scholarly journals Privacy Preserving Machine Learning in Various Attacks on Security Threat Models

2021 ◽  
Vol 11 (2) ◽  
pp. 418-428
Author(s):  
M. Subbulakshmi ◽  
S. Sujitha ◽  
A.P. Vetrivel ◽  
J. Nirmala Gandhi ◽  
Dr.K. Venkatesh Guru
Keyword(s):  
Intrusion Detection System ◽  
Detection System ◽  
Test Results ◽  
Security Threat ◽  
Advantages And Disadvantages ◽  
Flooding Attacks ◽  
Wide Range ◽  
Syn Flooding ◽  
Detection Mechanisms ◽  
Detection Schemes

Intrusion Detection System(IDS) is regularly used to recognize and forestall strange practices in an organization the executives framework. The fundamental thought of IDS is to utilize highlight esteems from network bundle catch system to characterize whether a conduct is anomalous. Notwithstanding, most customary order calculations are unequipped for perceiving obscure practices. The aim of the project is to review the state-of-the art of detection mechanisms of SYN flooding. The detection schemes for SYN Flooding attacks classified broadly into three categories – detection schemes based on the router data structure, statistical analysis of the packet flow based on artificial intelligence. The advantages and disadvantages for various detection schemes under each category have been critically examined Additionally, this crossover methodology for the proposed calculation is pointed toward improving the exactness of strange conduct identification of such a framework, diminishing the calculation season of an arrangement calculation, and making it feasible for the IDS to perceive the obscure and new variation assaults in an organization climate. The test results shows that the proposed calculation outflanks the wide range of various order calculations thought about in this paper regarding the precision.

Hybrid Intrusion Detection Framework for Ad hoc networks

2016 ◽  
Vol 10 (4) ◽  
pp. 1-32 ◽  
Author(s):  
Abdelaziz Amara Korba ◽  
Mehdi Nafaa ◽  
Salim Ghanemi
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Ad Hoc Networks ◽  
Intrusion Detection System ◽  
Ad Hoc ◽  
Detection System ◽  
Security Framework ◽  
Detection Techniques ◽  
Wide Range ◽  
Hoc Networks

In this paper, a cluster-based hybrid security framework called HSFA for ad hoc networks is proposed and evaluated. The proposed security framework combines both specification and anomaly detection techniques to efficiently detect and prevent wide range of routing attacks. In the proposed hierarchical architecture, cluster nodes run a host specification-based intrusion detection system to detect specification violations attacks such as fabrication, replay, etc. While the cluster heads run an anomaly-based intrusion detection system to detect wormhole and rushing attacks. The proposed specification-based detection approach relies on a set of specifications automatically generated, while anomaly-detection uses statistical techniques. The proposed security framework provides an adaptive response against attacks to prevent damage to the network. The security framework is evaluated by simulation in presence of malicious nodes that can launch different attacks. Simulation results show that the proposed hybrid security framework performs significantly better than other existing mechanisms.

scholarly journals Evaluation of Official Healthcare Informatics Applications in Saudi Arabia and their Role in Addressing COVID-19 Pandemic

2021 ◽  
Vol 27 (3) ◽  
pp. 255-263
Author(s):  
Nouf Alassaf ◽  
Sulaiman Bah ◽  
Fatima Almulhim ◽  
Norah AlDossary ◽  
Munirah Alqahtani
Keyword(s):  
Saudi Arabia ◽  
Content Analysis ◽  
Data Collection ◽  
Qualitative Content Analysis ◽  
Test Results ◽  
Healthcare Informatics ◽  
Advantages And Disadvantages ◽  
Wide Range ◽  
Health Related

Objectives: The purpose of this study was to examine official healthcare informatics applications in Saudi Arabia in the context of their role in addressing the coronavirus disease 2019 (COVID-19) pandemic.Methods: This is a case study of official healthcare informatics programs and applications (apps) developed in Saudi Arabia before and during the COVID-19 pandemic. The qualitative content analysis (QCA) method was used. Data collection consisted of two components: a desktop review of documents and actual testing of the programs. According to the QCA method, we developed a matrix for abstracting information on different apps and programs in order to categorize the data. The compilation of information and discussion were based on information summarized in the matrix.Results: Six apps in total were developed before the COVID-19 pandemic. With the advent of the COVID-19 pandemic, three of the apps, SEHA, Mawid, and Sehaty were modified to address different aspects of the pandemic. Both SEHA and Mawid included information about COVID-19 awareness. During the COVID-19 pandemic, three official apps were developed: Tawakkalna, Tetamman, and Tabaud. The Tawakkalna app is mandatory for all citizens and residents to activate when visiting stores and institutions. It has a wide range of COVID-19 and other health-related functions. The Tetamman app provides COVID-19 test results and allows one to check his or her daily symptoms. It also has an educational content library and provides alerts. The Tabaud app notifies individuals if they have been exposed to COVID-19. The features, advantages, and disadvantages of all of the apps were examined.Conclusions: Overall, there were more strengths than shortcomings in the role played by healthcare informatics in the handling of the COVID-19 pandemic in Saudi Arabia.

scholarly journals An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2562
Author(s):  
Georgios Zachos ◽  
Ismael Essop ◽  
Georgios Mantas ◽  
Kyriakos Porfyrakis ◽  
José C. Ribeiro ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Computational Cost ◽  
Healthcare Sector ◽  
The Internet ◽  
Full Potential ◽  
Log Files ◽  
Wide Range ◽  
Internet Of Medical Things

