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1.
入侵检测系统有效检测入侵的关键之一在于入侵规则库,本文针对普通入侵检测系统的缺点,将规则发现过程与数据挖掘技术结合起来,提出了改进的关联规则和聚类算法用于实时构建入侵检测系统规则库。 相似文献
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入侵检测技术是通过对计算机网络和主机系统中的关键信息进行实时采集和分析,从而判断出非法用户入侵和合法用户滥用资源的行为,并做出适当反应的网络安全技术,是继数据加密、防火墙等措施之后的又一种安全措施,随着计算机网络技术的不断发展,需要分析的数据急剧膨胀,如何提高检测的效率成为当务之急,而数据挖掘正是解决此问题的一剂良药,首先介绍入侵检测和数据挖掘的相关概念,接着分析采用数据挖掘技术的入侵检测系统的优势和缺点,最后提出一些改进的方向。 相似文献
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随着网络应用的开展,安全问题成为日常工作中的重要课题。针对入侵检测技术的发展趋势,融合主流数据挖掘技术,提出相应的入侵检测系统模型,实现数据预处理,检测、告警信息等功能并且达到降低维护成本和管理的目标。 相似文献
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基于数据挖掘技术,针对当前入侵检测系统的不足,把层次聚类算法与模糊c-均值算法相结合,设计出一种较优的入侵检测系统,实验证明该系统具有较高的检测率和良好的自适性。 相似文献
5.
阐述数据挖掘的技术及入侵检测系统基本原理和系统结构,介绍当前基于数据挖掘的入侵检测系统研究现状及其成果,最后介绍系统还有哪些不足之处。 相似文献
6.
数据挖掘模型在入侵检测系统中的应用 总被引:1,自引:0,他引:1
提出一种具有自学习功能的数据挖掘模型,可发现已知和未知的入侵和异常入侵恬动,基于数据挖掘的关联入侵规则生成算法使系统具有很强的常规入侵检测和协同入侵检测能力。 相似文献
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入侵检测系统是保障网络信息安全的重要手段,针对现有的入侵检测技术存在的不足.提出了基于机器学习的入侵检测系统的实现方案.简要介绍几种适合用于入侵检测系统中的机器学习算法,重点阐述基于神经网络、数据挖掘和人工免疫技术的入侵检测系统的性能特点. 相似文献
9.
本文简单介绍了智能入侵检测技术,主要包括神经网络技术,计算机免疫学,数据挖掘技术,状态转换分析,信息抽取,专家系统,基于多智能体的检测技术等等,以及智能入侵检测技术的发展趋势。 相似文献
10.
随着学生信息数据的急剧膨胀,为了更好的进行学生信息管理,提出基于数据挖掘技术的学生信息系统的开发,并举例说明如何利用数据挖掘技术和数据库技术建立学生信息管理系统,为相关专业人员提供参考。 相似文献
11.
Nasir Mahmood Yaser Hafeez Khalid Iqbal Shariq Hussain Muhammad Aqib Muhammad Jamal Oh-Young Song 《计算机、材料和连续体(英文)》2021,69(1):873-893
Despite advances in technological complexity and efforts, software repository maintenance requires reusing the data to reduce the effort and complexity. However, increasing ambiguity, irrelevance, and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories. Thus, there is a need for a repository mining technique for relevant and bug-free data prediction. This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software. To predict errors in mining data, the Apriori algorithm was used to discover association rules by fixing confidence at more than 40% and support at least 30%. The pruning strategy was adopted based on evaluation measures. Next, the rules were extracted from three projects of different domains; the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values. To evaluate the proposed approach, we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects. The evaluation showed that the results of our proposal are promising. Practitioners and developers can utilize these rules for defect prediction during early software development. 相似文献
12.
Kashif Iqbal Sagheer Abbas Muhammad Adnan Khan Atifa Athar Muhammad Saleem Khan Areej Fatima Gulzar Ahmad 《计算机、材料和连续体(英文)》2021,66(2):1595-1613
The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks. 相似文献
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Raniyah Wazirali 《计算机、材料和连续体(英文)》2021,67(2):1429-1445
Intrusion detection system (IDS) techniques are used in cybersecurity to protect and safeguard sensitive assets. The increasing network security risks can be mitigated by implementing effective IDS methods as a defense mechanism. The proposed research presents an IDS model based on the methodology of the adaptive fuzzy k-nearest neighbor (FKNN) algorithm. Using this method, two parameters, i.e., the neighborhood size (k) and fuzzy strength parameter (m) were characterized by implementing the particle swarm optimization (PSO). In addition to being used for FKNN parametric optimization, PSO is also used for selecting the conditional feature subsets for detection. To proficiently regulate the indigenous and comprehensive search skill of the PSO approach, two control parameters containing the time-varying inertia weight (TVIW) and time-varying acceleration coefficients (TVAC) were applied to the system. In addition, continuous and binary PSO algorithms were both executed on a multi-core platform. The proposed IDS model was compared with other state-of-the-art classifiers. The results of the proposed methodology are superior to the rest of the techniques in terms of the classification accuracy, precision, recall, and f-score. The results showed that the proposed methods gave the highest performance scores compared to the other conventional algorithms in detecting all the attack types in two datasets. Moreover, the proposed method was able to obtain a large number of true positives and negatives, with minimal number of false positives and negatives. 相似文献
15.
