Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "intrusion detection" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-13 z 13
    Tytuł:
    Intrusion Detection Systems : Model and implementation of module reacting on intrusion to computer system
    Autorzy:
    Barczak, A.
    Bereza, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/92857.pdf
    Data publikacji:
    2010
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    intrusion detection
    vulnerability scanning
    intrusion detection system
    IDS architecture
    Opis:
    The problems of intrusion detection capabilities are considered in this paper. The general idea of structure, model of IDS (Intrusion Detection System) and overall construction is presented with emphasize many problems which appear while creating procedures of such a tool.
    Źródło:
    Studia Informatica : systems and information technology; 2010, 1-2(14); 5-11
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Methods for increasing security of web servers
    Autorzy:
    Nycz, M.
    Hajder, M.
    Nienajadlo, S
    Powiązania:
    https://bibliotekanauki.pl/articles/106196.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
    Tematy:
    security
    web server
    intrusion detection
    intrusion prevention
    Opis:
    This article is addressed in most part to people dealing with security of web servers. This paper begins with presenting the statistical dimension of the issue of data security in the modern Internet. This paper begins with presenting statistics dealing with issues of data security on the modern World Wide Web. The authors main focus in this work is presenting the challenges of dealing with security and protection of web communication. The work analyses the security of implementing SSL/TLS (Secure Socket Layer/Transport Layer Security) protocol and proposes a new method of increasing security of web servers. This article is addressed to people dealing with analysis and security of web servers.
    Źródło:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2016, 16, 2; 39-42
    1732-1360
    2083-3628
    Pojawia się w:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    New architecture of system intrusion detection and prevention
    Autorzy:
    Nycz, M.
    Hajder, M.
    Gerka, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/106214.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
    Tematy:
    intrusion detection
    network security
    IDS
    IPS
    Opis:
    In this article there has been presented new intrusion detection and prevention algorithm implemented on Raspberry Pi platform. The paper begins with the presentation of research methodology in the field of Intrusion Detection Systems. Adequate supervision and control over network traffic is crucial for the security of information and communication technology. As a result of the limited budget allocated for the IT infrastructure of small businesses and the high price of dedicated solutions, many companies do not use mentioned systems. Therefore, in this order, there has been proposed monitoring solution based on the generally available Raspberry Pi platform. The paper is addressed to network administrators.
    Źródło:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2016, 16, 2; 20-24
    1732-1360
    2083-3628
    Pojawia się w:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of the Complex Event Processing system for anomaly detection and network monitoring
    Autorzy:
    Frankowski, G.
    Jerzak, M.
    Miłostan, M.
    Nowak, T.
    Pawłowski, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/305323.pdf
    Data publikacji:
    2015
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    network monitoring
    intrusion detection
    anomaly detection
    complex event processing
    Opis:
    Protection of infrastructures for e-science, including grid environments and NREN facilities, requires the use of novel techniques for anomaly detection and network monitoring. The aim is to raise situational awareness and provide early warning capabilities. The main operational problem that most network operators face is integrating and processing data from multiple sensors and systems placed at critical points of the infrastructure. From a scientific point of view, there is a need for the efficient analysis of large data volumes and automatic reasoning while minimizing detection errors. In this article, we describe two approaches to Complex Event Processing used for network monitoring and anomaly detection and introduce the ongoing SECOR project (Sensor Data Correlation Engine for Attack Detection and Support of Decision Process), supported by examples and test results. The aim is to develop methodology that allows for the construction of next-generation IDS systems with artificial intelligence, capable of performing signature-less intrusion detection.
    Źródło:
    Computer Science; 2015, 16 (4); 351-371
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Anomaly Detection Framework Based on Matching Pursuit for Network Security Enhancement
    Autorzy:
    Renk, R.
    Hołubowicz, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/309519.pdf
    Data publikacji:
    2011
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    anomaly detection
    intrusion detection
    matching pursuit
    network security
    signal processing
    Opis:
    In this paper, a framework for recognizing network traffic in order to detect anomalies is proposed. We propose to combine and correlate parameters from different layers in order to detect 0-day attacks and reduce false positives. Moreover, we propose to combine statistical and signal-based features. The major contribution of this paper are: novel framework for network security based on the correlation approach as well as new signal based algorithm for intrusion detection using matching pursuit.
    Źródło:
    Journal of Telecommunications and Information Technology; 2011, 1; 32-36
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    On Efficiency of Selected Machine Learning Algorithms for Intrusion Detection in Software Defined Networks
    Autorzy:
    Jankowski, D.
