Anomaly Classification Using Genetic Algorithm-Based Random Forest Modelfor Network Attack Detection

Anomaly classification based on network traffic features is an important task to monitor and detect network intrusion attacks.Network-based intrusion detection systems(NIDSs)using machine learning(ML)methods are effective tools for protecting network infrastructures and services from unpredictable and unseen attacks.Among several ML methods,random forest(RF)is a robust method that can be used in ML-based network intrusion detection solutions.However,the minimum number of instances for each split and the number of trees in the forest are two key parameters of RF that can affect classification accuracy.Therefore,optimal parameter selection is a real problem in RF-based anomaly classification of intrusion detection systems.In this paper,we propose to use the genetic algorithm(GA)for selecting the appropriate values of these two parameters,optimizing the RF classifier and improving the classification accuracy of normal and abnormal network traffics.To validate the proposed GA-based RF model,a number of experiments is conducted on two public datasets and evaluated using a set of performance evaluation measures.In these experiments,the accuracy result is compared with the accuracies of baseline ML classifiers in the recent works.Experimental results reveal that the proposed model can avert the uncertainty in selection the values of RF’s parameters,improving the accuracy of anomaly classification in NIDSs without incurring excessive time.

Exploring the Core-shell Structure of BaTiO3-based Dielectric Ceramics Using Machine Learning Models and Interpretability Analysis

A machine learning(ML)-based random forest(RF)classification model algorithm was employed to investigate the main factors affecting the formation of the core-shell structure of BaTiO_(3)-based ceramics and their interpretability was analyzed by using Shapley additive explanations(SHAP).An F1-score changed from 0.8795 to 0.9310,accuracy from 0.8450 to 0.9070,precision from 0.8714 to 0.9000,recall from 0.8929 to 0.9643,and ROC/AUC value of 0.97±0.03 was achieved by the RF classification with the optimal set of features containing only 5 features,demonstrating the high accuracy of our model and its high robustness.During the interpretability analysis of the model,it was found that the electronegativity,melting point,and sintering temperature of the dopant contribute highly to the formation of the core-shell structure,and based on these characteristics,specific ranges were delineated and twelve elements were finally obtained that met all the requirements,namely Si,Sc,Mn,Fe,Co,Ni,Pd,Er,Tm,Lu,Pa,and Cm.In the process of exploring the structure of the core-shell,the doping elements can be effectively localized to be selected by choosing the range of features.

Evaluating the addition of radar with optical data for vegetation mapping in a montane region in Sri Lanka

The use of freely-available multi-source imagery for mapping vegetation in montane terrain is important for many developing countries that do not have the funding for high-resolution data capture.Radar images are also now freely available and include Sentinel-1 in dual polarisation,and PALSAR-2.These images can penetrate cloud cover and provide the advantage of acquiring data in a cloudy tropical region.This research evaluated whether the addition of radar with optical and topographic data improves classification accuracy in a montane region in Sri Lanka.Six classification experiments were designed based on different combinations of image data to test whether radar data improved land cover classification accuracy compared with optical data alone.Random forest classifier in the Google Earth Engine has been utilised to classify the tropical montane vegetation.The results indicate that radar or optical data alone cannot obtain satisfactory results.However,when combining radar with optical data the overall accuracy increased by approximately 5%,and by an additional 2%when topography data were added.The highest accuracy(92%)was achieved with multiple imagery,and adding the vegetation indices improved the model slightly by 0.3%.In addition,feature importance analysis showed that radar data makes a significant contribution to the classification.These positive outcomes demonstrate that freely-accessible multi-source remotely-sensed data have impressive capability for vegetation mapping,and support the monitoring and managing of forest ecological resources in tropical montane regions.

Social Engineering Attack Classifications on Social Media Using Deep Learning

In defense-in-depth,humans have always been the weakest link in cybersecurity.However,unlike common threats,social engineering poses vulnerabilities not directly quantifiable in penetration testing.Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware.Social Engineering(SE)in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic.In this paper,a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory(RNN-LSTM)to identify well-disguised SE threats in social media posts.We use a custom dataset crawled from hundreds of corporate and personal Facebook posts.First,the social engineering attack detection pipeline(SEAD)is designed to filter out social posts with malicious intents using domain heuristics.Next,each social media post is tokenized into sentences and then analyzed with a sentiment analyzer before being labelled as an anomaly or normal training data.Then,we train an RNN-LSTM model to detect five types of social engineering attacks that potentially contain signs of information gathering.The experimental result showed that the Social Engineering Attack(SEA)model achieves 0.84 in classification precision and 0.81 in recall compared to the ground truth labeled by network experts.The experimental results showed that the semantics and linguistics similarities are an effective indicator for early detection of SEA.

