Analysis of Machine Learning Techniques to Identify and Mitigate Phishing Websites

Purpose The authors argue that the current studies about malicious insiders confuse the fact that malicious attacks belong to two different categories, namely, those that launch instrumental attacks and expressive attacks. The authors collect malicious insider data from publicly available sources and use text-mining techniques to analyze the association between malicious insiders’ characteristics and the different types of attack. Design/methodology/approach The authors investigated the relationship between personality characteristics and different types of malicious attacks. For the personality characteristics, the authors use the same method as Liang et al. (2016), which extracted these characteristics based on a keyword-characteristic dictionary. For different types of malicious attacks, two raters rated each case based on criteria modified from criminology research to determine the degree of expressiveness and instrumentality. Findings The results show that malicious insiders who are manipulative or seeking personal gain tend to carry out instrumental attacks. Malicious insiders who are arrogant tend to conduct expressive attacks. Research limitations/implications This study uses third party articles to identify the personality characteristics of known malicious insiders. As such, not all personality characteristics may have been reported. Data availability was an issue. Practical implications Understanding if different personality characteristics lead different types of attacks can help managers identify employees who exhibit them and mitigate an attack before it occurs. Social implications Malicious insider attacks can have devastating results on businesses and employees. Help to identify potential malicious insiders before they act, may prevent undue harm. Originality/value This study used 132 cases of none malicious insiders to examine their attack objectives. No other study that the authors know of used that many cases.

The rapid growth of the Internet and information technologies and also the diminution of the price of hardware components like wireless sensors leads to the fast growth in Wireless Sensor Network (WSN). The WSN is a bunch/group of sensors that are located across the area, in order to monitor and determine environmental conditions such as temperature, humidity, vibrations, wind, water levels, pollution levels. WSNs are susceptible to major attacks like Denial of Service (DOS), Wormhole attack, Sinkhole attack, etc. The main threat to the WSN is because of the broadcast nature of the nodes in the network. Therefore, the security of WSNs is the essential part that must be done. Hence, to overcome these problems or threats, we are trying to detect it using AI technology. With the expanding fields of Machine Learning and Deep Learning, we can apply various algorithms in order to classify different types of attacks. Once we detect the attack properly, we can prevent it accordingly. We are using WSN-DS. It has 4 classes of attacks which are Grayhole, Blackhole, TDMA(Scheduling), and Flooding which comes under the category of DOS attacks. In this chapter, we have analysed and compared the accuracies of 5 main machine learning classification algorithms. Also, we have analysed the 1 deep learning algorithm. The ANN (Artificial Neural Network) and 5 machine learning algorithms have been trained on the dataset. Furthermore, we have used K-fold cross-validation to get even more accurate predictions. After analysis of these algorithms, we came to the point that, the Machine Learning algorithms like Random Forest, Support Vector Machines and Deep Learning algorithm, namely, Artificial Neural Network can help us for detecting intrusions in the system or network. This will help researchers in designing their own machine learning model on top of our suggested model.

Integrating machine learning (ML) models into healthcare systems is a rapidly evolving field with the potential to revolutionize care delivery. This study aimed to classify fertility rates and identify significant predictors using ML models among reproductive women in Ethiopia. This study utilized eight ML models in 5864 reproductive-age women using Ethiopian Demographic Health Survey (EDHS), 2019 data. Phyton programming language was used to develop these models. Predictors of fertility rate were determined using the feature important techniques. The performance of models was evaluated using accuracy, area under the curve (AUC), precision, recall, F1-score, specificity, and sensitivity. The mean age of participants was 32.7 (± 5.6) years. The random forest classifier (accuracy = 0.901 and AUC = 0.961) followed by a one-dimensional convolutional neural network (accuracy = 0.899 and AUC = 0.958), logistic regression (accuracy = 0.874 and AUC = 0.937), and gradient boost classifier (accuracy = 0.851 and AUC 0.927) were the top performing ML models. Family size, age, occupation, and education with an average importance score of 0.198, 0.151, 0.118, and 0.081, respectively were the top significant predictors of the fertility rate. The best ML models to classify and predict fertility rates were random forest, one-dimensional convolutional neural network, logistic regression, and gradient boost classifier. The findings on important factors of fertility rate can inform targeted public health, programs that address disparities related to family size, occupation, education, and other socioeconomic factors.

