KDD Cup Research Articles

MTC-NET: A Multi-Channel Independent Anomaly Detection Method for Network Traffic.

In recent years, deep learning-based approaches, particularly those leveraging the Transformer architecture, have garnered widespread attention for network traffic anomaly detection. However, when dealing with noisy data sets, directly inputting network traffic sequences into Transformer networks often significantly degrades detection performance due to interference and noise across dimensions. In this paper, we propose a novel multi-channel network traffic anomaly detection model, MTC-Net, which reduces computational complexity and enhances the model's ability to capture long-distance dependencies. This is achieved by decomposing network traffic sequences into multiple unidimensional time sequences and introducing a patch-based strategy that enables each sub-sequence to retain local semantic information. A backbone network combining Transformer and CNN is employed to capture complex patterns, with information from all channels being fused at the final classification header in order to achieve modelling and detection of complex network traffic patterns. The experimental results demonstrate that MTC-Net outperforms existing state-of-the-art methods in several evaluation metrics, including accuracy, precision, recall, and F1 score, on four publicly available data sets: KDD Cup 99, NSL-KDD, UNSW-NB15, and CIC-IDS2017.

Strengthen Method Attacks Detection in Network using VFDT Algorithm

With the tremendous progress in organizing data over networks, network security has become more important than ever in modern technologies. Hacking represents a serious security risk in the climate of data transmission and reception. Sometimes there are new types of intrusions that are constantly evolving and detection systems have a difficult problem in identifying them. In this research, we proposed Organization based Abnormality Discovery Framework (OADF) system based on training an algorithm VFDT to detect attacks detection (AD).Exploratory outcomes on the kdd cup'99 data mining list show that our system is profoundly material to arrange interruption acknowledgment. It has been shown that the proposed methodology performs better as far as acknowledgment rate, deceiving issue rate, and computational time contrasted with various techniques. To enhance the success of this work, we used other type of previously prepared test groups, it ssenet-2011 dataset, and we trained these two types separately for the purpose of reaching optimal training. The purpose of this work is to discover the largest number of attacks or penetrations that occur on the internet. Exceptionally very fast decision tree (VFDT) algorithm was utilized to build the classifier for attacks. Exceptionally VFDT algorithm is one of data mining algorithms that arrangement with high data streams in an extremely brief time frame. Trial results exhibited that the proposed Organization based Abnormality Discovery Framework (OADF) system is exceptionally effective in detecting known and obscure assaults by 93%.

Improving Network Security with Gradient Boosting from KDD Cup Dataset

Improving Network Security with Gradient Boosting from KDD Cup Dataset

A graph attention network with spatio-temporal wind propagation graph for wind power ramp events prediction

The increasing penetration rate of wind power underscores the necessity for accurate forecasting and alerting of wind power ramp events (WPREs), given the unpredictable nature of wind. This article presents a novel approach to predicting WPREs, emphasizing the complexities of wind propagation across multiple locations, in contrast to traditional single-site analyses. By integrating a wind propagation graph into a Graph Attention Network (GAT), the prediction of ramp events is significantly enhanced. The efficacy of this approach is validated through comprehensive case studies utilizing the Spatial Dynamic Wind Power Forecasting (SDWPF) dataset from the Baidu KDD Cup 2022 and the WIND toolkit dataset from NREL, demonstrating superior results at both the wind turbine and wind farm scales.

Optimization of Intrusion Detection with Deep Learning: A Study Based on the KDD Cup 99 Database

Optimization of Intrusion Detection with Deep Learning: A Study Based on the KDD Cup 99 Database

A two stage lightweight approach for intrusion detection in Internet of Things

The prevalence of the Internet of Things (IoT) in people’s life has led to a significant rise in the amounts of breaches against different IoT devices. Given that IoT devices are typically installed in settings with constrained resources and limited processing power, and malicious traffic samples are difficult to collect, resulting in traditional deep learning algorithms that do not handle intrusion detection in IoT network well. In this work, we present a two-stage lightweight intrusion detection model based on self-supervised contrastive learning and self-knowledge distillation to reduce model’s over-reliance on labels, optimize the model’s capacity for generalization and significantly increase the IoT network’s intrusion detection speed while lowering the model’s complexity. The representations of the network traffic are extracted in the first step using self-supervised contrastive learning, and the representations learnt by the teacher model are transferred to the student model in the second stage using self-knowledge distillation. On the benchmark datasets KDD CUP99, NSL-KDD, UNSW-NB15, CIC IDS2017 and BoT-IoT, we carried out in-depth studies, and the experimental results demonstrate that our proposed model outperforms recent state-of-the-art models in addition to achieving superior intrusion detection capabilities. The code will be released at https://github.com/RicardoMLi/CL-SKD.

