6LoWPAN: a study on QoS security threats and countermeasures using intrusion detection system approach

2012 ◽  
Vol 25 (9) ◽  
pp. 1189-1212 ◽  
Author(s):  
Anhtuan Le ◽  
Jonathan Loo ◽  
Aboubaker Lasebae ◽  
Mahdi Aiash ◽  
Yuan Luo
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
System Approach ◽  
Detection System ◽  
Security Threats

scholarly journals A Scalable and Hybrid Intrusion Detection System Based on the Convolutional-LSTM Network

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 583 ◽  
Author(s):  
Muhammad Ashfaq Khan ◽  
Md. Rezaul Karim ◽  
Yangwoo Kim
Keyword(s):  
Intrusion Detection ◽  
Information And Communication Technologies ◽  
Intrusion Detection System ◽  
Large Scale ◽  
Detection System ◽  
Security Threats ◽  
Online Systems ◽  
Network Intrusion ◽  
Lstm Network ◽  
Convolutional Lstm

With the rapid advancements of ubiquitous information and communication technologies, a large number of trustworthy online systems and services have been deployed. However, cybersecurity threats are still mounting. An intrusion detection (ID) system can play a significant role in detecting such security threats. Thus, developing an intelligent and accurate ID system is a non-trivial research problem. Existing ID systems that are typically used in traditional network intrusion detection system often fail and cannot detect many known and new security threats, largely because those approaches are based on classical machine learning methods that provide less focus on accurate feature selection and classification. Consequently, many known signatures from the attack traffic remain unidentifiable and become latent. Furthermore, since a massive network infrastructure can produce large-scale data, these approaches often fail to handle them flexibly, hence are not scalable. To address these issues and improve the accuracy and scalability, we propose a scalable and hybrid IDS, which is based on Spark ML and the convolutional-LSTM (Conv-LSTM) network. This IDS is a two-stage ID system: the first stage employs the anomaly detection module, which is based on Spark ML. The second stage acts as a misuse detection module, which is based on the Conv-LSTM network, such that both global and local latent threat signatures can be addressed. Evaluations of several baseline models in the ISCX-UNB dataset show that our hybrid IDS can identify network misuses accurately in 97.29% of cases and outperforms state-of-the-art approaches during 10-fold cross-validation tests.

Types of Threats and Appropriate Countermeasures for Internet Communications

2021 ◽  
Vol 10 (1) ◽  
pp. 27-37
Author(s):  
Irina-Bristena BACÎŞ
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Configuration Management ◽  
Identity Theft ◽  
Security Threats ◽  
Security Measures ◽  
Protection Measures ◽  
Ip Address ◽  
Network Flooding

Threats can translate into various types of attacks an intruder can take on entities in a network: flooding the target with protocol messages, smurfing (targeted broadcasting of an ICMP protocol-based messaging protocol), distributed attacks that lead to blocking the service for legitimate users, IP address theft and flooding targets with unsolicited emails, identity theft, or fraudulent routing. Against these threats, a variety of security measures can be implemented, such as: configuration management, firewall installation, intrusion detection system installation. Used separately or together, these protection measures can eliminate or even minimize the probability of materializing security threats and preventing attacks on the security features of a system.

scholarly journals Big Data against Security Threats: The SPEAR Intrusion Detection System

2020 ◽  
Author(s):  
Dimitrios Pliatsios ◽  
Panagiotis Sarigiannidis ◽  
Konstantinos Psannis ◽  
Sotirios K. Goudos ◽  
Vasileios Vitsas ◽  
...  
Keyword(s):  
Big Data ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Security Threats

Development of Intrusion Detection System for Security Threats in Internet of Things Using Artificial Neural Network

2019 ◽  
Vol 16 (8) ◽  
pp. 3242-3245
Author(s):  
R. Ramadevi ◽  
N. R. Krishnamoorthy ◽  
D. Marshiana ◽  
Sujatha Kumaran ◽  
N. Aarthi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Security Threats ◽  
Data Set ◽  
Artificial Neural