This study introduces a machine learning (ML) adaptive interface for classifying transparent conductive oxide (TCO) materials by examining their optical characteristics, such as their wavelength, optical density, absorption, and transmission. Six supervised ML models were considered in this study, namely decision tree, random forest, extreme gradient boosting (XGBoost), support vector classifier (SVC), K -nearest neighbors (KNN), and multilayer perceptron (MLP). These models were evaluated using metrics such as accuracy, precision, recall, F1-score, confusion matrices, receiver operating characteristic (ROC) curves, and learning curves. The comparison findings revealed that XGBoost delivered the best performance, achieving an accuracy of $$96.21\%$$ 96.21 % and a perfect area under the curve (AUC) score of 1.00, effectively distinguishing all five sampled TCO classes: aluminum-doped zinc oxide (AZO), fluorine-doped tin oxide (FTO), indium tin oxide (ITO), magnesium-doped zinc oxide (MZO), and zinc oxide (ZnO). These findings reveal ML’s potential for both scientific and industrial integration in material science research. XGBoost, as a high-precision model, may significantly reduce process time and equipment-intensive laboratory characterizations by allowing for the rapid and precise classification of data from optical measurements. This expedites the search for materials suitable for use in photovoltaics, optoelectronics, and display technologies, all of which require high transparency and conductivity. Overall, this work expands the potential for using ML to categorize materials, particularly XGBoost. It provides both scientific understanding and practical applications in innovative substance informatics and industrial quality control.

In a present-day world there are different types of attacks being launched on computing devices. World is experiencing more and more cyber-attacks and types of attacks are also increasing. For example, an IoT device in a home network can act as a botnet attacking other devices or there could be man in the middle attack. As time goes by more and more devices are being connected within any given network. All these devices will be vulnerable to attacks if any one of the devices is compromised within the network. This complicates Intrusion Detection in any given network. Manual detection and intervention is nearly impossible. Hence it is quintessential to detect different types of attacks with more confidence with less computation complexity and time. A lot of research work has already been done in this area where the attacks have been studied separately. In this paper we focus on detecting intrusions including IoT botnet attacks and other types of network attacks. To achieve this, we build a multiclass classification using supervised learning models along with the dimensionality reduction technique. Numerous studies on ML-based IDS have been using KDD or the upgraded versions of KDD dataset. In this study we used a new dataset, IoT network Intrusion Detection dataset.

Obesity is one of the fastest-growing health risks today, especially in countries like India, where lifestyle changes and poor physical activity are becoming common. Obesity causes diseases such as diabetes, heart disease, and high blood pressure. Regular health check-ups sometimes miss early symptoms. There is a need for smarter, continuous monitoring systems. This work proposes a health monitoring solution that uses Wireless Body Area Networks (WBANs) with machine learning (ML) techniques to identify obesity levels and detect irregular health patterns. A Real-world obesity dataset was used, containing personal and habit features like age, weight, height, food habits, physical activity, and family history. These were used to imitate data that wearable sensors may collect. Several ML models were trained and tested-including Support Vector Machine (SVM), K- Nearest Neighbours (KNN). Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and XGBoost - as well as a hybrid SVM+KNN model. The models were evaluated using various performance metrics. Among them, XGBoost gave the best overall accuracy of around 96%, followed by Random Forest and SVM. The hybrid model offered stable and reliable results in borderline cases. By integrating wearable sensors along with ML algorithms, the proposed system supports the detection of obesity-related risks beforehand and provides continuous health status, especially useful in remote and mobile healthcare settings.

Accurate real-time bus arrival information is essential for an efficient public transport network, as it significantly impacts passenger experience, system reliability, reduced waiting times, dwell times, and operational efficiency. In Sri Lanka's public transport system, current bus arrival time computations primarily rely on static data, neglecting real-time information and critical factors influencing travel times. This highlights the need to identify the unique variables affecting bus arrival times within the Sri Lankan context and to develop robust prediction models that account for these influences. While traditional methods such as Historical Average Models, Regression, Time Series Analysis, and Kalman Filtering have been used in previous research for short-term travel time predictions, Machine Learning (ML) approaches have proven to deliver superior accuracy. ML models are regarded as the most effective for heterogeneous, lane-less traffic conditions with varying traffic volumes, such as those found in Sri Lanka. ML techniques excel in processing large, high-quality datasets and provide accurate predictions by accounting for all relevant variables influencing travel times. Although research has been conducted on developing various basic ML models for travel time prediction, there is a noticeable gap in studies comparing these models to determine the most suitable one for the Sri Lankan context. A Long- Short Term Memory (LSTM) neural network is a deep learning model that is capable of handling long-term dependencies. In the context of bus travel time prediction, LSTMs can leverage historical traffic and travel data to capture temporal patterns and fluctuations that influence travel times. By evaluating LSTMs against basic machine learning models, this study seeks to explore the advantages of applying deep learning techniques to transportation forecasting, ultimately contributing to more accurate and efficient predictive systems in transit planning. The ML models selected in this study include two basic traditional models K- Nearest Neighbours (KNN) and Support Vector Regression (SVR) and four advanced models that utilize ensemble techniques and advanced optimization as Random Forest Regression (RFR), Ada Boost, XG Boost and Gradient Boosting Machine (GBM). The performance of these models was compared with the LSTM model to identify the gap in their accuracies.