VQCNN: variational quantum convolutional neural networks based on quantum filters and fully connected layers

Abstract Classical machine learning is more susceptible to adversarial examples due to its linear and non-robust nature, which results in a severe degradation of the recognition accuracy of classical machine learning models. Quantum techniques are shown to have a higher robustness advantage and are more resistant to attacks from adversarial examples than classical machine learning. Inspired by the robustness advantage of quantum computing and the feature extraction advantage of convolutional neural networks, this paper proposes a novel variational quantum convolutional neural network model (VQCNN), whose quantum fully connected layer consists of a combination of a quantum filter and a variational quantum neural network to increase the model’s adversarial robustness. The network intrusion detection model based on VQCNN is verified on KDD CUP99 and UNSW-NB datasets. The results show that under the attack of Fast Gradient Sign Method, the decline values of accuracy, precision, and recall rate of the intrusion detection model based on VQCNN are less than those of the other four models, and it has higher adversarial robustness.

Design of network security monitoring system based on K-means clustering algorithm

The Network Security Monitoring System (NSMS) can use Big Data (BD) and K-means DT (K-means with distance threshold) algorithms to automatically learn and identify abnormal patterns in the network, improving the accuracy of network threat detection. In this article, KDD Cup 1999 and NSL KDD were selected as NSMS for dataset analysis. Preprocess the data; Extract statistical information, time series information, and traffic distribution characteristics. Value device DT further classifies regular attacks, remote location (R2L) attacks, and user to root (U2R) permission attacks. The experimental results show that the hybrid intrusion detection algorithm based on K-means DT achieves a network attack detection accuracy of 99.2% and a network attack detection accuracy of 98.9% on the NSL-KDD dataset. Hybrid intrusion detection algorithms can effectively improve the accuracy of network intrusion detection (NID). The hybrid intrusion detection system proposed in this article performs well on different datasets and can effectively detect various types of network intrusion attacks, with better performance than other algorithms. The NSMS designed in this article can cope with constantly changing network threats.

A Systematic Analysis and Review on Intrusion Detection Systems Using Machine Learning and Deep Learning Algorithms

An intrusion detection system (IDS) is crucial for defending computer networks and systems from cyberattacks, unauthorized entry, and harmful activities. Machine learning (ML) and deep learning (DL) based IDS is a security solution that uses sophisticated algorithms to automatically identify and predict malicious activity occurring within a computer network. It improves the network's security and identification of threats using various algorithms to monitor network traffic patterns, spot anomalies, and discern between normal and abnormal behavior. This study reviews 60 papers on the topic of IDS that implement various ML and DL techniques. The most commonly used techniques are the support vector machine (SVM), decision tree (DT), random forest (RF), K nearest neighbors, gradient boosting(GB), Naïve-Bayes (NB), multilayer perceptron (MLP), artificial neural network, recurrent neural network, and convolutional neural network (CNN). These techniques are tested on four different datasets: KDD Cup, NSL-KDD, UNSW-NB15, and Kyoto. The experimentation showed that, among ML algorithms, the DT classifier has the best average training time of 2.8 s, the best average testing time of 0.08 s, but achieved an average accuracy of 97.46% across all datasets. On the other hand, the NB classifier is easier to implement but took an average of 4.40 s in training time, 1.28 s in testing time, and has the least average accuracy of 74.22% across all datasets. The more sophisticated techniques such as SVM, MLP, GB, and CNN are time consuming with CNN taking the highest time in three out of four datasets. The RF algorithm achieved an average accuracy of 99.51%, the highest level of accuracy among all algorithms. In this way, a comprehensive analysis of the strengths and weaknesses of various ML and DL algorithms for IDS is presented.

SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting over a Large Turbine Array

Wind power is a clean and renewable energy, yet it poses integration challenges to the grid due to its variable nature. Thus, Wind Power Forecasting (WPF) is crucial for its successful integration. However, existing WPF datasets often cover only a limited number of turbines and lack detailed information. To bridge this gap and advance WPF research, we introduce the Spatial Dynamic Wind Power Forecasting dataset (SDWPF). The SDWPF dataset not only provides information on power generation and wind speed but also details the spatial distribution of the wind turbines and dynamic contextual factors specific to each turbine. These factors include weather information and the internal status of each wind turbine, thereby enriching the dataset and improving its applicability for predictive analysis. Further leveraging the potential of SDWPF, we initiated the ACM KDD Cup 2022, a competition distinguished as the foremost annual event in data mining, renowned for presenting cutting-edge challenges and attracting top talent from academia and industry. Our event successfully draws registrations from over 2400 teams around the globe.

Intrusion detection and secure data storage in the cloud were recommend by a multiscale deep bidirectional gated recurrent neural network

Data communication security is developing very day with the creation of cloud computing. The imperative for robust data communication security has become increasingly evident with the pervasive adoption of cloud computing. However, challenges persist due to the inherent complexities and limited security measures in cloud environments, particularly in transmitting and storing sensitive information. Previous studies have underscored the intricate nature of intrusion detection systems (IDS) in cloud-based settings. In this research, a Multi scale Deep Bidirectional Gated Recurrent Neural Network and Optimal Encryption Scheme espoused Intrusion Detection and Secure Data Storage in the Cloud (MDBGRNN-ID-SCESOA) is proposed. Leveraging the KDD CUP 99 dataset and DS2OS Dataset, initial data preprocessing involves Domain Transform Filtering (DTF) for tokenization, dimension reduction, and semantic analysis. Subsequently, MDBGRNN is employed to discern intrusion from non-intrusion data. Furthermore, a two-way encryption mechanism, integrating Elliptical Curve Cryptography (ECC) with the Sine Cosine Egret Swarm Optimization Algorithm (ECC-SCESOA), enhances data security while minimizing computational overhead. To safeguard encrypted data at rest in the cloud, a steganography process is devised, effectively concealing sensitive content. Performance evaluation metrics, including accuracy, specificity, sensitivity, encryption/decryption time, execution time, memory usage, and Matthews correlation coefficient (MCC), demonstrate the efficacy of MDBGRNN-ID-SCESOA. Comparative analysis with existing techniques reveals notable enhancements in computational efficiency and data security. This comprehensive approach addresses critical security concerns in cloud computing, offering a promising avenue for safeguarding sensitive data in cloud environments.

Secure Your Network: Building a Hybrid Intrusion Detection System in Java

Abstract: To protect the system from different types of intrusions, an intrusion detection system ID is required. It is essential to analyse the communication in order to classify the material as malicious or helpful. The usage of intrusion detection systems for cyber security shouldn't add to the processing time required for classification. These days, classification algorithms are utilized in conjunction with machine learning approaches to identify harmful data or intrusions. KDD cup 99 is the data set that was used in the experiment. Hybrid classification models can be used to adjust the performance of individual classification methods. This model blends rule-based algorithms with categorization techniques. An additional degree of security is provided by the combination of machine and human intelligence in classification. Precision, recall, F-Measure, and Mean Age Precision are used to validate an algorithm. The algorithm has a 92.35 percent accuracy rate. Even after merging our human-written criteria with traditional machine learning classification techniques, the model's accuracy is still considered satisfactory. However, there is still room for improvement and a more specific classification of the attack

A Comparative Analysis of Intrusion Detection Systems: Leveraging Classification Algorithms and Feature Selection Techniques

With the increasing use of the Internet and its coverage of all areas of life and the increasing amount of sensitive and confidential information on the Internet, the number of malicious attacks on that information has increased with the aim of destroying, changing, or misusing it. Consequently, the need to discover and prevent these kinds of attacks has increased in order to maintain privacy, reliability, and even availability. For this purpose, intelligent systems have been developed to detect these attacks, which are called Intrusion Detection System (IDS). These systems were tested and applied to special benchmark datasets that contain a large number of features and a massive number of observations. However, not all the features are important, and some are not relevant. Therefore, applying feature selection techniques becomes crucial, which select the features with the most importance and relevance in order to enhance the performance of the classification model. The aim of this review paper is to conduct a comparative analysis of various state-of-the-art IDS that use algorithm classifications to detect network attacks with the cooperation of feature selection techniques that have been applied to various well-known IDS datasets, such as KDD cup99, NSL-KDD, etc. This comparison is based on several factors, including the utilized classification technique, feature selection used, employed evaluation metrics, datasets used, and finally the highest accuracy rate obtained by each study.

