Types Of Intrusions Research Articles

Enhancing Internet of Things Network Security Using Hybrid CNN and XGBoost Model Tuned via Modified Reptile Search Algorithm

This paper addresses the critical security challenges in the internet of things (IoT) landscape by implementing an innovative solution that combines convolutional neural networks (CNNs) for feature extraction and the XGBoost model for intrusion detection. By customizing the reptile search algorithm for hyperparameter optimization, the methodology provides a resilient defense against emerging threats in IoT security. By applying the introduced algorithm to hyperparameter optimization, better-performing models are constructed capable of efficiently handling intrusion detection. Two experiments are carried out to evaluate the introduced technique. The first experiment tackles detection through binary classification. The second experiment handles the task by specifically identifying the type of intrusion through multi-class classification. A publicly accessible real-world dataset has been utilized for experimentation and several contemporary algorithms have been subjected to a comparative analysis. The introduced algorithm constructed models with the best performance in both cases. The outcomes have been meticulously statistically evaluated and the best-performing model has been analyzed using Shapley additive explanations to determine feature importance for model decisions.

M-MultiSVM: An efficient feature selection assisted network intrusion detection system using machine learning

The intrusions are increasing daily, so there is a huge amount of privacy violations, financial loss, illegal transferring of information, etc. Various forms of intrusion occur in networks, such as menacing networks, computer resources and network information. Each type of intrusion focuses on specified tasks, whereas the hackers may focus on stealing confidential data, industrial secrets and personal information, which is then leaked to others for illegal gains. Due to the false detection of attacks in the security and changing environmental fields, limitations like data lagging on actual attacks and sustaining financial harms occur. To resolve this, automatic abnormality detection systems are required to secure the required computing ability and to analyze the attacks. Hence, an efficient automated intrusion detection system using machine learning methodology is proposed in this research paper. Initially, the data are gathered from CSE-CIC-IDS 2018 and UNSW-NB15 datasets. The acquired data are pre-processed using Null value handling and Min-Max normalization. Null value handling is used to remove missing values and irrelevant parameters. Min-Max normalization adjusted the unnormalized data in the pre-processing stage. After pre-processing, the class imbalance problem is reduced by using the Advanced Synthetic Minority Oversampling Technique (ASmoT). ASmoT aims to balance the class and reduce imbalance class problems and overfitting issues. The next phase is feature extraction, which is performed by Modified Singular Value Decomposition (M-SvD). M-SvD extracts essential features such as basic features, content features and traffic features from the input. The extracted features are optimized by the Opposition-based Northern Goshawk Optimization algorithm (ONgO). These optimal features are able to produce optimal output. After feature selection, the different types of attacks are classified by a hybrid machine learning model called Mud Ring assisted multilayer support vector machine (M-MultiSVM) and finally, the hyperparameters are tuned by the Mud Ring optimization algorithm. Thus, the proposed M-MultiSVM model can efficiently detect intrusion in the network. The performance metrics show that the proposed system achieved 99.89 % accuracy by using the CSE-CIC-IDS 2018 dataset; also, the proposed system achieved 97.535 % accuracy by using the UNSW-NB15 dataset.

Hybrid intrusion detection model for Internet of Things (IoT) network environment

Technology development brought numerous lifestyle changes. People move around with smart gadgets and devices in the home, work environment, and familiar places. The Internet acts as a backbone for all applications and connecting multiple devices to set up a smart environment is technically termed as IoT (Internet of Things). The feature merits of IoT are explored in numerous fields from simple psychical data measurement to complex trajectory data measurement. Where the place is inaccessible to humans, IoT devices are used to analyze the region. Though IoT provides numerous benefits, due to its size and energy limitations, it faces security and privacy issues. Intrusions in IoT networks have become common due to these limitations and various intrusion detection methods are introduced in the past decade. Existing learning-based methods lag in performance while detecting multiple attacks. Conventional detection models could not be able to detect the intrusion type in detail. The diverse IoT network data has several types of high dimensional features which could not be effectively processed by the conventional methods while detecting intrusions. Recently improvements in learning strategies proved the performance of deep learning models in intrusion detection systems. However, detecting multiple attacks using a single deep learning model is quite complex. Thus, in this research a multi deep learning model is presented to detect multiple attacks. The initial intrusion features are extracted through the AlexNet, and then essential features are selected through bidirectional LSTM. Finally, the selected features are classified using the decision tree C5.0 algorithm to attain better detection accuracy. Proposed model experimentations include benchmark NSL-KDD dataset to verify performances and compared the results with existing IDSs based on DeepNet, Multi-CNN, Auto Encoder, Gaussian mixture, Generative adversarial Network, and Convolutional Neural Network models. The proposed model attained maximum detection accuracy of 98.8% over conventional methods. Overall, an average of 15% improved detection performance is attained by the proposed model in detecting several types of intrusions in the IoT network.

