scholarly journals Risk Data Analysis Based Anomaly Detection of Ship Information System

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3403 ◽  
Author(s):  
Bowen Xing ◽  
Yafeng Jiang ◽  
Yuqing Liu ◽  
Shouqi Cao
Keyword(s):  
Information System ◽  
Data Analysis ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Detection Method ◽  
Control Task ◽  
Detection Mechanism ◽  
Network Attacks ◽  
Collaborative Control ◽  
Industrial State

Due to the vulnerability and high risk of the ship environment, the Ship Information System (SIS) should provide 24 hours of uninterrupted protection against network attacks. Therefore, the corresponding intrusion detection mechanism is proposed for this situation. Based on the collaborative control structure of SIS, this paper proposes an anomaly detection pattern based on risk data analysis. An intrusion detection method based on the critical state is proposed, and the corresponding analysis algorithm is given. In the Industrial State Modeling Language (ISML), risk data are determined by all relevant data, even in different subsystems. In order to verify the attack recognition effect of the intrusion detection mechanism, this paper takes the course/roll collaborative control task as an example to carry out simulation verification of the effectiveness of the intrusion detection mechanism.

Internet of Things (IoTs) Security: Intrusion Detection using Deep Learning

2021 ◽  
Author(s):  
Ozgur Koray Sahingoz ◽  
Ugur Cekmez ◽  
Ali Buldu
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Recent Literature ◽  
Imbalanced Data ◽  
Industrial Applications ◽  
Test Bed ◽  
Detection Mechanism ◽  
Network Attacks ◽  
Negative Effect ◽  
Augmentation Techniques

With the development of sensor and communication technologies, the use of connected devices in industrial applications has been common for a long time. Reduction of costs during this period and the definition of Internet of Things (IoTs) concept have expanded the application area of small connected devices to the level of end-users. This paved the way for IoT technology to provide a wide variety of application alternative and become a part of daily life. Therefore, a poorly protected IoT network is not sustainable and has a negative effect on not only devices but also the users of the system. In this case, protection mechanisms which use conventional intrusion detection approaches become inadequate. As the intruders’ level of expertise increases, identification and prevention of new kinds of attacks are becoming more challenging. Thus, intelligent algorithms, which are capable of learning from the natural flow of data, are necessary to overcome possible security breaches. Many studies suggesting models on individual attack types have been successful up to a point in recent literature. However, it is seen that most of the studies aiming to detect multiple attack types cannot successfully detect all of these attacks with a single model. In this study, it is aimed to suggest an all-in-one intrusion detection mechanism for detecting multiple intrusive behaviors and given network attacks. For this aim, a custom deep neural network is designed and implemented to classify a number of different types of network attacks in IoT systems with high accuracy and F1-score. As a test-bed for comparable results, one of the up-to-date dataset (CICIDS2017), which is highly imbalanced, is used and the reached results are compared with the recent literature. While the initial propose was successful for most of the classes in the dataset, it was noted that achievement was low in classes with a small number of samples. To overcome imbalanced data problem, we proposed a number of augmentation techniques and compared all the results. Experimental results showed that the proposed methods yield highest efficiency among observed literature.

An Efficient DoS Attacks Detection Method Based on Data Mining Scheme

2011 ◽  
Vol 267 ◽  
pp. 302-307
Author(s):  
Xiang Chen
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Nearest Neighbors ◽  
Selection Method ◽  
Instance Selection ◽  
Dos Attacks ◽  
K Nearest Neighbors ◽  
Detection Mechanism ◽  
Mining Scheme ◽  
Network Anomaly Detection

To defend against DoS attacks and ensure QoS of web server, we first propose an efficient network anomaly detection method based on TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) algorithm. Secondly, we integrate a lot of objective and efficient DoS impact metrics from the perceptions of the end users into TCM-KNN algorithm to build a robust anomaly detection mechanism. Finally, Genetic Algorithm (GA) based instance selection method is introduced to boost the real-time detection performance of our method.

The Simulation Experiment and Research on an Improved Cumulative Sum Anomaly Detection Method

2015 ◽  
Vol 743 ◽  
pp. 219-225 ◽  
Author(s):  
Hua Zhao
Keyword(s):  
Control System ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Cyber Security ◽  
Detection Method ◽  
Metallurgical Industry ◽  
Detection Algorithm ◽  
Temperature Control System ◽  
Measured Signal ◽  
Cumulative Sum

In order to solve the rising serious cyber security problem of the industry control system (ICS) and to improve the reliability of process control of industrial control system, this paper presents an anomaly detection algorithm based on statistical methods. Aimed at the dome temperature control system of hot blast stove in the metallurgical industry, we established that system’s mathematical model and calculate the difference between the predicted output of the model and the measured signal at each moment to form the time-based statistical sequence. Applying the improved non-parametric cumulative sum intrusion detection method, we realizes the online intrusion detection and alarm. The simulation detection experiment shows that the method has a good real-time.

Research on Data Security Detection Algorithm in IoT Based on K-means

2021 ◽  
Vol 14 ◽  
Author(s):  
Jianxing Zhu ◽  
Lina Huo ◽  
Mohd Dilshad Ansari ◽  
Mohammad Asif Ikbal
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Data Security ◽  
Detection Method ◽  
Detection Algorithm ◽  
The Internet ◽  
Human Beings ◽  
Network Attacks ◽  
Hybrid Detection ◽  
The Internet Of Things

Background: The development of the Internet of Things has prominently expanded the perception of human beings, but ensuing security issues have attracted people's attention. From the perspective of the relatively weak sensor network in the Internet of Things. Method: Proposed method Aiming at the characteristics of diversification and heterogeneity of collected data in sensor networks, the data set is clustered and analyzed from the aspects of network delay and data flow to extract data characteristics. Then, according to the characteristics of different types of network attacks, a hybrid detection method for network attacks is established. An efficient data intrusion detection algorithm based on K-means clustering is proposed Results: This paper proposes a network node control method based on traffic constraints to improve the security level of the network. Simulation experiments show that compared with traditional password-based intrusion detection methods; the proposed method has a higher detection level and is suitable for data security protection in the Internet of Things. Conclusions: This paper proposes an efficient intrusion detection method for applications with Internet of Things

scholarly journals Research on Data Security Detection Algorithm in IoT Based on K-means

2021 ◽  
Vol 22 (2) ◽  
Author(s):  
Jianxing Zhu ◽  
Lina Huo ◽  
Mohd Dilshad Ansari ◽  
Mohammad Asif Ikbal
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Data Security ◽  
Detection Method ◽  
Detection Algorithm ◽  
The Internet ◽  
Human Beings ◽  
Network Attacks ◽  
Hybrid Detection ◽  
The Internet Of Things

The development of the Internet of Things has prominently expanded the perception of human beings, but ensuing security issues have attracted people's attention. From the perspective of the relatively weak sensor network in the Internet of Things. Proposed method is aiming at the characteristics of diversification and heterogeneity of collected data in sensor networks; the data set is clustered and analyzed from the aspects of network delay and data flow to extract data characteristics. Then, according to the characteristics of different types of network attacks, a hybrid detection method for network attacks is established. An efficient data intrusion detection algorithm based on K-means clustering is proposed. This paper proposes a network node control method based on traffic constraints to improve the security level of the network. Simulation experiments show that compared with traditional password-based intrusion detection methods; the proposed method has a higher detection level and is suitable for data security protection in the Internet of Things. This paper proposes an efficient intrusion detection method for applications with Internet of Things.

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