Machine learning (ML) algorithms are often used to design effective intrusion detection (ID) systems for appropriate mitigation and effective detection of malicious cyber threats at the host and network levels. However, cybersecurity attacks are still increasing. An ID system can play a vital role in detecting such threats. Existing ID systems are unable to detect malicious threats, primarily because they adopt approaches that are based on traditional ML techniques, which are less concerned with the accurate classification and feature selection. Thus, developing an accurate and intelligent ID system is a priority. The main objective of this study was to develop a hybrid intelligent intrusion detection system (HIIDS) to learn crucial features representation efficiently and automatically from massive unlabeled raw network traffic data. Many ID datasets are publicly available to the cybersecurity research community. As such, we used a spark MLlib (machine learning library)-based robust classifier, such as logistic regression (LR), extreme gradient boosting (XGB) was used for anomaly detection, and a state-of-the-art DL, such as a long short-term memory autoencoder (LSTMAE) for misuse attack was used to develop an efficient and HIIDS to detect and classify unpredictable attacks. Our approach utilized LSTM to detect temporal features and an AE to more efficiently detect global features. Therefore, to evaluate the efficacy of our proposed approach, experiments were conducted on a publicly existing dataset, the contemporary real-life ISCX-UNB dataset. The simulation results demonstrate that our proposed spark MLlib and LSTMAE-based HIIDS significantly outperformed existing ID approaches, achieving a high accuracy rate of up to 97.52% for the ISCX-UNB dataset respectively 10-fold cross-validation test. It is quite promising to use our proposed HIIDS in real-world circumstances on a large-scale. 相似文献
16.
Mavra Mehmood Talha Javed Jamel Nebhen Sidra Abbas Rabia Abid Giridhar Reddy Bojja Muhammad Rizwan 《计算机、材料和连续体(英文)》2022,70(1):91-107
Due to the widespread use of the internet and smart devices, various attacks like intrusion, zero-day, Malware, and security breaches are a constant threat to any organization's network infrastructure. Thus, a Network Intrusion Detection System (NIDS) is required to detect attacks in network traffic. This paper proposes a new hybrid method for intrusion detection and attack categorization. The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization. In the first step, the dataset is preprocessed through the data transformation technique and min-max method. Secondly, the random forest recursive feature elimination method is applied to identify optimal features that positively impact the model's performance. Next, we use various Support Vector Machine (SVM) types to detect intrusion and the Adaptive Neuro-Fuzzy System (ANFIS) to categorize probe, U2R, R2U, and DDOS attacks. The validation of the proposed method is calculated through Fine Gaussian SVM (FGSVM), which is 99.3% for the binary class. Mean Square Error (MSE) is reported as 0.084964 for training data, 0.0855203 for testing, and 0.084964 to validate multiclass categorization. 相似文献
17.
With the advancement of network communication technology, network traffic shows explosive growth. Consequently, network attacks occur frequently. Network intrusion detection systems are still the primary means of detecting attacks. However, two challenges continue to stymie the development of a viable network intrusion detection system: imbalanced training data and new undiscovered attacks. Therefore, this study proposes a unique deep learning-based intrusion detection method. We use two independent in-memory autoencoders trained on regular network traffic and attacks to capture the dynamic relationship between traffic features in the presence of unbalanced training data. Then the original data is fed into the triplet network by forming a triplet with the data reconstructed from the two encoders to train. Finally, the distance relationship between the triples determines whether the traffic is an attack. In addition, to improve the accuracy of detecting unknown attacks, this research proposes an improved triplet loss function that is used to pull the distances of the same class closer while pushing the distances belonging to different classes farther in the learned feature space. The proposed approach’s effectiveness, stability, and significance are evaluated against advanced models on the Android Adware and General Malware Dataset (AAGM17), Knowledge Discovery and Data Mining Cup 1999 (KDDCUP99), Canadian Institute for Cybersecurity Group’s Intrusion Detection Evaluation Dataset (CICIDS2017), UNSW-NB15, Network Security Lab-Knowledge Discovery and Data Mining (NSL-KDD) datasets. The achieved results confirmed the superiority of the proposed method for the task of network intrusion detection. 相似文献
18.
There are two key issues in distributed intrusion detection system, that is, maintaining load balance of system and protecting data integrity. To address these issues, this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation. A data allocation strategy based on capacity and workload is introduced to achieve local load balance, and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster. Moreover, data integrity is protected by using session reassemble and session partitioning. The simulation results show that the new model enjoys favorable advantages such as good load balance, higher detection rate and detection efficiency. 相似文献
19.
Hangjun Zhou Guang Sun Sha Fu Xiaoping Fan Wangdong Jiang Shuting Hu Lingjiao Li 《计算机、材料和连续体(英文)》2020,64(2):1091-1105
Supply Chain Finance (SCF) is important for improving the effectiveness of
supply chain capital operations and reducing the overall management cost of a supply
chain. In recent years, with the deep integration of supply chain and Internet, Big Data,
Artificial Intelligence, Internet of Things, Blockchain, etc., the efficiency of supply chain
financial services can be greatly promoted through building more customized risk pricing
models and conducting more rigorous investment decision-making processes. However,
with the rapid development of new technologies, the SCF data has been massively
increased and new financial fraud behaviors or patterns are becoming more covertly
scattered among normal ones. The lack of enough capability to handle the big data
volumes and mitigate the financial frauds may lead to huge losses in supply chains. In
this article, a distributed approach of big data mining is proposed for financial fraud
detection in a supply chain, which implements the distributed deep learning model of
Convolutional Neural Network (CNN) on big data infrastructure of Apache Spark and
Hadoop to speed up the processing of the large dataset in parallel and reduce the
processing time significantly. By training and testing on the continually updated SCF
dataset, the approach can intelligently and automatically classify the massive data
samples and discover the fraudulent financing behaviors, so as to enhance the financial
fraud detection with high precision and recall rates, and reduce the losses of frauds in a
supply chain. 相似文献