A MACHINE LEARNING APPROACH FOR INTRUSION DETECTION

In the rapidly evolving digital landscape, ensuring the security of computer systems and networks has become a paramount concern. Traditional methods often struggle to keep pace with the increasing complexity and diversity of cyber threats. In the context of cybersecurity and network analysis, the crucial tools for identifying and responding to unauthorized access, malicious activities, and potential threats within a network or system cannot take place. The significance of intrusion detection is underscored by the escalating frequency and sophistication of cyberattacks that can lead to data breaches, service disruptions, and financial losses. The XGBoost algorithms ability to handle complex, imbalanced datasets and its exceptional performance in various domains make it a promising candidate for improving intrusion detection accuracy. To evaluate the efficacy of the XGBoost algorithm, extensive experiments are conducted using the KDDCup99 dataset. This dataset represents diverse network traffic scenarios and intrusion types, providing a comprehensive evaluation environment. The Random Forest algorithm, known for its robustness in handling classification tasks, is selected as a baseline for comparison. The performance of the XGBoost algorithm is assessed using multiple performance metrics, including accuracy, precision, recall, F1-score. The outcomes highlight the potential advantages of utilizing XGBoost for intrusion detection. KEYWORDS: Cybersecurity, Network Analysis, Intrusion Detection, XGBoost Algorithm, Random Forest Algorithm, KDDCup99 Dataset.

A Predictive Model Using Deep Learning Neural Network for Efficient Intrusion Detection

Abstract: Network intrusion detection system helps to detect exploitations and mitigate damages. A network intrusion detection system detects the network traffic that deviates from the normal behavioral pattern. Developing an efficient intrusion detection system has many challenges and the patterns associated with one type of intrusion differ from other intrusions. In such situations, understanding different patterns and differentiating intrusions becomes essential to detect anomalies and attacks in the network. Deep learning models offer more power and intelligence to the detection system and extend the ability to differentiate & understand the network feature characteristics, also machine learning models with feature selection showed high performance in intrusion detections. This paper evaluates the proposed deep learning neural network model and machine learning models using feature selection for efficient intrusion detection using real world dataset.

Leveraging Machine Learning for Network Intrusion Detection in Social Internet Of Things (SIoT) Systems

This research investigates the application of machine learning models for network intrusion detection in the context of Social Internet of Things (SIoT) systems. We evaluate Convolutional Neural Network with Generative Adversarial Network (CNN+GAN), Generative Adversarial Network (GAN), and Logistic Regression models using the CIC IoT Dataset 2023. CNN+GAN emerges as a promising approach, exhibiting superior performance in accurately identifying diverse intrusion types. Our study emphasizes the significance of advanced machine learning techniques in enhancing SIoT security by effectively detecting anomalous behaviours within socially interconnected environments. The findings provide practical insights for selecting suitable intrusion detection methods and highlight the need for ongoing research to address evolving intrusion scenarios and vulnerabilities in SIoT ecosystems.