There are different types of adversarial attacks and defences for machine learning algorithms which makes assessing the robustness of an algorithm a daunting task. Moreover, there is an intrinsic bias in these adversarial attacks and defences to make matters worse. Here, we organise the problems faced: a) Model Dependence, b) Insufficient Evaluation, c) False Adversarial Samples, and d) Perturbation Dependent Results. Based on this, we propose a model agnostic adversarial robustness assessment method based on L0 and L∞ distance-based norms and the concept of robustness levels to tackle the problems. We validate our robustness assessment on several neural network architectures (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) and adversarial defences for image classification problem. The proposed robustness assessment reveals that the robustness may vary significantly depending on the metric used (i.e., L0 or L∞). Hence, the duality should be taken into account for a correct evaluation. Moreover, a mathematical derivation and a counter-example suggest that L1 and L2 metrics alone are not sufficient to avoid spurious adversarial samples. Interestingly, the threshold attack of the proposed assessment is a novel L∞ black-box adversarial method which requires even more minor perturbation than the One-Pixel Attack (only 12% of One-Pixel Attack’s amount of perturbation) to achieve similar results. We further show that all current networks and defences are vulnerable at all levels of robustness, suggesting that current networks and defences are only effective against a few attacks keeping the models vulnerable to different types of attacks.

The rapid growth of cloud computing has led to an increased demand for security mechanisms to safeguard sensitive data and resources from cyber threats. Intrusion detection systems (IDS) play a crucial role in identifying unauthorized access or malicious activities within cloud environments. This paper presents a comparative study of machine learning (ML) techniques used in intrusion detection for cloud computing platforms. Various ML algorithms, including decision trees, support vector machines, k-nearest neighbors, and neural networks, are evaluated based on their performance in detecting different types of attacks. The study assesses the accuracy, efficiency, and scalability of these techniques in cloud environments, highlighting their strengths and limitations. The findings provide valuable insights into the selection of appropriate machine learning models for effective intrusion detection in dynamic and scalable cloud systems.

Prediction of glass forming ability in amorphous alloys based on different machine learning algorithms

Widespread use of internet connected devices results in a flow of data among the connected devices. This has led to creating these network systems prone to different type of cyber attacks among which network intrusion is one of a kind. To detect network intrusion, an intrusion detection system (IDS) is placed in the system. The previous works in this field classify the attack mainly with traditional machine learning algorithm based models. This experiment proposes Long Short Term Memory (LSTM) based neural network model along with two other machine learning models i.e. Support Vector Machine (SVM), K-Nearest Neighbor (KNN), which shows significant improved performance in intrusion detection. The performance of the model is proved to be effective comparing to the previous works and also deployable in real life environments which is validated by using explainable AI. For this experiment, Kyoto University Honeypot log dataset has been used to build and evaluate the performances of the IDS model.KeywordsIntrusion detectionLSTMSVMLIMEClass imbalance

Rainfall prediction is the highest research priority in flood-prone areas across the world. This work assesses the abilities of the Decision Tree (DT), Distributed Decision Tree (DDT), Naïve Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), K Nearest Neighbour (KNN), and Fuzzy Logic Decision Tree (FDTs) machine learning algorithms for the rainfall prediction across the Kashmir province of the Union Territory of Jammu & Kashmir. On application of Machine learning algorithms on geographical datasets gave performance accuracy varying from (78.61–81.53)%. Further again machine learning algorithms were reapplied on the dataset without season variable yet again performance ranged in between (77.5–81)%. Vigorous analysis has established that these machine learning models are robust and our study has established that the dataset reaches performance stagnation and thus resulting in performance capping. The stagnation is irrespective of the choice of algorithm and the performance shall not improvise beyond a specific value irrespective of the choice of the machine learning algorithm.KeywordsDecision treeGeographical dataRandom forestNaïve BayesDistribute decision tree

