Counterintuitive Trend of Intrusion Pressure with Temperature in the Hydrophobic Cu2(tebpz) MOF.

Liquid porosimetry experiments reveal a peculiar trend of the intrusion pressure of water in hydrophobic Cu2(3,3',5,5'-tetraethyl-4,4'-bipyrazolate) MOF. At lower temperature (T) range, the intrusion pressure (Pi) increases with T. For higher T values, Pi first reaches a maximum and then decreases. This is at odds with the Young-Laplace law, which for systems showing a continuous decrease of contact angle with T predicts a corresponding reduction of the intrusion pressure. Though the Young-Laplace law is not expected to provide quantitative predictions at the subnanoscale of Cu2(tebpz) pores, the physical intuition suggests that to a reduction of their hydrophobicity corresponds a reduction of the Pi. Molecular dynamics simulations and sychrothron experiments allowed to clarify the mechanism of the peculiar trend of Pi with T. At increasing temperatures the vapor density within the MOF' pores grows significantly, bringing the corresponding partial pressure to ≈5 MPa. This pressure, which is consistent with the shift of Pi observed in liquid porosimetry, represents a threshold to be overcame before intrusion takes place. Beyond some value of temperature, the phenomenon of reduction of hydrophobicity (and water surface tension) dominated over the opposite effect of increase of vapor pressure and Pi inverts its trend with T.

Modelling approach integrating climate projections for coastal groundwater management.

Climate change and excessive groundwater extraction are major contributors to rising groundwater salinization due to seawater intrusion in coastal aquifers. This study aims to define a wide-applicable approach in which hydrological balance, boundary conditions, and irrigation water demand, defined over time considering climate change predictions, can integrated into a numerical model of the groundwater system. The approach was tested in a selected coastal aquifer. The approach spans from the past, used to define steady or almost natural conditions for calibration purposes (1950-2000 in the test), to the future (2100), divided in decade steps. The water balance analysis is based on an inverse hydrogeological water balance approach. The future climate change predictions are used to assess variations in boundary conditions of the groundwater model concerning salinity and sea level, recharge, and inflow from upstream aquifers. The approach considers changes in agricultural activities, groundwater demand, and river stage. The regional model is generated using the MODFLOW code for the groundwater flow model and the SEAWAT code for the salt transport model. The test concerns the Metaponto coastal plain, in which a porous aquifer is at salinization risk due to seawater intrusion. In this way, different influences of climate change and human activities are combined to define a 3d view of groundwater depletion and salinization effects. Quantifying these potential effects or risks, adaptation scenarios with numerical assessments are outlined in this study.

Analyzing anonymous activities using Interrupt-aware Anonymous User-System Detection Method (IAU-S-DM) in IoT.

The intrusion detection process is important in various applications to identify unauthorized Internet of Things (IoT) network access. IoT devices are accessed by intermediators while transmitting the information, which causes security issues. Several intrusion detection systems are developed to identify intruders and unauthorized access in different software applications. Existing systems consume high computation time, making it difficult to identify intruders accurately. This research issue is mitigated by applying the Interrupt-aware Anonymous User-System Detection Method (IAU-S-DM). The method uses concealed service sessions to identify the anonymous interrupts. During this process, the system is trained with the help of different parameters such as origin, session access demands, and legitimate and illegitimate users of various sessions. These parameters help to recognize the intruder's activities with minimum computation time. In addition, the collected data is processed using the deep recurrent learning approach that identifies service failures and breaches, improving the overall intruder detection rate. The created system uses the TON-IoT dataset information that helps to identify the intruder activities while accessing the different data resources. This method's consistency is verified using the metrics of service failures of 10.65%, detection precision of 14.63%, detection time of 15.54%, and classification ratio of 20.51%.

Suitability of rainwater harvesting in saline and arsenic affected areas of Bangladesh.