Hybrid CNN+LSTM Deep Learning Model for Intrusions Detection Over IoT Environment

The connectivity of devices through the internet plays a remarkable role in our daily lives. Many network-based applications are utilized in different domains, e.g., health care, smart environments, and businesses. These applications offer a wide range of services and provide services to large groups. Therefore, the safety of network-based applications has always been an area of research interest for academia and industry alike. The evolution of deep learning has enabled us to explore new areas of research. Hackers make use of the vulnerabilities in networks and attempt to gain access to confidential systems and information. This information and access to systems can be very harmful and portray losses beyond comprehension. Therefore, detection of these network intrusions is of the utmost importance. Deep learning-based techniques require minimal inputs while exploring every possible feature set in the network. Thus, in this paper, we present a hybrid CNN+LSTM deep learning model for the detection of network intrusions. In this research, we detect DDOS types of network intrusions, i.e., R2L, R2R, Prob, and which belong to the active attack category, and PortScan, which falls in the passive attack category. For this purpose, we used the benchmark CICIDS2017 dataset for conducting the experiments and achieved an accuracy of 99.82% as demonstrated in the experimental results.

Response of forest Turtur doves to conspecific and congeneric songs in sympatry and allopatry

Birds have a diverse acoustic communication system, and the ability to recognise their own species’ song from a distance facilitates complex behaviours related to mate attraction and rival deterrence. However, certain species, including doves, do not learn songs and their vocal repertoires are much simpler than those of better-studied songbirds. In these so-called non-learning birds, relatively little is known about the role that bird song plays in intra- and interspecific interactions, and how such behaviours might be acquired (inherited or learned from experience). To investigate this question, we focused on two species of African wood doves whose long-range songs are used in a territorial context. Specifically, we examined the responses of sympatric and allopatric populations of male blue-headed wood-doves (Turtur brehmeri) and tambourine doves (Turtur tympanistria) to different types of simulated territorial intrusions, i.e. playback of conspecific, congeneric, and control songs. We aimed to assess (i) whether these species, which have similar songs, respond only to their own species' song or exhibit interspecific territoriality, and (ii) if the response pattern is affected by the presence or absence of congeners in the general area. We found that both species responded strongly to playback of their own species in both sympatric and allopatric populations. In allopatry, though, male tambourine doves misdirected their response and also approached the playback of congeneric songs. Our results indicate that, in areas where the studied Turtur doves live in sympatry, they do not exhibit consistent interspecific territoriality. However, we cannot exclude the possibility that the smaller tambourine dove avoids its larger congener during the process of territory establishment. The difference in tambourine doves’ response toward the song of present (sympatric) or absent (allopatric) congeners suggests that the ability to discriminate between songs of similarly singing potential competitors is acquired through earlier interactions and learning. This plasticity in response supports the misdirected aggression hypothesis, which argues that interspecific territorialism emerges as a maladaptive by-product of signal similarity. However, on an evolutionary timescale, such an ability could be considered an adaptive cognitive tool useful for resolving competing interests with congeners.

A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city security

Ensuring the privacy and trustworthiness of smart city—Internet of Things (IoT) networks have recently remained the central problem. Cyborg intelligence is one of the most popular and advanced technologies suitable for securing smart city networks against cyber threats. Various machine learning and deep learning-based cyborg intelligence mechanisms have been developed to protect smart city networks by ensuring property, security, and privacy. However, it limits the critical problems of high time complexity, computational cost, difficulty to understand, and reduced level of security. Therefore, the proposed work intends to implement a group of novel methodologies for developing an effective Cyborg intelligence security model to secure smart city systems. Here, the Quantized Identical Data Imputation (QIDI) mechanism is implemented at first for data preprocessing and normalization. Then, the Conjugate Self-Organizing Migration (CSOM) optimization algorithm is deployed to select the most relevant features to train the classifier, which also supports increased detection accuracy. Moreover, the Reconciliate Multi-Agent Markov Learning (RMML) based classification algorithm is used to predict the intrusion with its appropriate classes. The original contribution of this work is to develop a novel Cyborg intelligence framework for protecting smart city networks from modern cyber-threats. In this system, a combination of unique and intelligent mechanisms are implemented to ensure the security of smart city networks. It includes QIDI for data filtering, CSOM for feature optimization and dimensionality reduction, and RMML for categorizing the type of intrusion. By using these methodologies, the overall attack detection performance and efficiency have been greatly increased in the proposed cyborg model. Here, the main reason of using CSOM methodology is to increase the learning speed and prediction performance of the classifier while detecting intrusions from the smart city networks. Moreover, the CSOM provides the optimized set of features for improving the training and testing operations of classifier with high accuracy and efficiency. Among other methodologies, the CSOM has the unique characteristics of increased searching efficiency, high convergence, and fast processing speed. During the evaluation, the different types of cyber-threat datasets are considered for testing and validation, and the results are compared with the recent state-of-the-art model approaches.

