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"Intrusion"
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Network anomaly detection : a machine learning perspective
\"This book discusses detection of anomalies in computer networks from a machine learning perspective. It introduces readers to how computer networks work and how they can be attacked by intruders in search of fame, fortune, or challenge. The reader will learn how one can look for patterns in captured network traffic data to look for anomalous patterns that may correspond to attempts at unauthorized intrusion. The reader will be given a technical and sophisticated description of such algorithms and their applications in the context of intrusion detection in networks\"-- Provided by publisher.
Book
Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset
Computer networks intrusion detection systems (IDSs) and intrusion prevention systems (IPSs) are critical aspects that contribute to the success of an organization. Over the past years, IDSs and IPSs using different approaches have been developed and implemented to ensure that computer networks within enterprises are secure, reliable and available. In this paper, we focus on IDSs that are built using machine learning (ML) techniques. IDSs based on ML methods are effective and accurate in detecting networks attacks. However, the performance of these systems decreases for high dimensional data spaces. Therefore, it is crucial to implement an appropriate feature extraction method that can prune some of the features that do not possess a great impact in the classification process. Moreover, many of the ML based IDSs suffer from an increase in false positive rate and a low detection accuracy when the models are trained on highly imbalanced datasets. In this paper, we present an analysis the UNSW-NB15 intrusion detection dataset that will be used for training and testing our models. Moreover, we apply a filter-based feature reduction technique using the XGBoost algorithm. We then implement the following ML approaches using the reduced feature space: Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Logistic Regression (LR), Artificial Neural Network (ANN) and Decision Tree (DT). In our experiments, we considered both the binary and multiclass classification configurations. The results demonstrated that the XGBoost-based feature selection method allows for methods such as the DT to increase its test accuracy from 88.13 to 90.85% for the binary classification scheme.
Journal Article
The worst 2020 saline water intrusion disaster of the past century in the Mekong Delta
Vietnam Mekong Delta (VMD), the country’s most important food basket, is constantly threatened by drought-infused salinity intrusion (SI). The SI disaster of 2020 is recognized as the worst in recent decades, hence inspiring this perspective article. The authors’ viewpoints on the disaster’s impacts and causes are presented. The arguments presented are mainly drawn from (i) up-to-date publications that report on the recent SI intensification in the VMD and (ii) the power spectral analysis results using water level data. We verified the intensifying SI in the VMD both in its frequency and magnitude and remarked on four of the key SI drivers: (i) upstream hydropower dams, (ii) land subsidence, (iii) the relative sea-level rise, and (iv) riverbed sand mining. Also, a non-exhaustive yet list of recommendable management implications to mitigate the negative effects of the SI is contributed. The mitigation measures must be realized at multiple scales, ranging from pursuing transboundary water diplomacy efforts to managing internal pressures via developing early warnings, restricting illegal sand mining activities, alleviating pressures on groundwater resources, and diversifying agriculture.
Journal Article
Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks
Intrusion in wireless sensor networks (WSNs) aims to degrade or even eliminating the capability of these networks to provide its functions. In this paper, an enhanced intrusion detection system (IDS) is proposed by using the modified binary grey wolf optimizer with support vector machine (GWOSVM-IDS). The GWOSVM-IDS used 3 wolves, 5 wolves and 7 wolves to find the best number of wolves. The proposed method aims to increase intrusion detection accuracy and detection rate and reduce processing time in the WSN environment through decrease false alarms rates, and the number of features resulted from the IDSs in the WSN environment. Indeed, the NSL KDD’99 dataset is used to demonstrate the performance of the proposed method and compare it with other existing methods. The proposed methods are evaluated in terms of accuracy, the number of features, execution time, false alarm rate, and detection rate. The results showed that the proposed GWOSVM-IDS with seven wolves overwhelms the other proposed and comparative algorithms.
Journal Article
The Invisible Flood
Saltwater intrusion is the leading edge of sea-level rise, preceding tidal inundation, but leaving its salty signature far inland. With climate change, saltwater is shifting landward into regions that previously have not experienced or adapted to salinity, leading to novel transitions in biogeochemistry, ecology, and human land uses. We explore these changes and their implications for climate adaptation in coastal ecosystems. Biogeochemical changes, including increases in ionic strength, sulfidation, and alkalinization, have cascading ecological consequences such as upland forest retreat, conversion of freshwater wetlands, nutrient mobilization, and declines in agricultural productivity. We explore the trade-offs among land management decisions in response to these changes and how public policy should shape socioecological transitions in the coastal zone. Understanding transitions resulting from saltwater intrusion—and how to manage them—is vital for promoting coastal resilience.
Journal Article
Zero-day attack detection: a systematic literature review
With the continuous increase in cyberattacks over the past few decades, the quest to develop a comprehensive, robust, and effective intrusion detection system (IDS) in the research community has gained traction. Many of the recently proposed solutions lack a holistic IDS approach due to explicitly relying on attack signature repositories, outdated datasets or the lack of considering zero-day (unknown) attacks while developing, training, or testing the machine learning (ML) or deep learning (DL)-based models. Overlooking these factors makes the proposed IDS less robust or practical in real-time environments. On the other hand, detecting zero-day attacks is a challenging subject, despite the many solutions proposed over the past many years. One of the goals of this systematic literature review (SLR) is to provide a research asset to future researchers on various methodologies, techniques, ML and DL algorithms that researchers used for the detection of zero-day attacks. The extensive literature review on the recent publications reveals exciting future research trends and challenges in this particular field. With all the advances in technology, the availability of large datasets, and the strong processing capabilities of DL algorithms, detecting a completely new or unknown attack remains an open research area. This SLR is an effort towards completing the gap in providing a single repository of finding ML and DL-based tools and techniques used by researchers for the detection of zero-day attacks.