A major portion of Bangladesh is currently experiencing a scarcity of safe drinking water because of arsenic contamination, high salinity and human-induced pollution. The objectives of this study were to identify locations with a high scarcity of drinking water and suitability of harvesting rainwater. Kriging interpolation algorithms of Geographical Information System (GIS) was employed to identify the probable water scarce zones as well as suitable zones of harvesting rain water from the available data of secondary sources. Statistical methods were employed to cluster, correlate, and regress variables such as rainfall, salinity, and As. The results showed that groundwater quality in the southwestern parts of Bangladesh is saline with high concentration (>10000 µS/cm). On the other hand, the northeastern and southwestern parts of Bangladesh are also vulnerable to arsenic contamination (60 %-97 % of tubewells), compared to other regions. The rainfall zonation map, covering the years 1951-2022, indicated that the Sylhet division had the highest potential for rainfall (ranging from 2600 to 3900 mm). From this study it was demonstrated that Sylhet, Noakhali, Bhola, Barishall, Patuakhali, Bagerhat, and Khulna were identified as suitable places for sustainable rainwater harvesting (RWH). The findings of this study may play significant role towards achieving sustainable potable water supply in vulnerable zones, if they receive attention from policymakers.

An improved equilibrium optimization algorithm for feature selection problem in network intrusion detection.

In this paper, an enhanced equilibrium optimization (EO) version named Levy-opposition-equilibrium optimization (LOEO) is proposed to select effective features in network intrusion detection systems (IDSs). The opposition-based learning (OBL) approach is applied by this algorithm to improve the diversity of the population. Also, the Levy flight method is utilized to escape local optima. Then, the binary rendition of the algorithm called BLOEO is employed to feature selection in IDSs. One of the main challenges in IDSs is the high-dimensional feature space, with many irrelevant or redundant features. The BLOEO algorithm is designed to intelligently select the most informative subset of features. The empirical findings on NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets demonstrate the effectiveness of the BLOEO algorithm. This algorithm has an acceptable ability to effectively reduce the number of data features, maintaining a high intrusion detection accuracy of over 95%. Specifically, on the UNSW-NB15 dataset, BLOEO selected only 10.8 features on average, achieving an accuracy of 97.6% and a precision of 100%.

Measurement invariance of the Facebook intrusion questionnaire across 25 countries.

Facebook is one of the most popular social networking sites. However, Facebook intrusion or addiction is a growing concern as it involves an excessive attachment to Facebook, which disrupts daily functioning. To date, few studies have examined whether cross-cultural differences in the measurement of Facebook addiction exist. The aim of this study was to investigate the cross-cultural validity and measurement invariance of the Facebook Intrusion Questionnaire (FIQ), one of the most widely used measures of Facebook addiction, across 25 countries (N = 12,204, 62.3% female; mean age = 25 years). Multigroup confirmatory factor analyses (MGCFA) assessed cross-cultural validity as well as invariance. Additionally, individual confirmatory factor analyses evaluated the factorial structure and measurement invariance across genders in each country. The FIQ demonstrated partial metric invariance across countries and metric (13 countries), scalar (11 countries) or residual (10 countries) invariance across genders within individual countries. A one-factor model indicated a good fit in 18 countries. Cronbach's alpha for the entire sample was .85. Our findings suggest that the FIQ may provide an adequate assessment of Facebook addiction that is psychometrically equivalent across cultures. Moreover, the questionnaire seems to be universal and suitable for studying different social media in distinct cultural environments. Consequently, this robust tool can be used to explore behaviours related to specific media that are particularly popular in any given country.

A Cooperative Intrusion Detection System for the Internet of Things Using Convolutional Neural Networks and Black Hole Optimization.

Maintaining security in communication networks has long been a major concern. This issue has become increasingly crucial due to the emergence of new communication architectures like the Internet of Things (IoT) and the advancement and complexity of infiltration techniques. For usage in networks based on the Internet of Things, previous intrusion detection systems (IDSs), which often use a centralized design to identify threats, are now ineffective. For the resolution of these issues, this study presents a novel and cooperative approach to IoT intrusion detection that may be useful in resolving certain current security issues. The suggested approach chooses the most important attributes that best describe the communication between objects by using Black Hole Optimization (BHO). Additionally, a novel method for describing the network's matrix-based communication properties is put forward. The inputs of the suggested intrusion detection model consist of these two feature sets. The suggested technique splits the network into a number of subnets using the software-defined network (SDN). Monitoring of each subnet is done by a controller node, which uses a parallel combination of convolutional neural networks (PCNN) to determine the presence of security threats in the traffic passing through its subnet. The proposed method also uses the majority voting approach for the cooperation of controller nodes in order to more accurately detect attacks. The findings demonstrate that, in comparison to the prior approaches, the suggested cooperative strategy can detect assaults in the NSLKDD and NSW-NB15 datasets with an accuracy of 99.89 and 97.72 percent, respectively. This is a minimum 0.6 percent improvement.