VBQ-Net: A Novel Vectorization-Based Boost Quantized Network Model for Maximizing the Security Level of IoT System to Prevent Intrusions

Data sharing with additional devices across wireless networks is made simple and advantageous by the Internet of Things (IoT), an emerging technology. However, IoT systems are more susceptible to cyberattacks because of their continued growth and technological advances, which could lead to powerful assaults. An intrusion detection system is one of the key defense mechanisms for information and communications technology. The primary shortcomings that plague current IoT security frameworks are their inability to detect intrusions properly, their substantial latency, and their prolonged processing time and delay. Therefore, this work develops a clever and innovative security architecture called Vectorization-Based Boost Quantized Network (VBQ-Net) for protecting IoT networks. Here, a Vector Space Bag of Words (VSBW) methodology is used to reduce the dimensionality of features and identify a key characteristic from the featured data. In addition, a brand-new classification technique, called Boosted Variance Quantization Neural Networks (BVQNNs), is used to classify the different types of intrusions using a weighted feature matrix. A Multi-Hunting Reptile Search Optimization (MH-RSO) algorithm is employed during categorization to calculate the probability value for selecting the right choices while anticipating intrusions. In this study, the most well-known and current datasets, such as IoTID-20, IoT-23, and CIDDS-001, are used to validate and evaluate the effectiveness of the proposed methodology. By evaluating the proposed approach on standard IoT datasets, the study seeks to address the limitations of current IoT security frameworks and provide a more effective defense mechanism against cyberattacks on IoT systems.

An improved long short term memory network for intrusion detection.

Over the years, intrusion detection system has played a crucial role in network security by discovering attacks from network traffics and generating an alarm signal to be sent to the security team. Machine learning methods, e.g., Support Vector Machine, K Nearest Neighbour, have been used in building intrusion detection systems but such systems still suffer from low accuracy and high false alarm rate. Deep learning models (e.g., Long Short-Term Memory, LSTM) have been employed in designing intrusion detection systems to address this issue. However, LSTM needs a high number of iterations to achieve high performance. In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. The performance of ILSTM and the intrusion detection system were evaluated using two public datasets (NSL-KDD dataset and LITNET-2020) under nine performance metrics. The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. The ILSTM achieved an accuracy of 93.09% and a precision of 96.86% while LSTM gave an accuracy of 82.74% and a precision of 76.49%. Also, the ILSTM performed better than LSTM in both datasets. In addition, the statistical analysis showed that ILSTM is more statistically significant than LSTM. Further, the proposed ISTLM gave better results of multiclassification of intrusion types such as DoS, Prob, and U2R attacks.