Over the past few years, the healthcare sector is being transformed due to the rise of the Internet of Things (IoT) and the introduction of the Internet of Medical Things (IoMT) technology, whose purpose is the improvement of the patient’s quality of life. Nevertheless, the heterogenous and resource-constrained characteristics of IoMT networks make them vulnerable to a wide range of threats. Thus, novel security mechanisms, such as accurate and efficient anomaly-based intrusion detection systems (AIDSs), considering the inherent limitations of the IoMT networks, need to be developed before IoMT networks reach their full potential in the market. Towards this direction, in this paper, we propose an efficient and effective anomaly-based intrusion detection system (AIDS) for IoMT networks. The proposed AIDS aims to leverage host-based and network-based techniques to reliably collect log files from the IoMT devices and the gateway, as well as traffic from the IoMT edge network, while taking into consideration the computational cost. The proposed AIDS is to rely on machine learning (ML) techniques, considering the computation overhead, in order to detect abnormalities in the collected data and thus identify malicious incidents in the IoMT network. A set of six popular ML algorithms was tested and evaluated for anomaly detection in the proposed AIDS, and the evaluation results showed which of them are the most suitable.

scholarly journals Reduct ECOC Framework for Network Intrusion Detection System

2020 ◽  
Vol 9 (3) ◽  
pp. 258-266
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Class Imbalance ◽  
Classification Problem ◽  
Network Intrusion ◽  
Wide Range ◽  
Multi Class Classification ◽  
Network Technologies ◽  
Better Than

Now a day’s network security is major concern for e-government and e-commerce applications. A wide range of malicious activities are increasing with the usage of internet and network technologies. Identifying novel threats and finding modern solutions for network to prevent from these threats are important. Designing an effective intrusion detection system is significant to continuously look out the network activities to efficiently thwart malicious attacks or to identify the intruders. To tackle multi class imbalance classification problem in networks, a reduct based ECOC ensemble framework for NIDS is proposed to efficiently identify attacks in a multi class scenario. The Reduct-ECOC classifier is validated on highly imbalanced benchmark NSL-KDD intrusion datasets as well as other UCI-ML datasets. The experimental results on eight highly imbalanced datasets show that Reduct-ECOC classifier performs better than many other state-of-art multi-class classification ECOC learning methods.

scholarly journals Hybrid Intrusion Detection System for Internet of Things (IoT)

2020 ◽  
Vol 2 (4) ◽  
pp. 190-199 ◽  
Author(s):  
Dr. S. Smys ◽  
Dr. Abul Basar ◽  
Dr. Haoxiang Wang
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Research Work ◽  
Experimental Result ◽  
Security Issues ◽  
Wide Range ◽  
Conventional Machine

Internet of things (IoT) is a promising solution to connect and access every device through internet. Every day the device count increases with large diversity in shape, size, usage and complexity. Since IoT drive the world and changes people lives with its wide range of services and applications. However, IoT provides numerous services through applications, it faces severe security issues and vulnerable to attacks such as sinkhole attack, eaves dropping, denial of service attacks, etc., Intrusion detection system is used to detect such attacks when the network security is breached. This research work proposed an intrusion detection system for IoT network and detect different types of attacks based on hybrid convolutional neural network model. Proposed model is suitable for wide range of IoT applications. Proposed research work is validated and compared with conventional machine learning and deep learning model. Experimental result demonstrate that proposed hybrid model is more sensitive to attacks in the IoT network.

Research on Network Intrusion Detection Based on Data Mining Technology

2015 ◽  
Vol 713-715 ◽  
pp. 2081-2084 ◽  
Author(s):  
Zeng Ying He
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Normal Activity ◽  
Network Intrusion Detection ◽  
Test Results ◽  
Mining Technology ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Aiming at some deficiencies of existing network intrusion detection system, the paper proposes a network intrusion detection system model based on data mining, applying data mining technology to network intrusion detection, and constructed the final test results of the system on the basis of Snort design. Experimental results demonstrate that this data mining based on cluster algorithm can effectively establish models of network normal activity and significantly accelerate intrusion detection, whilst its association analyzer can effectively unearth some new intrusion patterns from abnormal logs, and automatically construct intrusion detection rules.

scholarly journals Intrusion Detection Systems

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3976-3983
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Detection Rates ◽  
Detailed Review ◽  
Detection Techniques ◽  
Detection Systems ◽  
Research Gap ◽  
Detection Schemes

Intrusion Detection System is competent to detect the intrusions and alerting the administrator of system about the signs of possible intrusions. This paper presents a detailed review of the intrusion detection techniques used in WSNs. More specifically, the existing methods for blackhole and sinkhole attacks detection are reviewed. However, it is noted that most intrusion detection schemes proposed in the literature are either inefficient or have low detection rates/high false positive rates. This survey also highlights the research gap in this domain and provides better scope for the advanced work.

Intrusion Detection Systems: A Review

2019 ◽  
Vol 118 (6) ◽  
pp. 60-79
Author(s):  
Ashwini V. Jatti ◽  
V. J. K. Kishor Sonti
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Detection Rates ◽  
Detailed Review ◽  
Detection Techniques ◽  
Detection Systems ◽  
Research Gap ◽  
Detection Schemes

Intrusion Detection System is competent to detect the intrusions and alerting the administrator of system about the signs of possible intrusions. This paper presents a detailed review of the intrusion detection techniques used in WSNs. More specifically, the existing methods for blackhole and sinkhole attacks detection are reviewed. However, it is noted that most intrusion detection schemes proposed in the literature are either inefficient or have low detection rates/high false positive rates. This survey also highlights the research gap in this domain and provides better scope for the advanced work.

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