Cyber Security And The Evolution Of Intrusion Detection Systems

Internet of energy (IoE) is the natural evolution of Smart Grid incorporating the paradigm of internet of things (IoT). This complicated environment has a lot of threats and vulnerabilities, so the security challenges are very complex and specialized. This chapter contains a compilation of the main threats, vulnerabilities, and attacks that can occur in the IoE environment and the critical structure of the electrical grid. The objective is to show the best cybersecurity practices that can support maintaining a safe, reliable, and available electrical network complying with the requirements of availability, integrity, and confidentially of the information. The study includes review of countermeasures, standards, and specialized intrusion detection systems, as mechanisms to solve security problems in IoE. Better understanding of security challenges and solutions in the IoE can be the light on future research work for IoE security.

Download Full-text

A Compressive Compilation of Cyber Security for Internet of Energy (IoE)

Cyber Security of Industrial Control Systems in the Future Internet Environment - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-2910-2.ch013 ◽

2020 ◽

pp. 267-294

Author(s):

Gustavo Arroyo-Figueroa ◽

Isai Rojas-Gonzalez ◽

José Alberto Hernández-Aguilar

Keyword(s):

Internet Of Things ◽

Intrusion Detection ◽

Cyber Security ◽

Research Work ◽

Intrusion Detection Systems ◽

Electrical Network ◽

Future Research ◽

Electrical Grid ◽

Detection Systems ◽

Security Challenges

Internet of energy (IoE) is the natural evolution of Smart Grid incorporating the paradigm of internet of things (IoT). This complicated environment has a lot of threats and vulnerabilities, so the security challenges are very complex and specialized. This chapter contains a compilation of the main threats, vulnerabilities, and attacks that can occur in the IoE environment and the critical structure of the electrical grid. The objective is to show the best cybersecurity practices that can support maintaining a safe, reliable, and available electrical network complying with the requirements of availability, integrity, and confidentially of the information. The study includes review of countermeasures, standards, and specialized intrusion detection systems, as mechanisms to solve security problems in IoE. Better understanding of security challenges and solutions in the IoE can be the light on future research work for IoE security.

Download Full-text

Designing Light Weight Intrusion Detection Systems

Machine Learning ◽

10.4018/978-1-60960-818-7.ch213 ◽

2012 ◽

pp. 304-317

Author(s):

Václav Snášel ◽

Jan Platoš ◽

Pavel Krömer ◽

Ajith Abraham

Keyword(s):

Intrusion Detection ◽

Computational Intelligence ◽

Matrix Factorization ◽

Cyber Security ◽

Detection System ◽

Factorization Method ◽

Performance Comparison ◽

Intrusion Detection Systems ◽

Light Weight ◽

Detection Systems

Recently cyber security has emerged as an established discipline for computer systems and infrastructures with a focus on protection of valuable information stored on those systems from adversaries who want to obtain, corrupt, damage, destroy or prohibit access to it. An Intrusion Detection System (IDS) is a program that analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. This chapter presents some of the challenges in designing efficient ad light weight intrusion detection systems, which could provide high accuracy, low false alarm rate and reduced number of features. Finally, the authors present the Non-negative matrix factorization method for detecting real attacks and the performance comparison with other computational intelligence techniques.

Download Full-text

Designing Light Weight Intrusion Detection Systems

Socioeconomic and Legal Implications of Electronic Intrusion ◽

10.4018/978-1-60566-204-6.ch013 ◽

2009 ◽

pp. 216-229 ◽

Cited By ~ 1

Author(s):

Václav Snášel ◽

Jan Platoš ◽

Pavel Krömer ◽

Ajith Abraham

Keyword(s):

Intrusion Detection ◽

Computational Intelligence ◽

Matrix Factorization ◽

Cyber Security ◽

Detection System ◽

Factorization Method ◽

Performance Comparison ◽

Intrusion Detection Systems ◽

Light Weight ◽

Detection Systems

Recently cyber security has emerged as an established discipline for computer systems and infrastructures with a focus on protection of valuable information stored on those systems from adversaries who want to obtain, corrupt, damage, destroy or prohibit access to it. An Intrusion Detection System (IDS) is a program that analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. This chapter presents some of the challenges in designing efficient ad light weight intrusion detection systems, which could provide high accuracy, low false alarm rate and reduced number of features. Finally, the authors present the Non-negative matrix factorization method for detecting real attacks and the performance comparison with other computational intelligence techniques.

Download Full-text

Machine Learning Approach for Intrusion Detection Systems as a Cyber Security Strategy for Small and Medium Enterprises

WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS ◽

10.37394/23207.2022.19.43 ◽

2022 ◽

Vol 19 ◽

pp. 474-480

Author(s):

Nevila Baci ◽

Kreshnik Vukatana ◽

Marius Baci

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Cyber Security ◽

Small And Medium Enterprises ◽

Classification Model ◽

Supervised Machine Learning ◽

Intrusion Detection Systems ◽

Detection Systems ◽

Original Dataset ◽

Medium Enterprises

Small and medium enterprises (SMEs) are businesses that account for a large percentage of the economy in many countries, but they lack cyber security. The present study examines different supervised machine learning methods with a focus on intrusion detection systems (IDSs) that will help in improving SMEs’ security. The algorithms that are tested through a real dataset, are Naïve Bayes, Sequential minimal optimization (SMO), C4.5 decision tree, and Random Forest. The experiments are run using the Waikato Environment for Knowledge Analyses (WEKA) 3.8.4 tools and the metrics used to evaluate the results were: accuracy, false-positive rate (FPR), and total time to train and build a classification model. The results obtained from the original dataset with 130 features show a high value of accuracy, but the computation time to build the classification model was notably high for the cases of C4.5 (1 hr. and 20 mins) and SMO algorithm (4 hrs. and 20 mins). the Information Gain (IG) method was used and the result was impressive. The time needed to train the model was reduced in the order of a few minutes and the accuracy was high (above 95%). In the end, challenges that SMEs can have for choosing an IDS such as lack of scalability and autonomic self-adaptation, can be solved by using a correct methodology with machine learning techniques.