Software-Defined-Networking-Based One-versus-Rest Strategy for Detecting and Mitigating Distributed Denial-of-Service Attacks in Smart Home Internet of Things Devices.

The number of connected devices or Internet of Things (IoT) devices has rapidly increased. According to the latest available statistics, in 2023, there were approximately 17.2 billion connected IoT devices; this is expected to reach 25.4 billion IoT devices by 2030 and grow year over year for the foreseeable future. IoT devices share, collect, and exchange data via the internet, wireless networks, or other networks with one another. IoT interconnection technology improves and facilitates people's lives but, at the same time, poses a real threat to their security. Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are considered the most common and threatening attacks that strike IoT devices' security. These are considered to be an increasing trend, and it will be a major challenge to reduce risk, especially in the future. In this context, this paper presents an improved framework (SDN-ML-IoT) that works as an Intrusion and Prevention Detection System (IDPS) that could help to detect DDoS attacks with more efficiency and mitigate them in real time. This SDN-ML-IoT uses a Machine Learning (ML) method in a Software-Defined Networking (SDN) environment in order to protect smart home IoT devices from DDoS attacks. We employed an ML method based on Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (kNN), and Naive Bayes (NB) with a One-versus-Rest (OvR) strategy and then compared our work to other related works. Based on the performance metrics, such as confusion matrix, training time, prediction time, accuracy, and Area Under the Receiver Operating Characteristic curve (AUC-ROC), it was established that SDN-ML-IoT, when applied to RF, outperforms other ML algorithms, as well as similar approaches related to our work. It had an impressive accuracy of 99.99%, and it could mitigate DDoS attacks in less than 3 s. We conducted a comparative analysis of various models and algorithms used in the related works. The results indicated that our proposed approach outperforms others, showcasing its effectiveness in both detecting and mitigating DDoS attacks within SDNs. Based on these promising results, we have opted to deploy SDN-ML-IoT within the SDN. This implementation ensures the safeguarding of IoT devices in smart homes against DDoS attacks within the network traffic.

Evolving pathways towards water security in the Vietnamese Mekong Delta: An adaptive management perspective.

The interplay of climate change, upstream hydropower development, and local water engineering interventions for agricultural production contributes substantially to the transformation of waterscapes and water scarcity in the Vietnamese Mekong Delta. This paper aims to examine how these dynamics are linked to the paradigm shift in water management in An Giang and Ben Tre, the two ecologically distinct provinces that face serious water scarcity in the delta. We used the adaptive management concept to examine how state-led policy directions from food security towards water security enable change in water management that gives priority to water retention. While policy learning is evident, questions remain about how this ad-hoc solution could help address the presently acute water scarcity and water security over the long term. The paper advocates achieving water security should focus not only on diplomatic interventions into upstream climate-development complexities but also local water-livelihood politics.

Effect of seasonal variations in sea level on residual saltwater removal upstream of subsurface dam in coastal layered heterogeneity aquifers.

Subsurface dams have been recognized as one of the most effective measures for preventing saltwater intrusion. However, it may result in large amounts of residual saltwater being trapped upstream of the dam and take years to decades to remove, which may limit the utilization of fresh groundwater in coastal areas. In this study, field-scale numerical simulations were used to investigate the mechanisms of residual saltwater removal from a typical stratified aquifer, where an intermediate low-permeability layer (LPL) exists between two high-permeability layers, under the effect of seasonal sea level fluctuations. The study quantifies and compares the time of residual saltwater removal (Tre) for constant sea level (CSL) and seasonally varying sea level (FSL) scenarios. The modelling results indicate that, in most cases, seasonal fluctuations in sea level facilitate the dilution of residual saltwater and thus accelerate residual saltwater removal compared to a static sea level scenario. However, accounting for seasonal sea level variations may increase the required critical dam height (the minimum dam height required to achieve complete residual saltwater removal). Sensitivity analyses show that Tre decreases with increasing height of subsurface dam (Hd) under CSL or weaker sea level fluctuation scenarios; however, when the magnitude of sea level fluctuation is large, Tre changes non-monotonically with Hd. Tre decreases with increasing distance between subsurface dam and ocean for both CSL and FSL scenarios. We also found that stratification model had a significant effect on Tre. The increase in LPL thickness for both CSL and FSL scenarios leads to a decrease in Tre and critical dam height. Tre generally shows a non-monotonically decreasing trend as LPL elevation increases. These quantitative analyses provide valuable insights into the design of subsurface dams in complex situations.

