Intelligent Intrusion Detection Techniques for Secure Communications in Wireless Networks: A Survey

2018 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
N. Jayanthi ◽  
Rama Prabha Krishnamoorthy PakkirisamyPAKKIRISAMY
Keyword(s):  
Wireless Networks ◽  
Intrusion Detection ◽  
Secure Communications ◽  
Detection Techniques

scholarly journals Intrusion Detection Techniques for Secure Communication in Different Wireless Networks

2019 ◽  
Vol 8 (9S2) ◽  
pp. 668-671
Keyword(s):  
Wireless Networks ◽  
Network Security ◽  
Intrusion Detection ◽  
Wireless Lan ◽  
Secure Communication ◽  
Detection System ◽  
Technological Advancement ◽  
Detection Techniques ◽  
Wireless Network Security ◽  
Active Interest

Technological advancement in the design of wireless communication have propelled an active interest in the field of Wireless Networks, Wireless Sensor Networks (WSNs), and Mobile Adhoc Networks (MANETs). Now days the speed and privacy are more reason of concern than the performance. The attacks can occur and there is always a chance that it will be a success. One of the major problems with Wireless Network security is that, all types of attacks are not known, and new ones emerge constantly [6]. Moreover, there is also a range of attacks that can be launched in the different mode, and thus making it more difficult for the Intrusion Detection System (IDS) to detect them. Therefore, main approach in network security is to detect and remove malicious intrusions. In this paper three different techniques have been proposed for securing Wireless LAN, WSNs and MANETs.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

scholarly journals A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures

2021 ◽  
Vol 21 (4) ◽  
pp. 1-22
Author(s):  
Safa Otoum ◽  
Burak Kantarci ◽  
Hussein Mouftah
Keyword(s):  
Reinforcement Learning ◽  
Intrusion Detection ◽  
Comparative Study ◽  
Performance Metrics ◽  
Action Learning ◽  
Smart Devices ◽  
Critical Infrastructures ◽  
State Action ◽  
Detection Techniques ◽  
Depth Analysis

Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this article, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognise intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KDD’99 as real attack dataset in our simulations. Results present the performance metrics for three different IDSs, namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), and Q-learning based IDS (Q-IDS), to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that Q-IDS performs with detection rate while SARSA-IDS and TD-IDS perform at the order of .

Intrusion detection techniques in network environment: a systematic review

2021 ◽  
Author(s):  
Maruthi Rohit Ayyagari ◽  
Nishtha Kesswani ◽  
Munish Kumar ◽  
Krishan Kumar
Keyword(s):  
Systematic Review ◽  
Intrusion Detection ◽  
Network Environment ◽  
Detection Techniques

scholarly journals A Comparative Study of Network Securities on Mobile Transaction

2021 ◽  
Author(s):  
Geoffrey Tyolaha ◽  
Moses Israel
Keyword(s):  
Wireless Networks ◽  
Intrusion Detection ◽  
Human Factors ◽  
Comparative Study ◽  
Mobile Networks ◽  
Detection Methods ◽  
Mobile Payments ◽  
4G Networks ◽  
Mobile Transaction ◽  
Average User

In recent years, the number of mobile transactions has skyrocketed. Because mobile payments are made on the fly, many consumers prefer the method to the traditional local payment approach. The rise in mobile payments has inspired this study into the security of mobile networks in order to instill trust in those who may be involved in the transaction in some way. This report is a precursor to explain and compare some of the most popular wireless networks that enable mobile payments, from a security standpoint, this research presents, explains, and compares some of the most common wireless networks that enable mobile payments. Threat models in 3G with connections to GSM, WLAN, and 4G networks are classified into four categories: attacks on privacy, attacks on integrity, attacks on availability, and assaults on authentication. In addition, we offer classification countermeasures which are divided into three categories: cryptographic methods, human factors, and intrusion detection methods. One of the most important aspects we analyze is the security procedures that each network employs. Since the security of these networks is paramount, it gives hope to subscribers. In summary, the study aims to verify if mobile payments offer acceptable security to the average user.

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