through this process,we tracked collapsed passages and composed up-to-date digital maps for disaster sites that include the information of obstacles that interfere with the search and rescue work.

A New Decision-Making Model Based on Plithogenic Set for Supplier Selection

Supplier selection is a common and relevant phase to initialize the supply chain processes and ensure its sustainability.The choice of supplier is a multicriteria decision making(MCDM)to obtain the optimal decision based on a group of criteria.The health care sector faces several types of problems,and one of the most important is selecting an appropriate supplier that fits the desired performance level.The development of service/product quality in health care facilities in a country will improve the quality of the life of its population.This paper proposes an integrated multi-attribute border approximation area comparison(MABAC)based on the best-worst method(BWM),plithogenic set,and rough numbers.BWM is applied to regulate the weight vector of the measures in group decision-making problems with a high level of consistency.For the treatment of uncertainty,a plithogenic set and rough number(RN)are used to improve the accuracy of results.Plithogenic set operations are used to deal with information in the desired manner that handles uncertainty and vagueness.Then,based on the plithogenic aggregation and the results of BWM evaluation,we use MABAC to find the optimal alternative according to defined criteria.To examine the proposed integrated algorithm,an empirical example is produced to select an optimal supplier within five options in the healthcare industry.

A Fast Calculation of Metric Scores for Learning Bayesian Network

Frequent counting is a very so often required operation in machine learning algorithms. A typical machine learning task, learning the structure of Bayesian network （BN） based on metric scoring, is introduced as an example that heavily relies on frequent counting. A fast calculation method for frequent counting enhanced with two cache layers is then presented for learning BN. The main contribution of our approach is to eliminate comparison operations for frequent counting by introducing a multi-radix number system calculation. Both mathematical analysis and empirical comparison between our method and state-of-the-art solution are conducted. The results show that our method is dominantly superior to state-of-the-art solution in solving the problem of learning BN.

Color reproduction for noisy CFA data using directional cycle-spinning

This paper addresses color filter array（CFA） color reproduction problem where the aim is to utilize an image captured by the CFA to produce an image with full color information.First,conventional subband synthesis based color reproduction techniques do not consider the noise during image acquisition and assume that the CFA data are noiseless.To tackle the noisy CFA data,a novel approach is proposed by inserting a subband denoising scheme into the conventional subband synthesis framework.Second,conventional subband synthesis based techniques exploit the decimated wavelet transform that is not shift-invariant and could result in ringing artifacts in the result.To alleviate these artifacts,the directional cycle-spinning（DCS） technique is exploited.Furthermore,a new cycle-spinning pattern is proposed according to the sampling pattern of the Bayer CFA data.Extensive experiments are conducted to demonstrate that the proposed approach outperforms several approaches.