Design of ARP Intrusion Detection System

2014 ◽  
Vol 539 ◽  
pp. 326-330
Author(s):  
Xie Chao Guo
Keyword(s):  
Information Technology ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Network Flow ◽  
Detection System ◽  
Rapid Development ◽  
High Security

According to the rapid development of information technology and network flow, ARP intrusion attack from internet is more and more popular, which damages a lot to normal working, especially in some high security demand fields. Therefore, this paper analyzes the principle of ARP intrusion attack, designed the data collect and analyze module of networks, and then developed an ARP intrusion detection system. It shows this system can detect the ARP intrusion correctly and find where the attack occurs.

Safe and Secure Home Automation Through IoT Applications

2021 ◽  
pp. 190-219
Author(s):  
Rohit Rastogi ◽  
Puru Jain ◽  
Rishabh Jain
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Piezoelectric Sensors ◽  
Home Automation ◽  
State Security ◽  
Iot Applications ◽  
High Security

In current conditions, robotization has changed into the fundamental piece of our lives. Everybody is completely subject to mechanization whether it is an extraordinary bundling or home robotization. So as to bring home automation into thought, everybody now needs a heterogeneous state security, and in our task on residential robotization, such high security highlights are completely on the best possible consumption for this reason. In light of the structure of the interruption zone, there are some fundamental interests in it. Piezoelectric sensors are compelling for sharpening appropriated wellbeing checking and structures. An intrusion detection system (IDS) is a structure that screen for suspicious movement and issues alarms when such advancement is found. While impossible to miss worthiness and presentation is, some obstruction divulgence structures are fit to take practice when poisonous improvement or peculiar action is perceived.

scholarly journals ARIES: A Novel Multivariate Intrusion Detection System for Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5305
Author(s):  
Panagiotis Radoglou Grammatikis ◽  
Panagiotis Sarigiannidis ◽  
Georgios Efstathopoulos ◽  
Emmanouil Panaousis
Keyword(s):  
Intrusion Detection ◽  
Smart Grid ◽  
Intrusion Detection System ◽  
Network Flow ◽  
Network Flows ◽  
Detection System ◽  
Random Noise ◽  
Denial Of Service ◽  
The Third ◽  
Operational Data

The advent of the Smart Grid (SG) raises severe cybersecurity risks that can lead to devastating consequences. In this paper, we present a novel anomaly-based Intrusion Detection System (IDS), called ARIES (smArt gRid Intrusion dEtection System), which is capable of protecting efficiently SG communications. ARIES combines three detection layers that are devoted to recognising possible cyberattacks and anomalies against (a) network flows, (b) Modbus/Transmission Control Protocol (TCP) packets and (c) operational data. Each detection layer relies on a Machine Learning (ML) model trained using data originating from a power plant. In particular, the first layer (network flow-based detection) performs a supervised multiclass classification, recognising Denial of Service (DoS), brute force attacks, port scanning attacks and bots. The second layer (packet-based detection) detects possible anomalies related to the Modbus packets, while the third layer (operational data based detection) monitors and identifies anomalies upon operational data (i.e., time series electricity measurements). By emphasising on the third layer, the ARIES Generative Adversarial Network (ARIES GAN) with novel error minimisation functions was developed, considering mainly the reconstruction difference. Moreover, a novel reformed conditional input was suggested, consisting of random noise and the signal features at any given time instance. Based on the evaluation analysis, the proposed GAN network overcomes the efficacy of conventional ML methods in terms of Accuracy and the F1 score.

scholarly journals A Detailed Analysis of Benchmark Datasets for Network Intrusion Detection System

2021 ◽  
pp. 14-33
Author(s):  
Mossa Ghurab ◽  
Ghaleb Gaphari ◽  
Faisal Alshami ◽  
Reem Alshamy ◽  
Suad Othman
Keyword(s):  
Intrusion Detection ◽  
Detailed Analysis ◽  
Intrusion Detection System ◽  
Network Flow ◽  
Detection System ◽  
Attack Detection ◽  
Network Intrusion Detection ◽  
Detection Techniques ◽  
Network Intrusion ◽  
Network Intrusion Detection System

