Sökning: "Intrusion Detection systems"

Visar resultat 11 - 15 av 77 uppsatser innehållade orden Intrusion Detection systems.

1. 11. Implementing and evaluating variations of the Blackhole attack on RPL

   Kandidat-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Adam Pettersson; [2022]
   Nyckelord :;

   Sammanfattning : The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) is the most used routing protocol for resource constrained Internet-of-Things (IoT) networks. With the massive increase in number of Internet-connected IoT devices and the fact that they are becoming more common in safety-critical environments such as in health-care and in the industry, security in these networks are of a big concern. LÄS MER

3. 12. Smart Attack Detection for IoT Networks

   Master-uppsats, KTH/Kommunikationssystem, CoS

   Författare :Yang Yang; [2022]
   Nyckelord :Internet of Things; Security; Machine learning; Intrusion detection; Sakernas Internet; Säkerhet; Maskininlärning; Intrångsdetektering;

   Sammanfattning : The Internet of Things (IoT) is becoming related to more and more people's daily life. It is a network that consists of resource-constrained devices. Nowadays, the application of IoT like smart wearable devices is very common. Due to the wide and important application of IoT, its security also attracts research attention without any doubt. LÄS MER

4. 13. Methods for network intrusion detection : Evaluating rule-based methods and machine learning models on the CIC-IDS2017 dataset

   Master-uppsats, Uppsala universitet/Institutionen för informatik och media

   Författare :Henrik Lindstedt; [2022]
   Nyckelord :MLP; random forest; CIC-IDS2017; Snort; Intrusion Detection System;

   Sammanfattning : Network intrusion detection is a task aimed to identify malicious network traffic. Malicious networktraffic is generated when a perpetrator attacks a network or internet-connected device with the intent todisrupt, steal or destroy a service or information. LÄS MER

5. 14. The Resilience of Deep Learning Intrusion Detection Systems for Automotive Networks : The effect of adversarial samples and transferability on Deep Learning Intrusion Detection Systems for Controller Area Networks

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Ivo Zenden; [2022]
   Nyckelord :Vehicle Security; Deep Learning; Controller Area Network; Intrusion Detection System; Adversarial Samples; Fordonssäkerhet; Deep Learning; Controller Area Network; Intrusion Detection System; kontradiktoriska prover;

   Sammanfattning : This thesis will cover the topic of cyber security in vehicles. Current vehicles contain many computers which communicate over a controller area network. This network has many vulnerabilities which can be leveraged by attackers. To combat these attackers, intrusion detection systems have been implemented. LÄS MER

7. 15. Increasing the Trustworthiness ofAI-based In-Vehicle IDS usingeXplainable AI

   Uppsats för yrkesexamina på avancerad nivå, Mittuniversitetet/Institutionen för informationssystem och –teknologi

   Författare :Hampus Lundberg; [2022]
   Nyckelord :Intrusion Detection System; In-Vehicle Intrusion Detection System; Machine Learning; Deep Learning; Explainable Artificial Intelligence; Trustworthiness.;

   Sammanfattning : An in-vehicle intrusion detection system (IV-IDS) is one of the protection mechanisms used to detect cyber attacks on electric or autonomous vehicles where anomaly-based IDS solution have better potential at detecting the attacks especially zero-day attacks. Generally, the IV-IDS generate false alarms (falsely detecting normal data as attacks) because of the difficulty to differentiate between normal and attack data. LÄS MER