    Amanowicz, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/963945.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    software defined network
    intrusion detection
    machine learning
    Mininet
    SDN
    Opis:
    We propose a concept of using Software Defined Network (SDN) technology and machine learning algorithms for monitoring and detection of malicious activities in the SDN data plane. The statistics and features of network traffic are generated by the native mechanisms of SDN technology.In order to conduct tests and a verification of the concept, it was necessary to obtain a set of network workload test data.We present virtual environment which enables generation of the SDN network traffic.The article examines the efficiency of selected machine learning methods: Self Organizing Maps and Learning Vector Quantization and their enhanced versions.The results are compared with other SDN-based IDS.
    Źródło:
    International Journal of Electronics and Telecommunications; 2016, 62, 3; 247-252
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Intrusion Detection in Heterogeneous Networks of Resource-Limited Things
    Autorzy:
    Kozakiewicz, A.
    Lasota, K.
    Marks, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/307880.pdf
    Data publikacji:
    2015
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    client honeypot
    internet of things (IoT)
    intrusion detection
    wireless sensor network
    Opis:
    The paper discusses the threats to networks of resource-limited things such as wireless sensors and the different mechanisms used to deal with them. A novel approach to threat detection is proposed. MOTHON is a movement-assisted threat detection system using mobility to enhance a global threat assessment and provide a separate physical secure channel to deliver collected information.
    Źródło:
    Journal of Telecommunications and Information Technology; 2015, 4; 10-14
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Battery Drain Denial-of-Service Attacks and Defenses in the Internet of Things
    Autorzy:
    Ioulianou, Philokypros P.
    Vassilakis, Vassilios G.
    Logothetis, Michael D.
    Powiązania:
    https://bibliotekanauki.pl/articles/308296.pdf
    Data publikacji:
    2019
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    battery drain
    ContikiOS
    Cooja simulator
    denial-of-service
    intrusion detection
    IoT
    RPL
    Opis:
    IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is a popular routing protocol used in wireless sensor networks and in the Internet of Things (IoT). RPL was standardized by the IETF in 2012 and has been designed for devices with limited resources and capabilities. Open-source RPL implementations are supported by popular IoT operating systems (OS), such as ContikiOS and TinyOS. In this work, we investigate the possibility of battery drain Denial-of-Service (DoS) attacks in the RPL implementation of ContikiOS. In particular, we use the popular Cooja simulator and implement two types of DoS attacks, particularly version number modification and “Hello” flooding. We demonstrate the impact of these attacks on the power consumption of IoT devices. Finally, we discuss potential defenses relying on distributed intrusion detection modules.
    Źródło:
    Journal of Telecommunications and Information Technology; 2019, 2; 37-45
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Enhancing Intrusion Detection in Industrial Internet of Things through Automated Preprocessing
    Autorzy:
    Sezgin, Anıl
    Boyacı, Aytuğ
    Powiązania:
    https://bibliotekanauki.pl/articles/2201911.pdf
    Data publikacji:
    2023
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    feature selection
    intrusion detection
    machine learning
    industrial internet of things
    Internet of things
    IIoT
    Opis:
    Industrial Internet of Things (IIoT) is a rapidly growing field, where interconnected devices and systems are used to improve operational efficiency and productivity. However, the extensive connectivity and data exchange in the IIoT environment make it vulnerable to cyberattacks. Intrusion detection systems (IDS) are used to monitor IIoT networks and identify potential security breaches. Feature selection is an essential step in the IDS process, as it can reduce computational complexity and improve the accuracy of the system. In this research paper, we propose a hybrid feature selection approach for intrusion detection in the IIoT environment using Shapley values and a genetic algorithm-based automated preprocessing technique which has three automated steps including imputation, scaling and feature selection. Shapley values are used to evaluate the importance of features, while the genetic algorithm-based automated preprocessing technique optimizes feature selection. We evaluate the proposed approach on a publicly available dataset and compare its performance with existing state-of-the-art methods. The experimental results demonstrate that the proposed approach outperforms existing methods, achieving high accuracy, precision, recall, and F1-score. The proposed approach has the potential to enhance the performance of IDS in the IIoT environment and improve the overall security of critical industrial systems.
    Źródło:
    Advances in Science and Technology. Research Journal; 2023, 17, 2; 120--135
    2299-8624
    Pojawia się w:
    Advances in Science and Technology. Research Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An octopus-inspired intrusion deterrence model in distributed computing system
    Autorzy:
    Olajubu, E. A.
    Akinwale, A.
    Ogundoyin, K. I.