Reinventing Web Security: An Enhanced Cycle-Consistent Generative Adversarial Network Approach to Intrusion Detection

Web3.0, as the link between the physical and digital domains, faces increasing security threats due to its inherent complexity and openness. Traditional intrusion detection systems (IDSs) encounter formidable challenges in grappling with the multidimensional and nonlinear traffic data characteristic of the Web3.0 environment. Such challenges include insufficient samples of attack data, inadequate feature extraction, and resultant inaccuracies in model classification. Moreover, the scarcity of certain traffic data available for analysis by IDSs impedes the system’s capacity to document instances of malicious behavior. In response to these exigencies, this paper presents a novel approach to Web3.0 intrusion detection, predicated on the utilization of cycle-consistent generative adversarial networks (CycleGANs). Leveraging the data transformation capabilities of its generator, this method facilitates bidirectional conversion between normal Web3.0 behavioral data and potentially intrusive behavioral data. This transformative process not only augments the diversity and volume of recorded intrusive behaviors but also clandestinely simulates various attack scenarios. Furthermore, through fostering mutual competition and learning between the discriminator and generator, the approach enhances the ability to discern the defining characteristics of potential intrusive behaviors, thereby bolstering the accuracy of intrusion detection. To substantiate the efficacy of the CycleGAN-based intrusion detection method, simulation experiments were conducted utilizing public datasets, including KDD CUP 1999 (KDD), CIC-DDOS2019, CIC-IDS2018, and SR-BH 2020. The experimental findings evince the method’s remarkable accuracies across the four datasets, attaining rates of 99.81%, 97.79%, 89.25%, and 95.15%, respectively, while concurrently maintaining low false-positive rates. This research contributes novel insights and methodologies toward the advancement of Web3.0 intrusion detection through the application of CycleGAN technology, which is poised to play a pivotal role in fortifying the security landscape of Web3.0.

A ROBUST DETECTION OF CYBER INCIDENTS UTILIZING MACHINE LEARNING TECHNIQUES

A reliable Cyber Attack Detection Model (CADM) is a system that works as safeguard for the users of modern technological devices and assistant for the operators of networks. The research paper aims to develop a CADM for analyzing the network data patterns to classify cyber-attacks. CADM finds out attack wise detection accuracy using ensemble classification method. LASSO has been used to extract important features. It can work with large datasets, and it has more visualization capability. Gradient Boosting and Random Forest algorithms have been used for classification of network traffic data to build an ensemble method. Gradient Boosting algorithm trains weak learning models and select the best decision trees to deliver more improved prediction accuracy and Random Forest algorithm trains each tree in parallel manner. In this research work, Jive datasets such as NSL-KDD, KDD Cup 99, UNSWNB15, URL 2016 and CICIDS 2017 are also applied to check the efficiency of the proposed model.

A ROBUST DETECTION OF CYBER INCIDENTS UTILIZING MACHINE LEARNING TECHNIQUES

A reliable Cyber Attack Detection Model (CADM) is a system that works as safeguard for the users of modern technological devices and assistant for the operators of networks. The research paper aims to develop a CADM for analyzing the network data patterns to classify cyber-attacks. CADM finds out attack wise detection accuracy using ensemble classification method. LASSO has been used to extract important features. It can work with large datasets, and it has more visualization capability. Gradient Boosting and Random Forest algorithms have been used for classification of network traffic data to build an ensemble method. Gradient Boosting algorithm trains weak learning models and select the best decision trees to deliver more improved prediction accuracy and Random Forest algorithm trains each tree in parallel manner. In this research work, Jive datasets such as NSL-KDD, KDD Cup 99, UNSWNB15, URL 2016 and CICIDS 2017 are also applied to check the efficiency of the proposed model.