INVESTIGATION OF BACTERIAL AND VIRAL SLOW INTERVERTEBRAL DISC INFECTIONS

Objective. Currently, few publications have been available regarding an effect of level, type of intrusion, and viral infections on spinal disc degeneration. The study was aimed to evaluate an incidence of indolent bacterial and viral intervertebral disc infections in unstratified group of patients operated on for spinal degenerative pathologies.
 Material and methods. For the study, intraoperative disc samples were collected from patients who underwent discectomy and total spinal fusion surgery for degenerative pathologies between the years December, 2018 to January, 2020. 447 samples were examined obtained from 392 patients (189 women and 203 men) with a mean age of 58.1 years.
 The following epidemiological and clinical data were collected: gender, age, spinal segments affected, type of surgical approach, preoperative C-reactive protein (CRP; reference range 0.00.5 mg/dL), and preoperative as well as 6-month postoperative ODI and NDI, both expressed as a percentage. The incidence of postoperative infections within six months after surgery as well as MI for each operated segment was assessed. Only patients with ventral cervical or dorsal thoracic access were enrolled in the study. In the lumbar region, the ventral assess was chosen for anterior lumbar interbody fusion, the lateral approach for oblique or extreme lateral interbody fusion, and the dorsal approach in case of standard microdiscectomy of posterior transforaminal interbody fusion or transforaminal interbody fusion.
 A logistic regression analysis was performed to model a risk of positive microbiological culture and a risk of emerging modal changes by using a set of independent variables selected in a stepwise manner by using the Akaike information criterion as a measure of quality of select procedure.
 Results. MRI for modal analysis was available for all 447 segments. MRI revealed 45 type 1 SCs (10.07% of all segments), 118 type 2 SCs (26.4%), and 5 type 3 SCs (1.12%). Microbiological culture was available in 410 segments. In these 410 segments, 39 type 1 MCs, 113 type 2 MCs, and 5 type 3 MCs were found. A statistically significant relationship was found between SC type 2 and a positive microbiological result (p = 0.0127). In contrast, there were no statistically significant associations for type 1 and type 3 SCs (p=0.3052 and p=0.0767, respectively).
 Conclusion. A relation between disc degeneration and infections seems unlikely. Histopathological evidence of inflammation in degenerated discs is rare and unrelated to microbiological findings.

Traffic Load Learning Towards Early Detection of Intrusion in Industrial mMTC Networks

The fifth generation (5G) network offers a powerful infrastructure for Industrial IoT in that its support on massive machine-type communications (mMTC). Recently, grant-free (GF) access has been recognized as the promising new access approach for mMTC, however, it also introduces the potential risk. To timely discover intrusion, in this paper we propose a learning network by extracting traffic-load information from the states (success, collision, and idle) of access resources observed at media access control (MAC) and physical (PHY) layers. In particular, our learning network consists of three concatenated function modules, i.e., traffic-load estimation, traffic-load prediction, and intrusion identification. Moreover, our proposed learning network is able to identify two types of intrusion: false data dissemination and congestion attack. Simulation results indicate that the proposed scheme can effectively capture the number of active devices, provide reasonable prediction by using history records, and eventually, achieve more accurate detection compared with baseline approaches.

Geochemical Distribution of Platinum Metals, Gold and Silver in Intrusive Rocks of the Norilsk Region

The Norilsk ore district is one of the world leaders in the production of platinum metals. Long-term research focused on the detection of sulfide platinum-copper-nickel ores contributed to the accumulation of a large volume of scientific material on the geology and mineralization of the Norilsk area. Despite this, the issue of the composition of the initial melt for ore-bearing intrusive complexes and its degree of enrichment with noble metals remains open. Intrusive rocks of the Norilsk region are rarely analyzed for their ratio of noble metals. However, the analysis and comparison of geochemical parameters of different types of intrusions allows us to draw important conclusions not only about the composition of the initial magmas of ore-bearing complexes, but also about the formation conditions of the intrusions. This study demonstrates the distribution of platinum metals, gold and silver in the main petrographic differentiates of the Kharaelakh, Talnakh, Vologochan intrusions and Kruglogorsk-type intrusion. The regularities and variations of the distribution of metals depend on the host rocks. There are two series of rocks in the inner structure of the ore-bearing intrusions: 1. Picritic and taxitic gabbro-dolerites enriched in PGE-Au-Ag mineralization which forms disseminated ores at intrusion bottoms (ore-bearing rocks). 2. Olivine-, olivine-bearing, olivine-free gabbro-dolerites and leucogabbro with poor sulfide mineralization at the upper part of the intrusions (ore-free rocks). There is a distinct correlation between PGE, Cu, S and to a lesser extent correlation with Ni in the first rock group, which is a characteristic of sulfide PGE-Cu-Ni deposits. In the second group, correlations are also revealed, but the correlation coefficients are lower. The main element controlling the distribution of platinum metals is copper. The taxitic gabbro-dolerites of the Talnakh intrusion are the most enriched by noble metals. According to noble metal patterns the rocks of the Kharaelakh intrusion show the highest degree of melting of the initial mantle material during the formation of parental magmas chambers. Despite some differences, the geochemical features of the studied rocks indicate the similar characteristics of the accumulation of gold, silver and platinum metals in the intrusions of the Talnakh, Kruglogorsk and Zubovsk types, which allow suggesting the close conditions for the formation of ore mineralization of these intrusions.