Comparable bone penetration between antibiotic-loaded and plain bone cement in total knee arthroplasty.

INTRODUCTION: One of the main concerns around the use of antibiotic-loaded bone cement (ALBC) is the potential reduction in the mechanical properties of the cement when antibiotics are admixed. The purpose of this study was to determine whether there is a difference between plain cement and ALBC in terms of radiological intrusion into the bone in total knee arthroplasties (TKAs). METHODS: Prospective randomized study of 80 consecutive patients who underwent TKA. Depending on the cement used, patients were divided into two groups by a computer-generated randomization programme: the cement without antibiotic (Group 1) or the ALBC (Group 2). Cement intrusion was measured in postoperative radiographs in eight different regions in the tibial component and six regions in the femoral component. RESULTS: The average cement intrusion was similar in both groups (p = nonsignificance [n.s.]). Group 1 (plain cement) had an average cement intrusion in the femur of 1.4 mm (±0.4) and 2.4 mm (±0.4) in the tibia. In Group 2 (ALBC), the average cement intrusion in the femur came to 1.6 (±0.5) and 2.4 mm (±0.5) in the tibia. In 80% of the patients, the cement intrusion in the tibia averaged a minimum of 2 mm, being similar in both groups (p = n.s.). CONCLUSION: There are no differences in bone intrusion when comparing plain cement to ALBC. Therefore, the use of ALBC in primary TKA may be indicated, achieving optimal bone penetration. LEVEL OF EVIDENCE: Level I.

Increased ingestion and toxicity of polyamide microplastics in Nile tilapia with increase of salinity.

Microplastics pollution and salinity intrusion in freshwater ecosystem is one of the worldwide climate change consequences those have negative impacts on the physiology of aquatic organisms. Hence, a 15-day experiment was carried out where Nile tilapia (Oreochromis niloticus) was exposed to different salinity gradients i.e. 0 ‰, 3 ‰, 6 ‰, 9 ‰, and 12 ‰ alone and along with 10 mg/L polyamide microplastics (PA-MP) in order to measure its effects on the hematology, gill, and intestinal morphology. The results exhibited that all the fish treated with PA-MP ingested microplastics and the quantity of accumulation was significantly greater in higher salinity gradients (9 ‰ and 12 ‰). In addition, the PA-MP treated fish showed increased glucose level and at the same time reduced hemoglobin concentration with the increase of salinity. The percentages of abnormalities in erythrocytes both cellular (twin, teardrop and spindle shaped) and nuclear (notched nuclei, nuclear bridge and karyopyknosis) significantly enhanced with PA-MP exposure again in higher salinity treatments (9 ‰ and 12 ‰). The principal component analysis (PCA) exhibited that the addition of 10 mg/L PA-MP negatively affected the hematology of Nile tilapia than that of salinity treatments alone. Besides, the exposure of PA-MP in 9 ‰ and 12 ‰ salinity gradients escalated the severity of histological damages in gills and intestine. Overall, this experiment affirms that the increase of salinity enhanced the microplastics ingestion and toxicity in Nile tilapia, therefore, PA-MP possibly is addressed as additional physiological stressors along with increased salinity gradients in environment.

Research on the Method of Foreign Object Detection for Railway Tracks Based on Deep Learning.