The enormous increase in the use of the Internet in daily life has provided an opportunity for the intruder attempt to compromise the security principles of availability, confidentiality, and integrity. As a result, organizations are working to increase the level of security by using attack detection techniques such as Network Intrusion Detection System (NIDS), which monitors and analyzes network flow and attacks detection. There are a lot of researches proposed to develop the NIDS and depend on the dataset for the evaluation. Datasets allow evaluating the ability in detecting intrusion behavior. This paper introduces a detailed analysis of benchmark and recent datasets for NIDS. Specifically, we describe eight well-known datasets that include: KDD99, NSL-KDD, KYOTO 2006+, ISCX2012, UNSW-NB 15, CIDDS-001, CICIDS2017, and CSE-CIC-IDS2018. For each dataset, we provide a detailed analysis of its instances, features, classes, and the nature of the features. The main objective of this paper is to offer overviews of the datasets are available for the NIDS and what each dataset is comprised of. Furthermore, some recommendations were made to use network-based datasets.

IoT Applications in Smart Home Security

2021 ◽  
pp. 251-277
Author(s):  
Rohit Rastogi ◽  
Rishabh Jain ◽  
Puru Jain
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Smart Home ◽  
Detection System ◽  
Piezoelectric Sensors ◽  
Home Automation ◽  
State Security ◽  
Iot Applications ◽  
Home Security ◽  
High Security

Robotization has changed into a fundamental piece of our lives. Everybody is completely subject to mechanization whether it is an extraordinary bundling or home robotization. So as to bring home automation into thought, everybody now needs a heterogeneous state security, and in our task on residential robotization, such high security highlights are completely on the best possible consumption. Piezoelectric sensors are compelling for sharpening appropriated wellbeing checking and structures. An intrusion detection system (IDS) is a structure that screens for suspicious movement and issues alarms when such advancement is found. Some obstruction divulgence structures are fit to take practice when poisonous improvement or peculiar action is perceived.

Evaluación Heurística de Usabilidad utilizando Indicadores Cualitativos para Sistemas Detectores de Intrusión

2020 ◽  
Vol 14 (28) ◽  
pp. 46-51
Author(s):  
Cristian Ramón Cappo Araujo ◽  
Cristian Rodrigo Aceval Sosa
Keyword(s):  
Information Technology ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Network Intrusion Detection System ◽  
Security Information

La decisión de implementar en el seno de una organización un Sistema de Detección de Intrusión (IDS) puede resultar en una tarea complicada tanto del punto de vista técnico, así como de aquellos que afectan en la evaluación costo/beneficio de su uso. En este proceso de decisión/evaluación varias heurísticas combinadas con indicadores fueron propuestos focalizadas principalmente en la parte técnica de estos Sistemas. En la creación de estas heurísticas de usabilidad fuimos asistidos por un marco de trabajo (framework) de guías de delineamientos orientadas a los desafíos de implementación y diseño de herramientas para administrar la seguridad en tecnologías de la información (Security Information Technology - SIT). Expone además la experiencia de evaluar estas heurísticas en dos detectores de intrusión de tipo NIDS (Network Intrusion Detection System) ampliamente utilizados en el ámbito de SIT. Pretende por tanto ser una fuente de consulta para los evaluadores y profesionales de Seguridad de Tecnologías de la Información al igual que las personas encargadas de la toma de decisión de la organización.

Safe and Secure Home Automation Through IoT Applications

2022 ◽  
pp. 728-753
Author(s):  
Rohit Rastogi ◽  
Puru Jain ◽  
Rishabh Jain
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Piezoelectric Sensors ◽  
Home Automation ◽  
State Security ◽  
Iot Applications ◽  
High Security

In current conditions, robotization has changed into the fundamental piece of our lives. Everybody is completely subject to mechanization whether it is an extraordinary bundling or home robotization. So as to bring home automation into thought, everybody now needs a heterogeneous state security, and in our task on residential robotization, such high security highlights are completely on the best possible consumption for this reason. In light of the structure of the interruption zone, there are some fundamental interests in it. Piezoelectric sensors are compelling for sharpening appropriated wellbeing checking and structures. An intrusion detection system (IDS) is a structure that screen for suspicious movement and issues alarms when such advancement is found. While impossible to miss worthiness and presentation is, some obstruction divulgence structures are fit to take practice when poisonous improvement or peculiar action is perceived.