Enhancement of an IoT hybrid intrusion detection system based on fog-to-cloud computing

Nowadays, with the proliferation of internet of things-connected devices, the scope of cyber-attacks on the internet of things has grown exponentially. So, it makes it a necessity to develop an efficient and accurate intrusion detection system that should be fast, dynamic, and scalable in an internet of things environment. On the other hand, Fog computing is a decentralized platform that extends Cloud computing to deal with the inherent issues of the Cloud computing. As well, maintaining a high level of security is critical in order to ensure secure and reliable communication between Fog nodes and internet of things devices. To address this issue, we present an intrusion detection method based on artificial neural networks and genetic algorithms to efficiently detect various types of network intrusions on local Fog nodes. Through this approach, we applied genetic algorithms to optimize the interconnecting weights of the network and the biases associated with each neuron. Therefore, it can quickly and effectively establish a back-propagation neural network model. Moreover, the distributed architecture of fog computing enables the distribution of the intrusion detection system over local Fog nodes with a centralized Cloud, which achieves faster attack detection than the Cloud intrusion detection mechanism. A set of experiments were conducted on the Raspberry Pi4 as a Fog node, based on the UNSW-NB15 and ToN_IoT data sets for binary-class classification, which showed that the optimized weights and biases achieved better performance than those who used the neural network without optimization. The optimized model showed interoperability, flexibility, and scalability. Furthermore, achieving a higher intrusion detection rate through decreasing the neural network error rate and increasing the true positive rate is also possible. According to the experiments, the suggested approach produces better outcomes in terms of detection accuracy and processing time. In this case, the proposed approach achieved an 16.35% and 37.07% reduction in execution time for both data sets, respectively, compared to other state-of-the-art methods, which enhanced the acceleration of the convergence process and saved processing power.

A Short Review of Intrusion Arches in Orthodontics

Intrusion is the tooth movement that occurs in apical direction and whose center of rotation lies at infinity. It is a common orthodontic treatment approach employed for managing orthodontic esthetic and functional problems, including gummy smile, open bite and deep bite. This review includes the types of dental intrusion, the biomechanics of intrusion in orthodontic treatment and various methods for achieving successful orthodontic intrusion without causing any detrimental effects to tooth.

Petrogenesis of Middle Triassic Adakite-like Intrusions in the Asiha Orogenic Gold Deposit, East Kunlun Orogenic Belt, China

The East Kunlun Orogenic Belt is considered as one of the important gold mineralization regions in the Tethys tectonic domain. These orogenic gold deposits are related to intermediate-acid intrusions formed at the end of Paleo-Tethys evolution, but the petrogenesis is controversial. This paper presents a new study on the geochemistry of zircon U-Pb, O, S, and Pb isotopic compositions of Asiha quartz diorite, granite porphyry, and sulfides. The geochemical features of quartz diorite and granite porphyry are consistent with the modern adakite, with high content of Sr but low content of Y, Yb, and MgO. Magmatic zircons from these two types of intrusion yielded U-Pb ages of 238.4 ± 1.4 Ma and 240 ± 1.7 Ma, respectively. The high O isotopic composition of Asiha complex may reflect that crust or crustal derivates were incorporated into the magmatic melt, and the Pb isotope characteristics indicates a lower crust origin. The δ34S values of pyrites range from 4.9‰ to 11.6‰. This study infers that the Asiha complex perhaps formed by partial melting of the Paleo-Tethys subducted oceanic crust with seafloor sediments and is markedly different from the traditional adakite. Asiha deposit is an orogenic gold deposit related to adakite-like rocks, which formed in Triassic in the East Kunlun Orogenic Belt.

Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures

Computer viruses, malicious, and other hostile attacks can affect a computer network. Intrusion detection is a key component of network security as an active defence technology. Traditional intrusion detection systems struggle with issues like poor accuracy, ineffective detection, a high percentage of false positives, and an inability to handle new types of intrusions. To address these issues, we propose a deep learning-based novel method to detect cybersecurity vulnerabilities and breaches in cyber-physical systems. The proposed framework contrasts the unsupervised and deep learning-based discriminative approaches. This paper presents a generative adversarial network to detect cyber threats in IoT-driven IICs networks. The results demonstrate a performance increase of approximately 95% to 97% in terms of accuracy, reliability, and efficiency in detecting all types of attacks with a dropout value of 0.2 and an epoch value of 25. The output of well-known state-of-the-art DL classifiers achieved the highest true rate (TNR) and highest detection rate (HDR) when detecting the following attacks: (BruteForceXXS, BruteForceWEB, DoS_Hulk_Attack, and DOS_LOIC_HTTP_Attack) on the NSL-KDD, KDDCup99, and UNSW-NB15 datasets. It also maintained the confidentiality and integrity of users’ and systems’ sensitive information during the training and testing phases.

Summer Anticyclonic Eddies Carrying Kuroshio Waters Observed by a Large CPIES Array West of the Luzon Strait

Abstract The Kuroshio intrusion into the South China Sea (SCS) in summer is weak and has rarely been reported by in situ observations. Here, we describe a new form of Kuroshio water intrusion that is strongest during the summer, the North Luzon Warm Eddy (NLWE), which is an anticyclonic eddy originating north of Luzon Island. From early July to mid-September 2018, two NLWEs moving northwestward were captured by a mooring array consisting of 27 current- and pressure-recording inverted echo sounders (CPIESs). The three-dimensional CPIES estimates reveal that the NLWEs carried large amounts of saline Kuroshio waters (S > 34.7 psu) in the subsurface, which was also evidenced by Argo float profiles. The Kuroshio intrusion was confined to waters shallower than the 14.8°C isotherm. Historical data for NLWEs suggest that they occur mostly during the summer but are absent between November and March, which is attributed to seasonal wind stress curl (WSC). However, because the seasonal signal of WSC during summer is small, intraseasonal WSC is directly responsible for the generation of NLWEs. Significance Statement This paper describes a new type of Kuroshio water intrusion into the South China Sea (SCS)—the North Luzon Warm Eddy (NLWE), which is an anticyclonic eddy generated north of Luzon Island. The eddy mostly occurs during summer when the Kuroshio intrusion is commonly considered the weakest. From observations of a large CPIES array, we provide a cradle-to-grave picture of the NLWE. NLWEs are estimated to contribute almost half of the westward Luzon Strait transport during the summer and, as such, play an important role in the seasonal stratification and circulation in the northeastern SCS.

A Lightweight Deep Autoencoder Scheme for Cyberattack Detection in the Internet of Things

The Internet of things (IoT) is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision. Despite several advantages, the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals. A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries. To overcome the security challenges of IoT networks, this article proposes a lightweight deep autoencoder (DAE) based cyberattack detection framework. The proposed approach learns the normal and anomalous data patterns to identify the various types of network intrusions. The most significant feature of the proposed technique is its lower complexity which is attained by reducing the number of operations. To optimally train the proposed DAE, a range of hyperparameters was determined through extensive experiments that ensure higher attack detection accuracy. The efficacy of the suggested framework is evaluated via two standard and open-source datasets. The proposed DAE achieved the accuracies of 98.86%, and 98.26% for NSL-KDD, 99.32%, and 98.79% for the UNSW-NB15 dataset in binary class and multi-class scenarios. The performance of the suggested attack detection framework is also compared with several state-of-the-art intrusion detection schemes. Experimental outcomes proved the promising performance of the proposed scheme for cyberattack detection in IoT networks.