Addressing the limitations of current railway track foreign object detection techniques, which suffer from inadequate real-time performance and diminished accuracy in detecting small objects, this paper introduces an innovative vision-based perception methodology harnessing the power of deep learning. Central to this approach is the construction of a railway boundary model utilizing a sophisticated track detection method, along with an enhanced UNet semantic segmentation network to achieve autonomous segmentation of diverse track categories. By employing equal interval division and row-by-row traversal, critical track feature points are precisely extracted, and the track linear equation is derived through the least squares method, thus establishing an accurate railway boundary model. We optimized the YOLOv5s detection model in four aspects: incorporating the SE attention mechanism into the Neck network layer to enhance the model's feature extraction capabilities, adding a prediction layer to improve the detection performance for small objects, proposing a linear size scaling method to obtain suitable anchor boxes, and utilizing Inner-IoU to refine the boundary regression loss function, thereby increasing the positioning accuracy of the bounding boxes. We conducted a detection accuracy validation for railway track foreign object intrusion using a self-constructed image dataset. The results indicate that the proposed semantic segmentation model achieved an MIoU of 91.8%, representing a 3.9% improvement over the previous model, effectively segmenting railway tracks. Additionally, the optimized detection model could effectively detect foreign object intrusions on the tracks, reducing missed and false alarms and achieving a 7.4% increase in the mean average precision (IoU = 0.5) compared to the original YOLOv5s model. The model exhibits strong generalization capabilities in scenarios involving small objects. This proposed approach represents an effective exploration of deep learning techniques for railway track foreign object intrusion detection, suitable for use in complex environments to ensure the operational safety of rail lines.

Using Machine Learning Multiclass Classification Technique to Detect IoT Attacks in Real Time.

This paper presents a real-time intrusion detection system (IDS) aimed at detecting the Internet of Things (IoT) attacks using multiclass classification models within the PySpark architecture. The research objective is to enhance detection accuracy while reducing the prediction time. Various machine learning algorithms are employed using the OneVsRest (OVR) technique. The proposed method utilizes the IoT-23 dataset, which consists of network traffic from smart home IoT devices, for model development. Data preprocessing techniques, such as data cleaning, transformation, scaling, and the synthetic minority oversampling technique (SMOTE), are applied to prepare the dataset. Additionally, feature selection methods are employed to identify the most relevant features for classification. The performance of the classifiers is evaluated using metrics such as accuracy, precision, recall, and F1 score. The results indicate that among the evaluated algorithms, extreme gradient boosting achieves a high accuracy of 98.89%, while random forest demonstrates the most efficient training and prediction times, with a prediction time of only 0.0311 s. The proposed method demonstrates high accuracy in real-time intrusion detection of IoT attacks, outperforming existing approaches.

BFLIDS: Blockchain-Driven Federated Learning for Intrusion Detection in IoMT Networks.

The Internet of Medical Things (IoMT) has significantly advanced healthcare, but it has also brought about critical security challenges. Traditional security solutions struggle to keep pace with the dynamic and interconnected nature of IoMT systems. Machine learning (ML)-based Intrusion Detection Systems (IDS) have been increasingly adopted to counter cyberattacks, but centralized ML approaches pose privacy risks due to the single points of failure (SPoFs). Federated Learning (FL) emerges as a promising solution, enabling model updates directly on end devices without sharing private data with a central server. This study introduces the BFLIDS, a Blockchain-empowered Federated Learning-based IDS designed to enhance security and intrusion detection in IoMT networks. Our approach leverages blockchain to secure transaction records, FL to maintain data privacy by training models locally, IPFS for decentralized storage, and MongoDB for efficient data management. Ethereum smart contracts (SCs) oversee and secure all interactions and transactions within the system. We modified the FedAvg algorithm with the Kullback-Leibler divergence estimation and adaptive weight calculation to boost model accuracy and robustness against adversarial attacks. For classification, we implemented an Adaptive Max Pooling-based Convolutional Neural Network (CNN) and a modified Bidirectional Long Short-Term Memory (BiLSTM) with attention and residual connections on Edge-IIoTSet and TON-IoT datasets. We achieved accuracies of 97.43% (for CNNs and Edge-IIoTSet), 96.02% (for BiLSTM and Edge-IIoTSet), 98.21% (for CNNs and TON-IoT), and 97.42% (for BiLSTM and TON-IoT) in FL scenarios, which are competitive with centralized methods. The proposed BFLIDS effectively detects intrusions, enhancing the security and privacy of IoMT networks.