Time‐based DDoS attack detection through hybrid LSTM‐CNN model architectures: An investigation of many‐to‐one and many‐to‐many approaches

SummaryInternet data thefts, intrusions and DDoS attacks are some of the big concerns for the network security today. Detection of these anomalies, is gaining tremendous impetus with the development of machine learning and artificial intelligence. Even now researchers are shifting the base from machine learning to the deep neural architectures with auto‐feature selection capabilities. We in this paper propose multiple deep neural network architectures which can select, co‐learn and teach the gradients of the neural network by itself with no human intervention. This is what we call as meta‐learning. The models are configured in both many to one and many to many design architectures. We combine long short‐term memory (LSTM), bi‐directional long short‐term memory (BiLSTM), convolutional neural network (CNN) layers along with attention mechanism to achieve the higher accuracy values among all the available deep learning model architectures. LSTMs overcomes the vanishing and exploding gradient problem of RNN and attention mechanism mimics the human cognitive attention that screens the network flow to obtain the key features for network traffic classification. In addition, we also add multiple convolutional layers to get the key features for network traffic classification. We get the time series analysis of the traffic done for the possibility of a DDoS attack without using any feature selection techniques and without balancing the dataset. The performance analysis is done based on confusion matrix scores, that is, accuracy, false alarm rate (FAR), sensitivity, specificity, false‐positive rate (FPR), F1 score, area under curve (AUC) analysis and loss functions on well‐known public benchmark KDD Cup'99 data set. The results of our experiments reveal that our models outperform existing techniques, showing their superiority in performance.

Leveraging of Gradient Boosting Algorithm in Misuse Intrusion Detection using KDD Cup 99 Dataset

This study addresses the persistent challenge of intrusion detection as a long-term cybersecurity issue. Investigating the efficacy of machine learning algorithms in anomaly and misuse detection. Research employs supervised learning for misuse detection and explain anomaly detection. Focused on adaptability and continual evolution the study explores the application of ensemble learning models AdaBoost, LightGBM, and XGBoost. Applying these algorithms in the context of intrusion detection. Utilizing the KDD Cup 99 dataset as a benchmark the paper assesses and compares the performance of these models. Besides, illuminating their effectiveness particularly in identifying smurf attacks within the cybersecurity landscape.

Ultra-short term prediction of wind power based on multi-resolution closed-loop method

In order to improve the accuracy and stability of wind power prediction, a multi-resolution closed-loop wind power ultra-short term prediction method is proposed. Using historical data with different resolutions, the differential data is generated by the pre-predictor with low, medium and high resolutions, and then the differential data is combined with the post-prediction data into the post-predictor with three resolutions for training. Since the differential data is obtained from the prediction data with different resolutions, the post-prediction data containing the differential data contains inconsistent information between different resolutions, so that the post-predictor receives the inconsistency between different resolutions and minimizes the inconsistency after several closed-loop iterations, and finally outputs wind power prediction with different resolutions. At the end of the paper, simulation experiments are carried out using the data of the 2022 KDD Cup competition, and comparison is made with some mainstream wind power prediction models. It is found that the proposed model has high accuracy and stability.

An evolving learning style detection approach for online education using bipartite graph embedding

The increasingly widespread online education recently enabled a new form of teaching, learning, and overall educational outcome. However, the need of personalization is required as learners learn differently and learning follows a one-size-fits-all approach. Learning style is the most used personalization that plays an important role in learning. This learning style is a changeable trait that is influenced by the learner's behavior based on past experiences. As a result, knowing it over time helps to point learners in the right direction, motivate them, and enhance their learning outcomes. Research done so far does not take into account the changes in the behavior of learners, and the behavioral data is at large volume, making the existing approaches fail to capture and extract the behavior of learners efficiently. Inspired by these constraints, we propose an incremental learning style detection approach for online education with a bipartite graph embedding technique. We first construct a dynamic bipartite graph to represent the incremental interaction between learners and learning resources while learning. Then, introduce a dynamic bipartite graph embedding to learn the low dimensional representation of the constructed graph from the current and previous time. Finally, the low dimensional features are mapped to the selected Felder-Silverman learning style model (FSLSM) dimension each time to identify and group similar learners using the k-means clustering algorithm. The proposed approach can be integrated into different educational systems. Extensive experiments conducted using three datasets from 2015 KDD Cup courses demonstrate the effectiveness of our approach. Average of 93.19%, 95.76%, and 98.48% are achieved across the three datasets in accuracy compared to the existing approaches.