The internet and its various services have made users to easily communicate with each other. Internet benefits including online business and e-commerce. E-commerce has boosted online sales and online auction types. Despite their many uses and benefits, the internet and their services have various challenges, such as information theft, which challenges the use of these services. Information theft or phishing attacks are internet attacks that are major approach to success it is social engineering that the phisher has used. In these types of attacks, the attacker deceives the users and steals their valuable information by using a fake website that looks like real websites. The damage caused by fake websites and phishing attacks is so high that researchers are trying to identify these types of websites in different ways. So far, various methods have been developed to identify phishing web sites which most of them based on data- mining and learning machine are trying to identify these malicious websites. Artificial neural network is a data-mining method for identifying phishing websites which is used in most studies; however the error rate of this can be significant in detecting these websites, so learning-based optimization algorithm is used as a Swarm intelligence algorithm to reduce its error. In the proposed method, the error rate of multi-layer artificial neural network in detecting phishing websites is considered as a target function which minimized by using learning-based optimization algorithm. In the proposed method, learning- based optimization algorithm selects weights and bias of multi-layer artificial neural network optimally to minimize the error of clssification as an objective function. The datasets used to evaluate the proposed method are Phishing Websites explaind by others. The results of evaluating phishing attack dataset indicate that the rate of error of fake website detection in the proposed method is constantly reduced by repetition. The results of our assessment also indicate that the average accuracy, sensitivity, specificity, precision of the proposed method are 93.42, 92.27, 93.19 and 92.78%, respectively. The decision tree and regression are more accurate in detecting fake websites than artificial neural network.

Phishing websites are a problem for cybersecurity because they pretend to be real online services to get sensitive information from users like login details and financial information. The people who make these phishing websites can. Shut them down quickly which makes it hard for traditional methods to detect them. To solve this problem this paper talks about a system that uses machine learning to detect phishing websites by looking at the website address and domain. This way the system can detect phishing websites without having to look at the webpage. The system uses a set of data with 11,055 website addresses and 31 special features to test how well it works. These features look at things like the structure of the website address, who registered the domain and things that might indicate phishing. The system uses two kinds of classifiers Logistic Regression and Random Forest to test how well it can detect phishing websites. The system also uses an ensemble model that combines the results from both classifiers to make the predictions more reliable. The results show that thisensemble model is really good at detecting phishing websites and can balance being precise and catching all the phishing websites. The system is also good at telling the difference between fake websites. The people who made the system also created a user interface that can analyze website addresses in time which makes it more useful and practical. The system is a solution for detecting phishing websites in the real world and can be used in many different situations. Phishing websites are a problem but this system can help solve it. The system uses machine learning and a special ensemble model to detect phishing websites. It is really good, at it. Phishing websites will continue to be a problem. With this system we can detect them more easily. Keywords: Phishing Website Detection, Machine Learning, URL-Based Feature Extraction, Cybersecurity, URL Classification, Random Forest Classifier, Logistic Regression, XGBoost Algorithm, Fusion Model, Soft Voting Ensemble, Attack Type Identification, Credential Phishing, Financial Fraud Detection, ROC Curve Analysis, AUC PerformanceMetric

Security of networks is becoming progressively more necessary as the commercial internet keeps on developing. Intrusion detection techniques using network data have emerged as a significant area of investigation among the various network protection solutions. Many techniques for detecting network anomalies were suggested up to this point. A substantial amount of labeled data is needed for modeling in Machine Learning (ML) that constitutes the basis of the majority of more effective performance detection strategies. Nevertheless, manually filtering and labeling large-scale traffic data is not feasible in a real network. For real detection, network managers can only employ unsupervised ML techniques and the results of such strategies are far less accurate than those of supervised ML strategies. To increase network security’s efficiency of processing, adaptability, and specificity of detection, this method is useful for instantaneous, network-based safety applications because it maximizes intrusion detection accuracy across several attack types by utilizing quantum computer technology. This paper suggests an innovative multiclassification fusion strategy for optimizing network intrusion detection performance. Numerous fusion approaches are used in this research to combine the identification outcomes of effective intrusion detection approaches. A variety of publicly available KDD Cup 99 datasets were utilized for comparative investigations. According to the experiments, the suggested fusion approach [Decision Tree, K-Nearest Neighbor (KNN), improved Siam Intrusion Detection System (I-SiamIDS), Long Short-Term Memory (LSTM)] outperforms individual intrusion detection techniques in multiple measurements. It also exhibits enhanced resilience and stability, making it suitable for use in a variety of network-based safety recognition settings. The study uses the four primary classes of attacks such as Remote-to-Local (R2L), User to Root (U2R), Denial of Service (DoS), and Probing (Probe), which were compared with the performance metrics such as Precision, F-score, Accuracy, and Recall.