Assessment of Past and Future Land Use/Land Cover Dynamics of the Old Kumasi Metropolitan Assembly and Atwima Nwabiagya Municipal Area, Ghana

Ghana like all countries in Sub-Saharan region of Africa have long been undergoing intense land use land cover changes (LULCC) which have given rise to extensive forest loss (deforestation and degradation), loss of arable land and land degradation. This study assessed the past LULCC in the Atwima Nwabiagya which contains the Barekese and Owabi Headworks) and the old Kumasi Local Assemblies’ areas in Ghana and projected the scenario in 2040 for business-as-usual (BAU). The synergies of satellite imagery of 1990, 2000, 2010 and 2020 were classified with an overall accuracy of 90%. Markov Cellular-Automata method was used to forecast the future LULC pattern after detecting main driving forces of LULCC. The findings showed an extensive increase in built up areas from 11% in 1990 to 39% in 2020 owing largely to 23% decrease in forest cover and 6% decrease in agricultural lands within the past 30 years (1990-2020). The projected LULC under the BAU scenario for 2040 showed built-up surge from 39% to 45% indicating additional forest loss from 43% in 2020 to 40% and decreasing agricultural land from 17% to 14%. The main driver for the LULCC is clearly anthropogenic driven as the human population in the study area keeps rising every censual year. This study exemplifies the fast-tracked forest loss, loss of arable land and challenges on ecosystem sustainability of the Barekese-Owabi-Kumasi landscape. The current and projected maps necessitate the apt implementation of suitable interventions such as reforestation, protection measures and policy decision in deliberate land use planning to mitigate further loss of forest cover and safeguard the Barekese and Owabi headworks.

GIS-based landslide susceptibility modeling:A comparison between fuzzy multi-criteria and machine learning algorithms

Hazards and disasters have always negative impacts on the way of life.Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout theworld.The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin,Slovakia.In this regard,the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process(FDEMATEL-ANP),Naïve Bayes(NB)classifier,and random forest(RF)classifier were considered.Initially,a landslide inventory map was produced with 2000 landslide and nonlandslide points by randomly dividedwith a ratio of 70%:30%for training and testing,respectively.The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical,hydrological,lithological,and land cover factors.The ReliefF methodwas considered for determining the significance of selected conditioning factors and inclusion in the model building.Consequently,the landslide susceptibility maps(LSMs)were generated using the FDEMATEL-ANP,Naïve Bayes(NB)classifier,and random forest(RF)classifier models.Finally,the area under curve(AUC)and different arithmetic evaluation were used for validating and comparing the results and models.The results revealed that random forest(RF)classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve(AUC=0.954),lower value of mean absolute error(MAE=0.1238)and root mean square error(RMSE=0.2555),and higher value of Kappa index(K=0.8435)and overall accuracy(OAC=92.2%).

Robust Magnification Independent Colon Biopsy Grading System over Multiple Data Sources

Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification.This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes:normal,well,moderate,and poor.The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature,Gabor wavelet,wavelet moments,HSV histogram,color auto-correlogram,color moments,and morphological features that can be used to characterize different grades.Besides,the classifier is modeled as a multiclass structure with six binary class Bayesian optimized random forest(BO-RF)classifiers.This study uses four datasets(two collected from Indian hospitals—Ishita Pathology Center(IPC)of 4X,10X,and 40X and Aster Medcity(AMC)of 10X,20X,and 40X—two benchmark datasets—gland segmentation(GlaS)of 20X and IMEDIATREAT of 10X)comprising multiple microscope magnifications.Experimental results demonstrate that the proposed method outperforms the other methods used for colon cancer grading in terms of accuracy(97.25%-IPC,94.40%-AMC,97.58%-GlaS,99.16%-Imediatreat),sensitivity(0.9725-IPC,0.9440-AMC,0.9807-GlaS,0.9923-Imediatreat),specificity(0.9908-IPC,0.9813-AMC,0.9907-GlaS,0.9971-Imediatreat)and F-score(0.9725-IPC,0.9441-AMC,0.9780-GlaS,0.9923-Imediatreat).The generalizability of the model to any magnified input image is validated by training in one dataset and testing in another dataset,highlighting strong concordance in multiclass classification and evidencing its effective use in the first level of automatic biopsy grading and second opinion.