    Powiązania:
    https://bibliotekanauki.pl/articles/305521.pdf
    Data publikacji:
    2016
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    CIDM
    MANET
    intrusion detection
    mobility
    routing load
    throughput
    1-hop neighbors
    wireless
    malicious
    injection
    Opis:
    The study formulated and evaluated a model for effective management of ma- licious nodes in mobile Ad-hoc network based on Ad-Hoc on- demand distance vector routing protocol. A collaborative injection model called Collaborative Injection Deterrence Model (CIDM) was formulated using stochastic theory. The definition of the model was presented using graph theory. CIDM was simulated using three different scenarios. The three scenarios were then compared using packets delivery ratio (PDR), routing load, throughput and delay as performance metrics. The simulation result showed that CIDM reduce considerably the rate of packets dropped caused by malicious nodes in MANET network. CIDM did not introduce additional load to the network and, yet produce higher throughput. Lastly, the access delay in CIDM is minimal compared with convectional OADV. The study developed a model to mete out a punitive measure to rogue nodes as a form of intrusion deterrence without degrading the overall performance of the network. The well known CRAWDAD dataset was used in the simulation.
    Źródło:
    Computer Science; 2016, 17 (4); 483-501
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An autoencoder-enhanced stacking neural network model for increasing the performance of intrusion detection
    Autorzy:
    Brunner, Csaba
    Kő, Andrea
    Fodor, Szabina
    Powiązania:
    https://bibliotekanauki.pl/articles/2147134.pdf
    Data publikacji:
    2022
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    intrusion detection
    neural network
    ensemble classifiers
    hyperparameter optimization
    sparse autoencoder
    NSL-KDD
    machine learning
    Opis:
    Security threats, among other intrusions affecting the availability, confidentiality and integrity of IT resources and services, are spreading fast and can cause serious harm to organizations. Intrusion detection has a key role in capturing intrusions. In particular, the application of machine learning methods in this area can enrich the intrusion detection efficiency. Various methods, such as pattern recognition from event logs, can be applied in intrusion detection. The main goal of our research is to present a possible intrusion detection approach using recent machine learning techniques. In this paper, we suggest and evaluate the usage of stacked ensembles consisting of neural network (SNN) and autoencoder (AE) models augmented with a tree-structured Parzen estimator hyperparameter optimization approach for intrusion detection. The main contribution of our work is the application of advanced hyperparameter optimization and stacked ensembles together. We conducted several experiments to check the effectiveness of our approach. We used the NSL-KDD dataset, a common benchmark dataset in intrusion detection, to train our models. The comparative results demonstrate that our proposed models can compete with and, in some cases, outperform existing models.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 2; 149--163
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Gramian angular field transformation-based intrusion detection
    Autorzy:
    Terzi, Duygu Sinanc
    Powiązania:
    https://bibliotekanauki.pl/articles/27312895.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    encoding intrusions as images
    convolutional neural networks
    Gramian angular fields
    intrusion detection
    network security
    Opis:
    Cyber threats are increasing progressively in their frequency, scale, sophistication, and cost. The advancement of such threats has raised the need to enhance intelligent intrusion-detection systems. In this study, a different perspective has been developed for intrusion detection. Gramian angular fields were adapted to encode network traffic data as images. Hereby, a way to reveal bilateral feature relationships and benefit from the visual interpretation capability of deep-learning methods has been opened. Then, image-encoded intrusions were classified as binary and multi-class using convolutional neural networks. The obtained results were compared to both conventional machine-learning methods and related studies. According to the results, the proposed approach surpassed the success of traditional methods and produced success rates that were close to the related studies. Despite the use of complex mechanisms such as feature extraction, feature selection, class balancing, virtual data generation, or ensemble classifiers in related studies, the proposed approach is fairly plain – involving only data-image conversion and classification. This shows the power of simply changing the problem space.
    Źródło:
    Computer Science; 2022, 23 (4); 571--585
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Markov Decision Process based Model for Performance Analysis an Intrusion Detection System in IoT Networks
    Autorzy:
    Kalnoor, Gauri
    Gowrishankar, -
    Powiązania:
    https://bibliotekanauki.pl/articles/1839336.pdf
    Data publikacji:
    2021
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    DDoS
    intrusion detection
    IoT
    machine learning
    Markov decision process
    MDP
    Q-learning
    NSL-KDD
    reinforcement learning
    Opis:
    In this paper, a new reinforcement learning intrusion detection system is developed for IoT networks incorporated with WSNs. A research is carried out and the proposed model RL-IDS plot is shown, where the detection rate is improved. The outcome shows a decrease in false alarm rates and is compared with the current methodologies. Computational analysis is performed, and then the results are compared with the current methodologies, i.e. distributed denial of service (DDoS) attack. The performance of the network is estimated based on security and other metrics.
    Źródło:
    Journal of Telecommunications and Information Technology; 2021, 3; 42-49
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-13 z 13

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies