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INTRUSION DETECTION SYSTEM ARCHITECTURE [37]. 

Source publication
+5

From Intrusion Detection to an Intrusion Response System: Fundamentals,
Requirements, and Future Directions
Article
Full-text available
 * Mar 2017

 * Shahid Anwar
 * Jasni Mohamad Zain
 * Mohamad Fadli Zolkipli
 * [...]
 * Victor Chang

In the past few decades, the rise in attacks on communication devices in
networks has resulted in a reduction of network functionality, throughput, and
performance. To detect and mitigate these network attacks, researchers,
academicians, and practitioners developed Intrusion Detection Systems (IDSs)
with automatic response systems. The response sys...
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CONTEXT IN SOURCE PUBLICATION

Context 1
... mobile visualization hybrid IDS [4] is an adaptive hybrid IDS that uses an
artificial neural network for intrusion detection. A generalized intrusion
detection and prevention (GIDP) mechanism for mobile ad hoc-networks [35] Figure
3 presents the basic architecture of intrusion detection system. ...
View in full-text


SIMILAR PUBLICATIONS

INTRUSION DETECTION SYSTEM
Article
Full-text available
 * Apr 2017

 * Mohit Tiwari
 * Raj Kumar
 * Akash Bharti
 * Jai Kishan

Intrusion Detection System (IDS) defined as a Device or software application
which monitors the network or system activities and finds if there is any
malicious activity occur. Outstanding growth and usage of internet raises
concerns about how to communicate and protect the digital information safely. In
today's world hackers use different types of...
View




CITATIONS

... interest in the research of intrusion detection systems and good detection
results have been achieved [8][9][10]. ...

MFVT: an anomaly traffic detection method merging feature fusion network and
vision transformer architecture
Article
Full-text available
 * Apr 2022
 * EURASIP J WIREL COMM

 * Ming Li
 * Dezhi Han
 * Dun Li
 * Han Liu
 * Chin-Chen Chang

Network intrusion detection, which takes the extraction and analysis of network
traffic features as the main method, plays a vital role in network security
protection. The current network traffic feature extraction and analysis for
network intrusion detection mostly uses deep learning algorithms. Currently,
deep learning requires a lot of training resources and has weak processing
capabilities for imbalanced datasets. In this paper, a deep learning model
(MFVT) based on feature fusion network and vision transformer architecture is
proposed, which improves the processing ability of imbalanced datasets and
reduces the sample data resources needed for training. Besides, to improve the
traditional raw traffic features extraction methods, a new raw traffic features
extraction method (CRP) is proposed, and the CPR uses PCA algorithm to reduce
all the processed digital traffic features to the specified dimension. On the
IDS 2017 dataset and the IDS 2012 dataset, the ablation experiments show that
the performance of the proposed MFVT model is significantly better than other
network intrusion detection models, and the detection accuracy can reach the
state-of-the-art level. And, when MFVT model is combined with CRP algorithm, the
detection accuracy is further improved to 99.99%.
View
... It works using constructed hyper-plane that uses linear models implementing
class boundaries that has non-linear attributes which engages input vectors with
non-linear mapping in high dimensional feature space [12]. The SVM has been
engaged in different domains for prediction ranging from system intruder's
detection, pattern recognition, estimation of age, face recognition, as well as
telecommunications according to ([13], [14], [15], [16). ...

Analysis of Violent Crime Dataset Using Support Vector Machine Model
Chapter
 * Apr 2022

 * Falade Adesola
 * Ambrose Azeta
 * Sanjay Misra
 * Aderonke Oni
 * Ademola Omolola

It is usually a challenging task predicting violent crime occurrences in space
and time. Huge dataset are needed for accurate prediction of future violent
crime occurrence, which in most cases were subjected to artificial intelligence
or statistical methods. Most studies from literature adopted data mining
techniques for violent crime prediction with some inherent limitation of
accuracy as one of the gaps that needed to be filled. The study applied support
vector machine model on the six different historical violent crime dataset
gathered between July 2016 and July 2019 from Nigeria Police Lagos headquarter
to predict spatio-temporal occurrences of violent crime in the state. The six
different violent crime dataset used for the study are: armed robbery, rape,
kidnapping, assault, murder and manslaughter. The dataset was preprocessed and
fed into the support vector machine model built in Watson machine learning
studio using python as a programming language. The model returned 82.12%
prediction accuracy, which is assumed to be good enough for any prediction
system. This result was evaluated using confusion matric, and tested against
some results from literature, and was found to out-perform some machine learning
models used in the previous studies. Based on this empirical study, the police
authority could adopt this model to strengthen violent crime prevention
strategies in order to mitigate violent crime occurrences in Lagos state,
Nigeria.
View
... The purpose of an IDS is to monitor traffic data in order to identify and
protect an information system against intrusions that could compromise its
confidentiality, integrity, and availability [35]. The purpose of an IDS is to
capture a copy of information systems' data traffic and to analyze this copy to
identify potentially harmful activities [36]. ...

Investigation on Intrusion Detection Systems (IDSs) in IoT
Article
Full-text available
 * Mar 2022

 * Karthikkumar Vaigandla
 * Nilofar Azmi
 * Radhakrishna Karne

In smart environments, comfort and efficiency are important goals in terms of
the quality of human life. Recent developments in Internet of Things (IoT)
technology have made it possible to design smart environments. IoT-based smart
environments are concerned with security and privacy as key issues. Systems
based on the IoT pose a security threat to smart environments. In order to
prevent IoT-related security attacks which take advantage of some of these
security vulnerabilities, intrusion detection systems (IDSs) designed for IoT
environments are crucial. Data generated by connected objects in the age of the
IoT provides the basis for big data analytics, which could be employed to
identify patterns and identifies anomalies in data. In order to detect
intrusions, most cyber security systems employ IDSs, which are used by a variety
of techniques and architectures. As opposed to signature-based IDS,
anomaly-based IDS learns the normal pattern of system behavior and alerts on
abnormal events that occur, as opposed to monitoring monitored events against a
database of known intrusion experiences. This paper focuses on the IDS
implementation on the IoT network. The use of sensor devices to collect data
from smart grid environments has led to smart grids becoming the preferred
intrusion target due to the IoTs using advanced information technology. Clouds
are data storage systems that provide a variety of smart infrastructure
services, such as smart homes and smart buildings, over the internet. A deep
learning-based intrusion detection system for the Internet of Things requires
consideration of key design principles presented in this paper.
View
... As internet-connected devices are increasing, cyber security became more
important [1]. Intrusion is a series of actions that invade security policies
like integrity and confidentiality [2]. The adversaries attack a network with
highly skilled programming tools and target vulnerabilities in the network. ...

A Deep Learning-Based Smart Framework for Cyber-Physical and Satellite System
Security Threats Detection
Article
Full-text available
 * Feb 2022

 * Imran Ashraf
 * Manideep Narra
 * Muhammad Umer
 * Rizwan Majeed
 * Nouman Rasool

An intrusion detection system serves as the backbone for providing high-level
network security. Different forms of network attacks have been discovered and
they continue to become gradually more sophisticated and complicated. With the
wide use of internet-based applications, cyber security has become an important
research area. Despite the availability of many existing intrusion detection
systems, intuitive cybersecurity systems are needed due to alarmingly increasing
intrusion attacks. Furthermore, with new intrusion attacks, the efficacy of
existing systems depletes unless they evolve. The lack of real datasets adds
further difficulties to properly investigating this problem. This study proposes
an intrusion detection approach for the modern network environment by
considering the data from satellite and terrestrial networks. Incorporating
machine learning models, the study proposes an ensemble model RFMLP that
integrates random forest (RF) and multilayer perceptron (MLP) for increasing
intrusion detection performance. For analyzing the efficiency of the proposed
framework, three different datasets are used for experiments and validation,
namely KDD-CUP 99, NSL-KDD, and STIN. In addition, performance comparison with
state-of-the-art models is performed which suggests that the RFMLP can detect
intrusion attacks with high accuracy than the existing approaches.
View
... Response mechanism is the way an IDS responds when an intrusion has
occurred; it can be an active or a passive response (Anwar et al. 2017). Active
IDS response mechanism can be stated as the system built to block the intrusions
or attacks instantly at the time they are detected without even concerning the
security expert (Inayat et al. 2016). ...

A survey on intrusion detection system: feature selection, model, performance
measures, application perspective, challenges, and future research directions
Article
Full-text available
 * Jan 2022
 * ARTIF INTELL REV

 * Ankit Thakkar
 * Ritika Lohiya

With the increase in the usage of the Internet, a large amount of information is
exchanged between different communicating devices. The data should be
communicated securely between the communicating devices and therefore, network
security is one of the dominant research areas for the current network scenario.
Intrusion detection systems (IDSs) are therefore widely used along with other
security mechanisms such as firewall and access control. Many research ideas
have been proposed pertaining to the IDS using machine learning (ML) techniques,
deep learning (DL) techniques, and swarm and evolutionary algorithms (SWEVO).
These methods have been tested on the datasets such as DARPA, KDD CUP 99, and
NSL-KDD using network features to classify attack types. This paper surveys the
intrusion detection problem by considering algorithms from areas such as ML, DL,
and SWEVO. The survey is a representative research work carried out in the field
of IDS from the year 2008 to 2020. The paper focuses on the methods that have
incorporated feature selection in their models for performance evaluation. The
paper also discusses the different datasets of IDS and a detailed description of
recent dataset CIC IDS-2017. The paper presents applications of IDS with
challenges and potential future research directions. The study presented, can
serve as a pedestal for research communities and novice researchers in the field
of network security for understanding and developing efficient IDS models.
View
... Denial of Service DoS attack, for instance, floods computing resources with
information, which destroys the concept of availability, while malware disrupts
the implementation of a program that infringes the concept of integrity [19]. An
IDS is a surveillance and review tool for operations in a computerized system or
infrastructure to identify perceived threats by their observations of offences
related to the principles of CIA in computer security policy [18], [20], [21].
According to Nexusguard the DDoS attacks in first quarter of 2020 has increased
by 500% as compared to last quarter of 2019 [22]. ...

Protocol Based Deep Intrusion Detection for DoS and DDoS attacks using UNSW-NB15
and Bot-IoT data-sets
Article
Full-text available
 * Dec 2021

 * Muhammad Zeeshan
 * Qaiser Riaz
 * Muhammad Ahmad Bilal
 * Muhammad K. Shahzad
 * Azizur Rahim

Since its inception, the Internet of Things (IoT) has witnessed mushroom growth
as a breakthrough technology. In a nutshell, IoT is the integration of devices
and data such that processes are automated and centralized to a certain extent.
IoT is revolutionizing the way business is done and is transforming society as a
whole. As this technology advances further, the need to exploit detection and
weakness awareness increases to prevent unauthorized access to critical
resources and business functions, thereby rendering the system unavailable.
Denial of Service (DoS) and Distributed DoS attacks are all too common. In this
paper, we propose a Protocol Based Deep Intrusion Detection (PB-DID)
architecture, in which we created a data-set of packets from IoT traffic by
comparing features from the UNSWNB15 and Bot-IoT data-sets based on flow and
Transmission Control Protocol (TCP). We classify non-anomalous, DoS, and DDoS
traffic uniquely by taking care of the problems like imbalanced and
over-fitting. We have achieved a classification accuracy of 96.3% by using deep
learning (DL) technique.
View
... Web 2.0, in collaboration with SaaS (Software as a Service), free their
users from system maintenance troubles as the networks hold the cloud
environment. Still, it faces many security concerns like[11][41][1][42][43]. ...

Cloud related paper
Preprint
 * Dec 2021

 * Dr Kamta Nath Mishra
 * Vandana Bhattacharjee
 * Shashwat Saket
 * Shivam Mishra

Abstract
View
... Intrusion detection is one of the significant presentations of outlier
identification that is utilized to recognize the system attacks by opponents.
Intrusion Detection Systems (IDSs) are fundamental to guarantee system security
[4,5]. The generally utilized methodologies for intrusion detection are the
anomaly and signature dependent methodologies [6]. ...

Support Based Graph Framework for Effective Intrusion Detection and
Classification
Preprint
Full-text available
 * Oct 2021

 * Rahul B Adhao
 * Vinod K Pachghare

Intrusion Detection System is one of the worthwhile areas for researchers for a
long. Numbers of researchers have worked for increasing the efficiency of
Intrusion Detection Systems. But still, many challenges are present in modern
Intrusion Detection Systems. One of the major challenges is controlling the
false positive rate. In this paper, we have presented an efficient soft
computing framework for the classification of intrusion detection dataset to
diminish a false positive rate. The proposed processing steps are described as;
the input data is at first pre-processed by the normalization process.
Afterward, optimal features are chosen for the dimensionality decrease utilizing
krill herd optimization. Here, the effective feature assortment is utilized to
enhance classification accuracy. Support value is then estimated from ideally
chosen features and lastly, a support value-based graph is created for the
powerful classification of data into intrusion or normal. The exploratory
outcomes demonstrate that the presented technique outperforms the existing
techniques regarding different performance examinations like execution time,
accuracy, false-positive rate, and their intrusion detection model increases the
detection rate and decreases the false rate.
View
... Recently, due to the rapid development of mobile Internet, attacks on
Internet-connected devices are gradually increasing. Thus, many scholars have a
strong interest in the research of intrusion detection systems and good
detection results have been achieved [4]. ...

MFVT:An Anomaly Traffic Detection Method Merging Feature Fusion Network and
Vision Transformer Architecture
Preprint
Full-text available
 * Sep 2021

 * Ming Li
 * Dezhi Han
 * Dun Li
 * Han Liu
 * Chin- Chen Chang

Network intrusion detection, which takes the extraction and analysis of network
traffic features as the main method, plays a vital role in network security
protection. The current network traffic feature extraction and analysis for
network intrusion detection mostly uses deep learning algorithms. Currently,
deep learning requires a lot of training resources, and have weak processing
capabilities for imbalanced data sets. In this paper, a deep learning model
(MFVT) based on feature fusion network and Vision Transformer architecture is
proposed, to which improves the processing ability of imbalanced data sets and
reduces the sample data resources needed for training. Besides, to improve the
traditional raw traffic features extraction methods, a new raw traffic features
extraction method (CRP) is proposed, the CPR uses PCA algorithm to reduce all
the processed digital traffic features to the specified dimension. On the IDS
2017 dataset and the IDS 2012 dataset, the ablation experiments show that the
performance of the proposed MFVT model is significantly better than other
network intrusion detection models, and the detection accuracy can reach the
state-of-the-art level. And, When MFVT model is combined with CRP algorithm, the
detection accuracy is further improved to 99.99%.
View
... Phishing: Phishing is a fraud type attack [32] that tries to delude the
users by impersonating someone else, e.g., a company, which the user (victim)
trusts. Often in these attacks, the attackers send an email that seems to be
legitimate but it is not. ...

Intrusion Detection in Critical Infrastructures: A Literature Review
Article
Full-text available
 * Aug 2021

 * Fountas Panagiotis
 * Kouskouras Taxiarxchis
 * Kranas Georgios
 * Leandros Maglaras
 * Mohamed Amine Ferrag

Over the years, the digitization of all aspects of life in modern societies is
considered an acquired advantage. However, like the terrestrial world, the
digital world is not perfect and many dangers and threats are present. In the
present work, we conduct a systematic review on the methods of network detection
and cyber attacks that can take place in a critical infrastructure. As is shown,
the implementation of a system that learns from the system behavior (machine
learning), on multiple levels and spots any diversity, is one of the most
effective solutions.
View
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