Machine learning model for snow depth estimation using a multisensory ubiquitous platform

Snow depth estimation is an important parameter that guides several hydrological applications and climate change prediction.Despite advances in remote sensing technology and enhanced satellite observations,the estimation of snow depth at local scale still requires improved accuracy and flexibility.The advances in ubiquitous and wearable technology promote new prospects in tackling this challenge.In this paper,a wearable IoT platform that exploits pressure and acoustic sensor readings to estimate and classify snow depth classes using some machine-learning models have been put forward.Significantly,the results of Random Forest classifier showed an accuracy of 94%,indicating a promising alternative in snow depth measurement compared to in situ,LiDAR,or expensive large-scale wireless sensor network,which may foster the development of further affordable ecological monitoring systems based on cheap ubiquitous sensors.

Real-Time Human Body Motion Capturing System

This paper proposes a human body motion capturing system using the depth images. It consists of three processes to estimate the human pose parameters. First, we develop a pixel-based body part classifier to segment the human silhouette into different body part sub-regions and extract the primary joints. Second, we convert the distribution of the joints to the feature vector and apply the regression forest to estimate human pose parameters. Third, we apply the temporal constraints mechanism to find the best human pose parameter with the minimum estimation error. In experiments, we show that our system can operate in real-time with sufficient accuracy.

An enhanced segmentation technique and improved support vector machine classifier for facial image recognition

Purpose-One of the challenging issues in computer vision and pattern recognition is face image recognition.Several studies based on face recognition were introduced in the past decades,but it has few classification issues in terms of poor performances.Hence,the authors proposed a novel model for face recognition.Design/methodology/approach-The proposed method consists of four major sections such as data acquisition,segmentation,feature extraction and recognition.Initially,the images are transferred into grayscale images,and they pose issues that are eliminated by resizing the input images.The contrast limited adaptive histogram equalization(CLAHE)utilizes the image preprocessing step,thereby eliminating unwanted noise and improving the image contrast level.Second,the active contour and level set-based segmentation(ALS)with neural network(NN)or ALS with NN algorithm is used for facial image segmentation.Next,the four major kinds of feature descriptors are dominant color structure descriptors,scale-invariant feature transform descriptors,improved center-symmetric local binary patterns(ICSLBP)and histograms of gradients(HOG)are based on clour and texture features.Finally,the support vector machine(SVM)with modified random forest(MRF)model for facial image recognition.Findings-Experimentally,the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy,similarity index,dice similarity coefficient,precision,recall and F-score results.However,the proposed method offers superior recognition performances than other state-of-art methods.Further face recognition was analyzed with the metrics such as accuracy,precision,recall and F-score and attained 99.2,96,98 and 96%,respectively.Originality/value-The good facial recognition method is proposed in this research work to overcome threat to privacy,violation of rights and provide better security of data.

Rapid Decomposition of Epoxy Resins via Raman Spectrometry in Combination with Machine Learning Algorithms

Epoxy resins are a group of important materials that have been used everywhere,and development of new materials of this kind with optimal mechanical properties from either bio-resources or industrial precursors has drawn great focus from scientists and engineers.By reacting different kinds of epoxy adhesives and curatives,massive kinds of epoxy resins with different characteristics are produced.Determination of original mixing ratio of epoxy adhesives and corresponding curatives of their curing products is useful in controlling and examining these materials.Here in this work,we described an efficient method based on Raman spectrometry and machine learning algorithms for rapid molar composition determination of epoxy resins.Original mixing ratio of epoxy adhesives and curatives could be calculated simply via Raman spectra of the products.Raman spectral data scanned during curing procedure was fed to random forest(RF)classification to calculate weights of Raman shift features and reduce data dimensionality,then spectral data of selected features were processed by partial least squares regression(PLSR)for model training and composition ratio determination.It turned out that ratio predictions of our model fit well to their actual values,with a coefficient of determination(R2)of 0.9926,and a root mean squared error(RMSE)of 0.0305.