Journal Article
Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction
Cybersecurity has emerged as a critical global concern. Intrusion Detection Systems (IDS) play a critical role in protecting interconnected networks by detecting malicious actors and activities. Machine Learning (ML)-based behavior analysis within the IDS has considerable potential for detecting dynamic cyber threats, identifying abnormalities, and identifying malicious conduct within the network. However, as the number of data grows, dimension reduction becomes an increasingly difficult task when training ML models. Addressing this, our paper introduces a novel ML-based network intrusion detection model that uses Random Oversampling (RO) to address data imbalance and Stacking Feature Embedding based on clustering results, as well as Principal Component Analysis (PCA) for dimension reduction and is specifically designed for large and imbalanced datasets. This model’s performance is carefully evaluated using three cutting-edge benchmark datasets: UNSW-NB15, CIC-IDS-2017, and CIC-IDS-2018. On the UNSW-NB15 dataset, our trials show that the RF and ET models achieve accuracy rates of 99.59% and 99.95%, respectively. Furthermore, using the CIC-IDS2017 dataset, DT, RF, and ET models reach 99.99% accuracy, while DT and RF models obtain 99.94% accuracy on CIC-IDS2018. These performance results continuously outperform the state-of-art, indicating significant progress in the field of network intrusion detection. This achievement demonstrates the efficacy of the suggested methodology, which can be used practically to accurately monitor and identify network traffic intrusions, thereby blocking possible threats.
Journal Article
A Deep Learning‐Based Data Assimilation Approach to Characterizing Coastal Aquifers Amid Non‐Linearity and Non‐Gaussianity Challenges
Seawater intrusion (SI) poses a substantial threat to water security in coastal regions, where numerical models play a pivotal role in supporting groundwater management and protection. However, the inherent heterogeneity of coastal aquifers introduces significant uncertainties into SI predictions, potentially diminishing their effectiveness in management decisions. Data assimilation (DA) offers a solution by integrating various types of observational data with the model to characterize heterogeneous coastal aquifers. Traditional DA techniques, like ensemble smoother using the Kalman formula (ESK) and Markov chain Monte Carlo, face challenges when confronted with the non‐linearity, non‐Gaussianity, and high‐dimensionality issues commonly encountered in aquifer characterization. In this study, we introduce a novel DA approach rooted in deep learning (DL), referred to as ESDL, aimed at effectively characterizing coastal aquifers with varying levels of heterogeneity. We systematically investigate a range of factors that impact the performance of ESDL, including the number and types of observations, the degree of aquifer heterogeneity, the structure and training options of the DL models. Our findings reveal that ESDL excels in characterizing heterogeneous aquifers under non‐linear and non‐Gaussian conditions. Comparison between ESDL and ESK under different experimentation settings underscores the robustness of ESDL. Conversely, in certain scenarios, ESK displays noticeable biases in the characterization results, especially when measurement data from non‐linear and discontinuous processes are used. To optimize the efficacy of ESDL, attention must be given to the design of the DL model and the selection of observational data, which are crucial to ensure the universal applicability of this DA method. Key Points Non‐linearity and non‐Gaussianity in coastal aquifer characterization problems pose challenges to traditional data assimilation (DA) methods We propose to address these issues with a deep learning‐based DA method called ESDL Various factors influencing the performance of ESDL are systematically investigated
Journal Article
A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions
With the increase in the usage of the Internet, a large amount of information is exchanged between different communicating devices. The data should be communicated securely between the communicating devices and therefore, network security is one of the dominant research areas for the current network scenario. Intrusion detection systems (IDSs) are therefore widely used along with other security mechanisms such as firewall and access control. Many research ideas have been proposed pertaining to the IDS using machine learning (ML) techniques, deep learning (DL) techniques, and swarm and evolutionary algorithms (SWEVO). These methods have been tested on the datasets such as DARPA, KDD CUP 99, and NSL-KDD using network features to classify attack types. This paper surveys the intrusion detection problem by considering algorithms from areas such as ML, DL, and SWEVO. The survey is a representative research work carried out in the field of IDS from the year 2008 to 2020. The paper focuses on the methods that have incorporated feature selection in their models for performance evaluation. The paper also discusses the different datasets of IDS and a detailed description of recent dataset CIC IDS-2017. The paper presents applications of IDS with challenges and potential future research directions. The study presented, can serve as a pedestal for research communities and novice researchers in the field of network security for understanding and developing efficient IDS models.
Journal Article
Machine Learning Based Intrusion Detection Systems for IoT Applications
Internet of Things (IoT) and its applications are the most popular research areas at present. The characteristics of IoT on one side make it easily applicable to real-life applications, whereas on the other side expose it to cyber threats. Denial of Service (DoS) is one of the most catastrophic attacks against IoT. In this paper, we investigate the prospects of using machine learning classification algorithms for securing IoT against DoS attacks. A comprehensive study is carried on the classifiers which can advance the development of anomaly-based intrusion detection systems (IDSs). Performance assessment of classifiers is done in terms of prominent metrics and validation methods. Popular datasets CIDDS-001, UNSW-NB15, and NSL-KDD are used for benchmarking classifiers. Friedman and Nemenyi tests are employed to analyze the significant differences among classifiers statistically. In addition, Raspberry Pi is used to evaluate the response time of classifiers on IoT specific hardware. We also discuss a methodology for selecting the best classifier as per application requirements. The main goals of this study are to motivate IoT security researchers for developing IDSs using ensemble learning, and suggesting appropriate methods for statistical assessment of classifier’s performance.
Journal Article