scholarly journals Effectiveness of Focal Loss for Minority Classification in Network Intrusion Detection Systems

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 4
Author(s):  
Mulyanto Mulyanto ◽  
Muhamad Faisal ◽  
Setya Widyawan Prakosa ◽  
Jenq-Shiou Leu
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Rapid Development ◽  
Imbalanced Data ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Network Intrusion Detection System ◽  
Access To Data

As the rapid development of information and communication technology systems offers limitless access to data, the risk of malicious violations increases. A network intrusion detection system (NIDS) is used to prevent violations, and several algorithms, such as shallow machine learning and deep neural network (DNN), have previously been explored. However, intrusion detection with imbalanced data has usually been neglected. In this paper, a cost-sensitive neural network based on focal loss, called the focal loss network intrusion detection system (FL-NIDS), is proposed to overcome the imbalanced data problem. FL-NIDS was applied using DNN and convolutional neural network (CNN) to evaluate three benchmark intrusion detection datasets that suffer from imbalanced distributions: NSL-KDD, UNSW-NB15, and Bot-IoT. The results showed that the proposed algorithm using FL-NIDS in DNN and CNN architecture increased the detection of intrusions in imbalanced datasets compared to vanilla DNN and CNN in both binary and multiclass classifications.

scholarly journals A Multifractal Analysis and Machine Learning Based Intrusion Detection System with an Application in a UAS/RADAR System

Drones ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. 21
Author(s):  
Ruohao Zhang ◽  
Jean-Philippe Condomines ◽  
Emmanuel Lochin
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Multifractal Analysis ◽  
Detection System ◽  
Rapid Development ◽  
Mobile Network ◽  
Attack Detection ◽  
Unmanned Aerial Systems ◽  
Novel Algorithm

The rapid development of Internet of Things (IoT) technology, together with mobile network technology, has created a never-before-seen world of interconnection, evoking research on how to make it vaster, faster, and safer. To support the ongoing fight against the malicious misuse of networks, in this paper we propose a novel algorithm called AMDES (unmanned aerial system multifractal analysis intrusion detection system) for spoofing attack detection. This novel algorithm is based on both wavelet leader multifractal analysis (WLM) and machine learning (ML) principles. In earlier research on unmanned aerial systems (UAS), intrusion detection systems (IDS) based on multifractal (MF) spectral analysis have been used to provide accurate MF spectrum estimations of network traffic. Such an estimation is then used to detect and characterize flooding anomalies that can be observed in an unmanned aerial vehicle (UAV) network. However, the previous contributions have lacked the consideration of other types of network intrusions commonly observed in UAS networks, such as the man in the middle attack (MITM). In this work, this promising methodology has been accommodated to detect a spoofing attack within a UAS. This methodology highlights a robust approach in terms of false positive performance in detecting intrusions in a UAS location reporting system.

IoT Applications in Smart Home Security

2022 ◽  
pp. 754-779
Author(s):  
Rohit Rastogi ◽  
Rishabh Jain ◽  
Puru Jain
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Smart Home ◽  
Detection System ◽  
Piezoelectric Sensors ◽  
Home Automation ◽  
State Security ◽  
Iot Applications ◽  
Home Security ◽  
High Security

Robotization has changed into a fundamental piece of our lives. Everybody is completely subject to mechanization whether it is an extraordinary bundling or home robotization. So as to bring home automation into thought, everybody now needs a heterogeneous state security, and in our task on residential robotization, such high security highlights are completely on the best possible consumption. Piezoelectric sensors are compelling for sharpening appropriated wellbeing checking and structures. An intrusion detection system (IDS) is a structure that screens for suspicious movement and issues alarms when such advancement is found. Some obstruction divulgence structures are fit to take practice when poisonous improvement or peculiar action is perceived.

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