Download Full-text

A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems

Journal of Artificial Intelligence and Soft Computing Research ◽

10.2478/jaiscr-2021-0008 ◽

2021 ◽

Vol 11 (2) ◽

pp. 111-142

Author(s):

Nassima Bougueroua ◽

Smaine Mazouzi ◽

Mohamed Belaoued ◽

Noureddine Seddari ◽

Abdelouahid Derhab ◽

...

Keyword(s):

Intrusion Detection ◽

Cyber Security ◽

Intrusion Detection Systems ◽

Multi Agent Systems ◽

Research Issues ◽

Detection Systems ◽

Multi Agent ◽

Collaborative Intrusion Detection ◽

Complex Phenomena ◽

General Architecture

Abstract Multi-Agent Systems (MAS) have been widely used in many areas like modeling and simulation of complex phenomena, and distributed problem solving. Likewise, MAS have been used in cyber-security, to build more efficient Intrusion Detection Systems (IDS), namely Collaborative Intrusion Detection Systems (CIDS). This work presents a taxonomy for classifying the methods used to design intrusion detection systems, and how such methods were used alongside with MAS in order to build IDS that are deployed in distributed environments, resulting in the emergence of CIDS. The proposed taxonomy, consists of three parts: 1) general architecture of CIDS, 2) the used agent technology, and 3) decision techniques, in which used technologies are presented. The proposed taxonomy reviews and classifies the most relevant works in this topic and highlights open research issues in view of recent and emerging threats. Thus, this work provides a good insight regarding past, current, and future solutions for CIDS, and helps both researchers and professionals design more effective solutions.

Download Full-text

An Intrusion Detection System- Techniques and Algorithms of Machine and Deep Learning

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j7541.0891020 ◽

2020 ◽

Vol 9 (10) ◽

pp. 370-376

Keyword(s):

Deep Learning ◽

Intrusion Detection ◽

Cyber Security ◽

Intrusion Detection System ◽

Detection System ◽

Intrusion Detection Systems ◽

Learning Technology ◽

Detection Systems ◽

Vital Component ◽

Open Issues

Computer networks are vital component for today’s development of science and technology, due to the emergence of limitless communication pattern and exponential count of network devices cyber security become crucial for this world to secure the most valuable data or information which is more vulnerable for attack by the intruders. New pattern of intrusion and attacks are created in everyday manner by potential intruders and they should be identified by efficient Intrusion Detection Systems (IDSs), also proper counter should be applied for. The paper surveys about the discussion of various machine /deep learning technology and algorithm related to Intrusion Detection System (IDSs) for the real time performance of the system. Finally the literature review investigated gives some open issues which will need to be considered for further research in the field of network security.

Download Full-text

Analyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems

Sustainability ◽

10.3390/su132212337 ◽

2021 ◽

Vol 13 (22) ◽

pp. 12337

Author(s):

Abdullah Alharbi ◽

Adil Hussain Seh ◽

Wael Alosaimi ◽

Hashem Alyami ◽

Alka Agrawal ◽

...

Keyword(s):

Machine Learning ◽

Decision Making ◽

Intrusion Detection ◽

Cyber Security ◽

Security And Privacy ◽

Intrusion Detection Systems ◽

Critical Function ◽

Security Issues ◽

Detection Systems ◽

The Impact

Machine learning (ML) is one of the dominating technologies practiced in both the industrial and academic domains throughout the world. ML algorithms can examine the threats and respond to intrusions and security incidents swiftly in an instinctive way. It plays a critical function in providing a proactive security mechanism in the cybersecurity domain. Cybersecurity ensures the real time protection of information, information systems, and networks from intruders. Several security and privacy reports have cited that there has been a rapid increase in both the frequency and the number of cybersecurity breaches in the last decade. Information security has been compromised by intruders at an alarming rate. Anomaly detection, phishing page identification, software vulnerability diagnosis, malware identification, and denial of services attacks are the main cyber-security issues that demand effective solutions. Researchers and experts have been practicing different approaches to address the current cybersecurity issues and challenges. However, in this research endeavor, our objective is to make an idealness assessment of machine learning-based intrusion detection systems (IDS) under the hesitant fuzzy (HF) conditions, using a multi-criteria decision making (MCDM)-based analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal-solutions (TOPSIS). Hesitant fuzzy sets are useful for addressing decision-making situations in which experts must overcome the reluctance to make a conclusion. The proposed research project would assist the machine learning practitioners and cybersecurity specialists in identifying, selecting, and prioritizing cybersecurity-related attributes for intrusion detection systems, and build more ideal and effective intrusion detection systems.

Download Full-text