Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 36 for “"Network Intrusion Detection"”.
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Network Intrusion Detection: Monitoring, Simulation And Visualization
This dissertation presents our work on network intrusion detection and intrusion sim- ulation. The work in intrusion detection consists of two different network anomaly-based approaches. The work in intrusion simulation introduces a model using explicit traffic gen- eration for the packet level …
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Undersampling GA-SVM for network intrusion detection
Network intrusion detection is one of the hottest issues in the world. An increasing number of researchers and engineers deal with this problem by using machine learning methods. However, how to improve the identification accuracy of all the attack classes remains unsolved since the dataset is an …
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A SOM+ Diagnostic System for Network Intrusion Detection
… a new theoretical Soft Computing (SC) hybridized network intrusion detection diagnostic system including complex hybridization of a 3D full color Self-Organizing Map (SOM), Artificial Immune System Danger Theory (AISDT), and a Fuzzy Inference System (FIS). This SOM+ diagnostic archetype includes …
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A statistical process control approach for network intrusion detection
Intrusion detection systems (IDS) have a vital role in protecting computer networks and information systems. In this thesis we applied an SPC monitoring concept to a certain type of traffic data in order to detect a network intrusion. We developed a general SPC intrusion detection approach and …
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Intelligent network intrusion detection using an evolutionary computation approach
… the need for secure and reliable computer networks also increases. Availability of effective automatic tools for carrying out different types of network attacks raises the need for effective intrusion detection systems. Generally, a comprehensive defence mechanism consists of three phases, …
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Using Self-Organizing Maps for Computer Network Intrusion Detection
<p>Anomaly detection in user access patterns using artificial neural networks is a novel way of combating the ever-present concern of computer network intrusion detection for many entities around the world. Anomaly detection is a technique in network security in which a profile is built around a …
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High Performance Network Intrusion Detection: A New Paradigm is Needed
… rates and complicated protocols have outpaced network intrusion detection systems. Administrators are forced to choose between breadth and depth: systems either deeply analyze traffic for a small handful of vulnerabilities, or search for many in parallel using more primitive (and easily evadable) …
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Measuring concept drift in malware and network intrusion detection models
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Explainable AI Methods For Enhancing AI-Based Network Intrusion Detection Systems
In network security, the exponential growth of intrusions stimulates research toward developing advanced artificial intelligence (AI) techniques for intrusion detection systems (IDS). However, the reliance on AI for IDS presents challenges, including the performance variability of different AI …
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Towards an Efficient Network Intrusion Detection System for IoT Networks Leveraging Graph Neural Networks
… new attacks in IoT traffic because they treat network flows independently. Graph Neural Networks (GNNs) have emerged as a promising alternative having the ability to capture the underlying network topology. However, existing approaches focus solely on either node or edge features, limiting …
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Creating Models Of Internet Background Traffic Suitable For Use In Evaluating Network Intrusion Detection Systems
… Internet background traffic generation and network intrusion detection. It is organized in two parts. Part one introduces a method to model realistic Internet background traffic and demonstrates how the models are used both in a simulation environment and in a lab environment. Part two …
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A Machine Learning Approach to Network Intrusion Detection System Using K Nearest Neighbor and Random Forest
… for cyber criminals and security experts. Intrusions have now become a major concern in the cyberspace. Different methods are employed in tackling these threats, but there has been a need now more than ever to updating the traditional methods from rudimentary approaches such as manually …
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Attack development for intrusion detector evaluation
An important goal of the 1999 DARPA Intrusion Detection Evaluation was to promote the development of intrusion detection systems that can detect new attacks. This thesis describes UNIX attacks developed for the 1999 DARPA Evaluation. Some attacks were new in 1999 and others were stealthy versions …
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Noncooperative Games for Control of Networked Systems
… resource allocation schemes to address various network control problems such as congestion control, code division multiple access (CDMA) power control, and network intrusion detection and response. In the cases of CDMA power control and congestion control, a fairly general, distributed, …
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Algorithms and Frameworks for Accelerating Security Applications on HPC Platforms
… including mobile software security, network security, and system security. They have the following performance issues, respectively: 1) The flow- and context-sensitive static analysis for the large and complex Android APKs are incredibly time-consuming. Existing CPU-only …
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Flow-oriented anomaly-based detection of denial of service attacks with flow-control-assisted mitigation
… In this doctoral dissertation, the Computer Network Management and Control System (CNMCS) is proposed and investigated; it consists of the Flow-based Network Intrusion Detection System (FNIDS), the Flow-based Congestion Control (FCC) System, and the Server Bandwidth Management System (SBMS). …
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Detecting exploit patterns from network packet streams
<p>Network-based Intrusion Detection Systems (NIDS), e.g., Snort, Bro or NSM, try to detect malicious network activity such as Denial of Service (DoS) attacks and port scans by monitoring network traffic. Research from network traffic measurement has identified various patterns that exploits on …
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Towards Accurate and Reliable Industrial Intrusion Detection Systems Using Shadow Replicas
… protect SCADA systems against attacks that evade detection because of the lack of a comprehensive view of both application and network-layer responses. Specifically, we leverage multiple open-source Network Intrusion Detection Systems (NIDSs) paired with a SCADA shadow replica to provide both …
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PATTERN ENCODING ALGORITHMS AND INFORMATION MODELING METRICS FOR NETWORK QUALITY OF SERVICE
Networks are becoming increasingly complex, making network quality of service (QoS) an ongoing and difficult problem. Important QoS challenges include ensuring availability of services, privacy and accuracy of data and preventing data loss. These challenges can be addressed by providing effective …
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A CNN–LSTM–Attention Hybrid Architecture for Real-Time Intrusion Detection at the Data Link Layer
… one of the most underexplored areas in modern network intrusion detection research, despite its critical role as the foundation of reliable communication between networked devices. Attacks at this layer, such as ARP spoofing, MAC flooding, VLAN hopping, and DHCP starvation, can compromise …
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