Internet of things (IoT) is a revolutionary technology which changes our life and work. Many industry sectors such as manufacturing, transportation, utilities, health care, consumer electronics and automobiles are invested and adopted towards IoT technology. The major inconvenience with IoT is its safety, as it is prone to attack by hackers. Detection Systems are used to detect these intrusions to protect the information and communication systems. Hence it is essential to design an intrusion detection system for security threats of IoT networks. This paper focuses, on the development of Artificial Neural Network (ANN) based Intrusion Detection System for threat analysis in IoT network. KDD-99 data set with Denial of Service (DoS) type attack is used to train and test three different ANN models. In this research, a Feed Forward Back Propagation (FFBP) network is used to detect the DoS attack. The process of optimization of a FFBP network involves comparison of classification accuracy during both training and testing in terms of true positive and false positive rates. For the data set considered the optimised network has achieved 100% efficiency during both training and testing.

Security Framework Based on SDN

2014 ◽  
Vol 989-994 ◽  
pp. 4690-4693
Author(s):  
Yang Yu ◽  
Yu Nan Wang ◽  
Wei Yang
Keyword(s):  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Effective Means ◽  
Network System ◽  
Security Threats ◽  
Detection Technology ◽  
Working Principle ◽  
Robust Network

With the growing demand for information, it has a strategic importance for the future of sustainable development how to create a safe and robust network system to ensure the security of important information. Intrusion detection technology can proactively react against intrusion behavior and adjust its strategies in time. So it provides an effective means for network security to minimize or avoid loss when network system is attacked. It is an important part of network security system. This article first explains the current framework and the working principle of SDN. Then it explains the existing security threats of current framework. Next intrusion detection system based on SDN is proposed after the introduction of the intrusion detection system. And we made experiments to verify it. Finally we analyze the lack of the structure and propose some improvements.

scholarly journals Intrusion Detection Framework Using Efficient Spectral Clustering Technique

2020 ◽  
Author(s):  
K. Vengatesan ◽  
Abhishek Kumar ◽  
K. Harish Eknath ◽  
Sayyad Samee ◽  
Rajiv Vincent ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Cyber Security ◽  
Intrusion Detection System ◽  
Spectral Clustering ◽  
Detection System ◽  
Security Threats ◽  
Data Set ◽  
Rule Based ◽  
Clustering Technique ◽  
Irregular Data

Developing cyber-security threats are an industrious test for system managers and security specialists as new malware is persistently cleared. Attackers may search for vulnerabilities in commercial items or execute advanced surveillance crusades to comprehend an objective’s network and assemble data on security items like firewalls and intrusion detection/avoidance systems (network or host-based). Numerous new assaults will in general be changes of existing ones. In such a situation, rule-based systems neglect to detect the assault, despite the fact that there are minor contrasts in conditions/credits between rules to distinguish the new and existing assault. To detect these distinctions the IDS must have the option to disconnect the subset of conditions that are valid and foresee the feasible conditions (not the same as the first) that must be watched. We have given various techniques to detect intrusions (or anomalies) which are dissipated consistently and structure little clusters of irregular data. To improve the clustering results, the dissipated anomalies are detected and expelled before agent clusters are framed utilizing SC (spectral clustering). For assessment, a manufactured and genuine data set are utilized and our outcomes show that the utilization of SC (spectral clustering) is a promising way to deal with the advancement of an Intrusion Detection System.

scholarly journals An IoT-Based Intrusion Detection System Approach for TCP SYN Attacks

2022 ◽  
Vol 71 (2) ◽  
pp. 3839-3851
Author(s):  
Abdelwahed Berguiga ◽  
Ahlem Harchay
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
System Approach ◽  
Detection System

Classification with Clustering and Gaussian Functions in Intrusion Detection System

2014 ◽  
Vol 134 (12) ◽  
pp. 1908-1915 ◽  
Author(s):  
Nannan Lu ◽  
Shingo Mabu ◽  
Yuhong Li ◽  
Kotaro Hirasawa
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Gaussian Functions
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