Effectiveness of different intrusion modes of maxillary anterior teeth with mini-implants in clear aligner treatment: a three-dimensional finite element analysis.

BACKGROUND: The intrusion of maxillary anterior teeth is often required and there are various intrusion modes with mini-implants in clear aligner treatment. The objective of this study was to evaluate the effectiveness of maxillary anterior teeth intrusion with different intrusion modes, aiming to provide references for precise and safe intrusion movements in clinical practice. METHODS: Cone-beam computed tomography and intraoral optical scanning data of a patient were collected. Finite element models of the maxilla, maxillary dentition, periodontal ligaments (PDLs), clear aligner (CA), attachments, and mini-implants were established. Different intrusion modes of the maxillary anterior teeth were simulated by changing the mini-implant site (between central incisors, between central and lateral incisor, between lateral incisor and canine), loading site (between central incisors, on central incisor, between central and lateral incisor, between lateral incisor and canine), and loading mode (labial loading and labiolingual loading). Ten conditions were generated and intrusive forces of 100 g were applied totally. Then displacement tendency of the maxillary anterior teeth and CA, and stress of the PDLs were analyzed. RESULTS: For the central incisor under condition L14 and for the canine under conditions L11, L13, L23, and L33, the intrusion amount was negative. Under other conditions, the intrusion amount was positive. The labiolingual angulation of maxillary anterior teeth exhibited positive changes under all conditions, with greater changes under linguoincisal loading. The mesiodistal angulation of canine exhibited positive changes under labial loading, while negative changes under linguoincisal loading except for condition L14. CONCLUSIONS: The intrusion amount, labiolingual and mesiodistal angulations of the maxillary anterior teeth were affected by the mini-implant site, loading site, and loading mode. Labial and linguoincisal loading may have opposite effects on the intrusion amount of maxillary anterior teeth and the mesiodistal angulation of canine. The labiolingual angulation of the maxillary incisors would increase under all intrusion modes, with greater increases under linguoincisal loading.

Confidence for intrusion errors during the attentional blink depends on target-defining features.

Research surrounding the attentional blink phenomenon - a deficit in responding to the second of two temporally proximal stimuli when presented 150-500 ms after the first - has used a wide variety of target-defining and response features of stimuli. The typical U-shape curve for absolute performance is robust, surviving across most stimulus features, and therefore changes in performance are discussed as dynamics in an attentional system that are nonspecific a stimulus type. However, the patterns of errors participants make might not show the same robustness, and participants' confidences in these errors might differ - potentially suggesting the involvement of different attentional or perceptual mechanisms. The present research is a comparison of error patterns and confidence in those errors when letter target stimuli are defined by either the color of the letter, the presence of a surrounding annulus, or the color of the annulus. Across three experiments, we show that participants erroneously report stimuli that are further away from T2 and they are similarly confident in specifically their post-target errors as their correct responses when annuli define targets, but not when color of the letter defines targets. Experiment 3 provides some evidence to suggest that this error pattern and associated confidence is time-dependent when the color of the annulus defines the target, but not when the color of the letter defines the target. These results raise questions concerning the nature of the errors and possibly the mechanisms of the attentional blink phenomenon itself.

Enhanced Network Intrusion Detection System for Internet of Things Security Using Multimodal Big Data Representation with Transfer Learning and Game Theory.

Internet of Things (IoT) applications and resources are highly vulnerable to flood attacks, including Distributed Denial of Service (DDoS) attacks. These attacks overwhelm the targeted device with numerous network packets, making its resources inaccessible to authorized users. Such attacks may comprise attack references, attack types, sub-categories, host information, malicious scripts, etc. These details assist security professionals in identifying weaknesses, tailoring defense measures, and responding rapidly to possible threats, thereby improving the overall security posture of IoT devices. Developing an intelligent Intrusion Detection System (IDS) is highly complex due to its numerous network features. This study presents an improved IDS for IoT security that employs multimodal big data representation and transfer learning. First, the Packet Capture (PCAP) files are crawled to retrieve the necessary attacks and bytes. Second, Spark-based big data optimization algorithms handle huge volumes of data. Second, a transfer learning approach such as word2vec retrieves semantically-based observed features. Third, an algorithm is developed to convert network bytes into images, and texture features are extracted by configuring an attention-based Residual Network (ResNet). Finally, the trained text and texture features are combined and used as multimodal features to classify various attacks. The proposed method is thoroughly evaluated on three widely used IoT-based datasets: CIC-IoT 2022, CIC-IoT 2023, and Edge-IIoT. The proposed method achieves excellent classification performance, with an accuracy of 98.2%. In addition, we present a game theory-based process to validate the proposed approach formally.

