Intrusion Detection System Based on Integrated System Calls Graph and Neural Networks

Gimeno, Francisco José Mora; Mora, Higinio; Volckaert, Bruno; Atrey, Ankita

doi:10.1109/access.2021.3049249

Cited by 13 publications

(8 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of a comparative analysis of the proposed MLDN model with the HBM model, the APID model, and models proposed by [5,6], [7], [8], and [9] are shown in Fig. 20 to 22.…”

Section: Resultsmentioning

confidence: 99%

“…A delegate test will be selected from within this subgroup in order to prepare the model. SVM-based intrusion detection systems are incorporated into another comparative SVM-based model for network intrusion detection models [9]. The most important AI methods have been implemented in a number of the models, and those models also demonstrate intriguing expectations.…”

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

A Fast and Effective Method for Intrusion Detection using Multi-Layered Deep Learning Networks

Srikrishnan¹,

Raaza²,

B³

et al. 2022

IJACSA

View full text Add to dashboard Cite

The practise of recognising unauthorised abnormal actions on computer systems is referred to as intrusion detection. The primary goal of an Intrusion Detection System (IDS) is to identify user behaviours as normal or abnormal based on the data they communicate. Firewalls, data encryption, and authentication techniques were all employed in traditional security systems. Current intrusion scenarios, on the other hand, are very complex and capable of readily breaching the security measures provided by previous protection systems. However, current intrusion scenarios are highly sophisticated and are capable of easily breaking the security mechanisms imposed by the traditional protection systems. Detecting intrusions is a challenging aspect especially in networked environments, as the system designed for such a scenario should be able to handle the huge volume and velocity associated with the domain. This research presents three models, APID (Adaptive Parallelized Intrusion Detection), HBM (Heterogeneous Bagging Model) and MLDN (Multi Layered Deep learning Network) that can be used for fast and efficient detection of intrusions in networked environments. The deep learning model has been constructed using the Keras library. The training data is preprocessed and segregated to fit the processing architecture of neural networks. The network is constructed with multiple layers and the other required parameters for the network are set in accordance with the input data. The trained model is validated using the validation data that has been specifically segregated for this purpose.

show abstract

“…The results of a comparative analysis of the proposed MLDN model with the HBM model, the APID model, and models proposed by [5,6], [7], [8], and [9] are shown in Fig. 20 to 22.…”

Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

A Fast and Effective Method for Intrusion Detection using Multi-Layered Deep Learning Networks

Srikrishnan¹,

Raaza²,

B³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…Authors in [22] proposed intrusion detection system using a deep belief network (DBN) and improved genetic algorithm. A genetic algorithm provides an optimal network, and DBN is used to classify the intrusions.…”

Section: Literature Surveymentioning

confidence: 99%

Multi-Zone-Wise Blockchain Based Intrusion Detection and Prevention System for IoT Environment

Kably¹,

Benbarrad²,

Alaoui³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Blockchain merges technology with the Internet of Things (IoT) for addressing security and privacy-related issues. However, conventional blockchain suffers from scalability issues due to its linear structure, which increases the storage overhead, and Intrusion detection performed was limited with attack severity, leading to performance degradation. To overcome these issues, we proposed MZWB (Multi-Zone-Wise Blockchain) model. Initially, all the authenticated IoT nodes in the network ensure their legitimacy by using the Enhanced Blowfish Algorithm (EBA), considering several metrics. Then, the legitimately considered nodes for network construction for managing the network using Bayesian-Direct Acyclic Graph (B-DAG), which considers several metrics. The intrusion detection is performed based on two tiers. In the first tier, a Deep Convolution Neural Network (DCNN) analyzes the data packets by extracting packet flow features to classify the packets as normal, malicious, and suspicious. In the second tier, the suspicious packets are classified as normal or malicious using the Generative Adversarial Network (GAN). Finally, intrusion scenario performed reconstruction to reduce the severity of attacks in which Improved Monkey Optimization (IMO) is used for attack path discovery by considering several metrics, and the Graph cut utilized algorithm for attack scenario reconstruction (ASR). UNSW-NB15 and BoT-IoT utilized datasets for the MZWB method simulated using a Network simulator (NS-3.26). Compared with previous performance metrics such as energy consumption, storage overhead accuracy, response time, attack detection rate, precision, recall, and F-measure. The simulation result shows that the proposed MZWB method achieves high performance than existing works

show abstract

“…The intrusion detection system presented in Mora‐Gimeno et al 28 utilized graphs and neural networks to detect network attacks. System call graph and deep neural network models were integrated to define the data structure and to detect intrusions 29,30 .…”

Section: Related Workmentioning

confidence: 99%

“…Though the presented approach improved the detection rate, it is not accurate since the minimum ranked features also produce some impacts in the intrusions. [25][26][27] The intrusion detection system presented in Mora-Gimeno et al 28 utilized graphs and neural networks to detect network attacks. System call graph and deep neural network models were integrated to define the data structure and to detect intrusions.…”

Section: Related Workmentioning

confidence: 99%

Optimized deep learning‐based intrusion detection for wireless sensor networks

Vembu

Ramasamy

2022

Int J Communication

View full text Add to dashboard Cite

Summary The technological innovations and wide use of Wireless Sensor Network (WSN) applications need to handle diverse data. These huge data possess network security issues as intrusions that cannot be neglected or ignored. An effective strategy to counteract security issues in WSN can be achieved through the Intrusion Detection System (IDS). IDS ensures network integrity, availability, and confidentiality by detecting different attacks. Regardless of efforts by various researchers, the domain is still open to obtain an IDS with improved detection accuracy with minimum false alarms to detect intrusions. Machine learning models are deployed as IDS, but their potential solutions need to be improved in terms of detection accuracy. The neural network performance depends on feature selection, and hence, it is essential to bring an efficient feature selection model for better performance. An optimized deep learning model has been presented to detect different types of attacks in WSN. Instead of the conventional parameter selection procedure for Convolutional Neural Network (CNN) architecture, a nature‐inspired whale optimization algorithm is included to optimize the CNN parameters such as kernel size, feature map count, padding, and pooling type. These optimized features greatly improved the intrusion detection accuracy compared to Deep Neural network (DNN), Random Forest (RF), and Decision Tree (DT) models.

show abstract

Intrusion Detection System Based on Integrated System Calls Graph and Neural Networks

Cited by 13 publications

References 57 publications

A Fast and Effective Method for Intrusion Detection using Multi-Layered Deep Learning Networks

A Fast and Effective Method for Intrusion Detection using Multi-Layered Deep Learning Networks

Multi-Zone-Wise Blockchain Based Intrusion Detection and Prevention System for IoT Environment

Optimized deep learning‐based intrusion detection for wireless sensor networks

Contact Info

Product

Resources

About