scholarly journals A Graph Representation Learning Algorithm for Low-Order Proximity Feature Extraction to Enhance Unsupervised IDS Preprocessing

2019 ◽  
Vol 9 (20) ◽  
pp. 4473 ◽  
Author(s):  
Yiran Hao ◽  
Yiqiang Sheng ◽  
Jinlin Wang
Keyword(s):  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Representation Learning ◽  
Graph Representation ◽  
First Time ◽  
The Relationship ◽  
Proximity Structure ◽  
Local Proximity ◽  
Low Order

Most existing studies on an unsupervised intrusion detection system (IDS) preprocessing ignore the relationship among packets. According to the homophily hypothesis, the local proximity structure in the similarity relational graph has similar embedding after preprocessing. To improve the performance of IDS by building a relationship among packets, we propose a packet2vec learning algorithm that extracts accurate local proximity features based on graph representation by adding penalty to node2vec. In this algorithm, we construct a relational graph G’ by using each packet as a node, calculate the cosine similarity between packets as edges, and then explore the low-order proximity of each packet via the penalty-based random walk in G’. We use the above algorithm as a preprocessing method to enhance the accuracy of unsupervised IDS by retaining the local proximity features of packets maximally. The original features of the packet are combined with the local proximity features as the input of a deep auto-encoder for IDS. Experiments based on ISCX2012 show that the proposal outperforms the state-of-the-art algorithms by 11.6% with respect to the accuracy of unsupervised IDS. It is the first time to introduce graph representation learning for packet-embedded preprocessing in the field of IDS.

scholarly journals An Intrusion Detection System for Smart Grid Neighborhood Area Network

2021 ◽  
Author(s):  
Nasim Beigi Mohammadi
Keyword(s):  
Intrusion Detection ◽  
Smart Grid ◽  
Intrusion Detection System ◽  
Detection System ◽  
Area Network ◽  
Wormhole Attack ◽  
Detection Systems ◽  
Wireless Mesh ◽  
First Time ◽  
Neighborhood Area Network

Smart grid is expected to improve the efficiency, reliability and economics of current energy systems. Using two-way flow of electricity and information, smart grid builds an automated, highly distributed energy delivery network. In this thesis, we present the requirements for intrusion detection systems in smart grid, neighborhood area network (NAN) in particular. We propose an intrusion detection system (IDS) that considers the constraints and requirements of the NAN. It captures the communication and computation overhead constraints as well as the lack of a central point to install the IDS. The IDS is distributed on some nodes which are powerful in terms of memory, computation and the degree of connectivity. Our IDS uses an analytical approach for detecting Wormhole attack. We simulate wireless mesh NANs in OPNET Modeler and for the first time, we integrate our analytical model in Maple from MapleSoft with our OPNET simulation model.

Hybridization of Machine Learning Algorithm in Intrusion Detection System

2020 ◽  
pp. 150-175
Author(s):  
Amudha P. ◽  
Sivakumari S.
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Classification Accuracy ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Feature Selection Technique ◽  
Efficient Manner

In recent years, the field of machine learning grows very fast both on the development of techniques and its application in intrusion detection. The computational complexity of the machine learning algorithms increases rapidly as the number of features in the datasets increases. By choosing the significant features, the number of features in the dataset can be reduced, which is critical to progress the classification accuracy and speed of algorithms. Also, achieving high accuracy and detection rate and lowering false alarm rates are the major challenges in designing an intrusion detection system. The major motivation of this work is to address these issues by hybridizing machine learning and swarm intelligence algorithms for enhancing the performance of intrusion detection system. It also emphasizes applying principal component analysis as feature selection technique on intrusion detection dataset for identifying the most suitable feature subsets which may provide high-quality results in a fast and efficient manner.

scholarly journals Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1977 ◽  
Author(s):  
Geethapriya Thamilarasu ◽  
Shiven Chawla
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Cyber Attacks ◽  
The Internet ◽  
Critical Systems ◽  
The Internet Of Things

Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.

scholarly journals Exploring the Philosophical Problems of Artificial Intelligence Based on ERP Experiment

Proceedings ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 53
Author(s):  
Yuan Yan
Keyword(s):  
Artificial Intelligence ◽  
Computer Technology ◽  
Cognitive Process ◽  
Learning Algorithm ◽  
Expected Effect ◽  
Scientific Experiments ◽  
Before And After ◽  
Main Effects ◽  
First Time ◽  
The Relationship

To study the cognitive process of the human brain in dealing with philosophical issues, for the first time, from the perspective of scientific experiments, the issue of “relationship” in philosophy was verified. A set of algorithms combining physiology analysis and computer technology linearity and a nonlinear manifold learning algorithm were proposed. Two groups of auditory cognitive experiments were performed, and the concept expected effect was defined as the symbol of conceptual intervention. From the perspective of time, whether the concept was involved after the sensation arises was explored. EEG (electroencephalogram) physiology was used to analyze the data. The results showed that the concept induced a positive shift of the waveform after intervention. It has little effect on the early components, but it has a significant effect on the composition of the sensory components. Waveform changes before and after conceptual intervention have significant main effects. Perceptual production does not involve conceptual intervention, which verifies, in time, that “the relationship” exists.

scholarly journals Hybrid Low-Order and Higher-Order Graph Convolutional Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Fangyuan Lei ◽  
Xun Liu ◽  
Qingyun Dai ◽  
Bingo Wing-Kuen Ling ◽  
Huimin Zhao ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Large Scale ◽  
Representation Learning ◽  
Higher Order ◽  
Semisupervised Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Proposed Model ◽  
Low Order

With the higher-order neighborhood information of a graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher-order graph convolutional networks have a large number of parameters and high computational complexity. Therefore, we propose a hybrid lower-order and higher-order graph convolutional network (HLHG) learning model, which uses a weight sharing mechanism to reduce the number of network parameters. To reduce the computational complexity, we propose a novel information fusion pooling layer to combine the high-order and low-order neighborhood matrix information. We theoretically compare the computational complexity and the number of parameters of the proposed model with those of the other state-of-the-art models. Experimentally, we verify the proposed model on large-scale text network datasets using supervised learning and on citation network datasets using semisupervised learning. The experimental results show that the proposed model achieves higher classification accuracy with a small set of trainable weight parameters.

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