Optimizing IoT Intrusion Detection Using Balanced Class Distribution, Feature Selection, and Ensemble Machine Learning Techniques.

Internet of Things (IoT) devices are leading to advancements in innovation, efficiency, and sustainability across various industries. However, as the number of connected IoT devices increases, the risk of intrusion becomes a major concern in IoT security. To prevent intrusions, it is crucial to implement intrusion detection systems (IDSs) that can detect and prevent such attacks. IDSs are a critical component of cybersecurity infrastructure. They are designed to detect and respond to malicious activities within a network or system. Traditional IDS methods rely on predefined signatures or rules to identify known threats, but these techniques may struggle to detect novel or sophisticated attacks. The implementation of IDSs with machine learning (ML) and deep learning (DL) techniques has been proposed to improve IDSs' ability to detect attacks. This will enhance overall cybersecurity posture and resilience. However, ML and DL techniques face several issues that may impact the models' performance and effectiveness, such as overfitting and the effects of unimportant features on finding meaningful patterns. To ensure better performance and reliability of machine learning models in IDSs when dealing with new and unseen threats, the models need to be optimized. This can be done by addressing overfitting and implementing feature selection. In this paper, we propose a scheme to optimize IoT intrusion detection by using class balancing and feature selection for preprocessing. We evaluated the experiment on the UNSW-NB15 dataset and the NSL-KD dataset by implementing two different ensemble models: one using a support vector machine (SVM) with bagging and another using long short-term memory (LSTM) with stacking. The results of the performance and the confusion matrix show that the LSTM stacking with analysis of variance (ANOVA) feature selection model is a superior model for classifying network attacks. It has remarkable accuracies of 96.92% and 99.77% and overfitting values of 0.33% and 0.04% on the two datasets, respectively. The model's ROC is also shaped with a sharp bend, with AUC values of 0.9665 and 0.9971 for the UNSW-NB15 dataset and the NSL-KD dataset, respectively.

Impact of ionic strength on goethite colloids co-transported with arsenite (As3+) through a saturated sand column under anoxic condition: Experiment and mathematical modeling.

The knowledge about co-transport of goethite and As3+ to investigate the effect of goethite colloids on As3+ transport under various degrees of seawater intrusion, particular extremely conditions, in groundwater environment is still limited. The main objective is to investigate the influence of seawater intrusion on the sorption, migration, and reaction of As3+and goethite colloids into sand aquifer media under anoxic conditions by using the bench-scale and reactive geochemical modeling. The research consisted of two parts as follows: 1) column transport experiments consisting of 8 columns, which were packed by using synthesis groundwater at IS of 0.5, 50, 200, and 400 mM referring to the saline of seawater system in the study area, and 2) reactive transport modeling, the mathematical model (HYDRUS-1D) was applied to describe the co-transport of As3+ and goethite. Finally, to explain the interaction of goethite and As3+, the Derjaguin-Landau-Verwey-Overbeek (DLVO) calculation was considered to support the experimental results and HYDRUS-1D model. The results of column experiments showed goethite colloids can significantly inhibit the mobility of As3+ under high IS conditions (>200 mM). The Rf of As3+ bound to goethite grows to higher sizes (47.5 and 65.0 µm for 200 and 400 mM, respectively) of goethite colloid, inhibiting As3+ migration through the sand columns. In contrast, based on Rf value, goethite colloids transport As3+ more rapidly than a solution with a lower IS (0.5 and 50 mM). The knowledge gained from this study would help to better understand the mechanisms of As3+ contamination in urbanized coastal groundwater aquifers and to assess the transport of As3+ in groundwater, which is useful for groundwater management, including the optimum pumping rate and long-term monitoring of groundwater quality.