A Brief Introduction to Intrusion Detection System

Mohamed, Ashara Banu; Idris, Norbik Bashah; Shanmugum, Bharanidharan

doi:10.1007/978-3-642-35197-6_29

Cited by 13 publications

(4 citation statements)

References 16 publications

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“…In the IoT environment, most of the data interaction occurs in the network layer and the perceptual layer, and the distribution of the IoT facilities is complex and scattered. The centralized IDS can therefore not effectively detect intrusions of IoT devices [18].…”

Section: Placement Strategymentioning

confidence: 99%

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Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems

et al. 2020

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With the popularity of Internet of Things (IoT) technology, the security of the IoT network has become an important issue. Traditional intrusion detection systems have their limitations when applied to the IoT network due to resource constraints and the complexity. This research focusses on the design, implementation and testing of an intrusion detection system which uses a hybrid placement strategy based on a multi-agent system, blockchain and deep learning algorithms. The system consists of the following modules: data collection, data management, analysis, and response. The National security lab–knowledge discovery and data mining NSL-KDD dataset is used to test the system. The results demonstrate the efficiency of deep learning algorithms when detecting attacks from the transport layer. The experiment indicates that deep learning algorithms are suitable for intrusion detection in IoT network environment.

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Section: Placement Strategymentioning

confidence: 99%

“…The primary advantage is high scalability, which means it can be adapted to future IoT environments. The disadvantage is that the resource consumption and communication costs can be high [18].…”

Section: Placement Strategymentioning

confidence: 99%

Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems

et al. 2020

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“…In order to counter those attacks, Intrusion Detection System (IDS) Sharafaldin [1] has been working as a defense line to detect system abnormality, including network anomaly or activities with unauthorized access into the network system. IDS can be classified into network-based IDS (NIDS) [2] and host-based IDS (HIDS) [3] in terms of architecture, or signature-based IDS [4] and anomaly-based IDS [5] in terms of detection techniques. NIDS plays a crucial role in the network defense process by raising alerts for network administrators about on-going abnormal activities, provoked by intrusion, attack or malware.…”

Section: Introductionmentioning

confidence: 99%

Network attacks detection across infrastructures using an auto-labeling method and network-based Deep Inductive Transfer Learning approach

Nguyen

Tran

2023

Preprint

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An alarming number of cyber attacks have been witnessed in recent years, especially during COVID-19 epidemic. Many prevention measures have been proposed as a defense line for a network infrastructure, including Intrusion Detection System (IDS). A variance of IDS using Machine Learning and Deep Learning to detect network anomalies is gaining promising results. However, this approach also poses limitations regarding the indigenous dataset acquisition or the ability to apply a model learned from a benchmark dataset to different network infrastructures. Therefore, this paper proposes a reliable automatic labeling method for a new network dataset, and a Deep Transfer Learning model to detect both known and unknown attacks across different network infrastructures, then compares with other approaches. Network attacks detection using auto-labeling and network-based Deep Learning The obtained results reveal an outstanding performance of Transfer-Learning model, in comparison with non Transfer-Learning method, on two benchmark datasets (NSL-KDD, CIC-IDS2017) and a self-captured simulated network dataset using the auto-labeling approach. Furthermore, the model is verified to retain both new and old knowledge after the transfer learning process, which has not been mentioned in other studies, focusing only on learning new knowledge ability.

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“…Yang et al [26] developed a system to secure the IoT in the healthcare environment; it controlled traffic and made the healthcare environment smarter. Furthermore, security methods have been developed for IoT systems, as described in [27][28][29]. Other algorithms applied as solutions for the security of DNP3 traffic include statistical approaches and machine learning [30,31].…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection System to Advance Internet of Things Infrastructure-Based Deep Learning Algorithms

Alkahtani

Aldhyani

2021

Complexity

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Smart grids, advanced information technology, have become the favored intrusion targets due to the Internet of Things (IoT) using sensor devices to collect data from a smart grid environment. These data are sent to the cloud, which is a huge network of super servers that provides different services to different smart infrastructures, such as smart homes and smart buildings. These can provide a large space for attackers to launch destructive cyberattacks. The novelty of this proposed research is the development of a robust framework system for detecting intrusions based on the IoT environment. An IoTID20 dataset attack was employed to develop the proposed system; it is a newly generated dataset from the IoT infrastructure. In this framework, three advanced deep learning algorithms were applied to classify the intrusion: a convolution neural network (CNN), a long short-term memory (LSTM), and a hybrid convolution neural network with the long short-term memory (CNN-LSTM) model. The complexity of the network dataset was dimensionality reduced, and to improve the proposed system, the particle swarm optimization method (PSO) was used to select relevant features from the network dataset. The obtained features were processed using deep learning algorithms. The experimental results showed that the proposed systems achieved accuracy as follows: CNN = 96.60%, LSTM = 99.82%, and CNN-LSTM = 98.80%. The proposed framework attained the desired performance on a new variable dataset, and the system will be implemented in our university IoT environment. The results of comparative predictions between the proposed framework and existing systems showed that the proposed system more efficiently and effectively enhanced the security of the IoT environment from attacks. The experimental results confirmed that the proposed framework based on deep learning algorithms for an intrusion detection system can effectively detect real-world attacks and is capable of enhancing the security of the IoT environment.

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A Brief Introduction to Intrusion Detection System

Cited by 13 publications

References 16 publications

Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems

Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems

Network attacks detection across infrastructures using an auto-labeling method and network-based Deep Inductive Transfer Learning approach

Intrusion Detection System to Advance Internet of Things Infrastructure-Based Deep Learning Algorithms

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