scholarly journals A Model for Auditing Smart Intrusion Detection Systems (IDSs) and Log Analyzers in Cyber Physical Systems (CPSs)

2021 ◽  
Author(s):  
Joshua Ojo Nehinbe
Keyword(s):  
Intrusion Detection ◽  
Global Economy ◽  
Cyber Physical Systems ◽  
Security And Privacy ◽  
Intrusion Detection Systems ◽  
Physical Systems ◽  
Detection Systems ◽  
C Programming ◽  
Key Issues ◽  
Synthetic Datasets

Suitable models that auditors can adopt to concurrently audit smart Intrusion Detection Systems (IDSs) and log analyzers in Cyber Physical Systems (CPSs) that are also founded on sound empirical claims are scarce. Recently, post-intrusion studies on the resilience of the above mechanisms and prevalence of intrusions in the above domains have shown that certain intrusions that can reduce the performance of smart IDSs can equally overwhelm log analyzers such that both mechanisms can gradually dwindle and suddenly stop working. Studies have also shown that several components of Cyber Physical Systems have unusual vulnerabilities. These key issues often increase cyber threats on data security and privacy of resources that many users can receive over Internet of a Thing (IoT). Dreadful intrusions on physical and computational components of Cyber Physical Systems can cause systemic reduction in global economy, quality of digital services and continue usage of smart toolkits that should support risk assessments and identification of strategies of intruders. Unfortunately, pragmatic studies on how to reduce the above problems are grossly inadequate. This chapter uses alerts from Snort and C++ programming language to practically explore the above issues and further proposes a feasible model for operators and researchers to lessen the above problems. Evaluation with real and synthetic datasets demonstrates that the capabilities and resilience of smart Intrusion Detection Systems (IDSs) to safeguard Cyber Physical Systems (CPSs) can be improved given a framework to facilitate audit of smart IDSs and log analyzers in Cyberspaces and knowledge of the variability in the lengths and components of alerts warned by Smart Intrusion Detection Systems (IDSs).

Detecting Intrusions in Cyber-Physical Systems of Smart Cities

2019 ◽  
pp. 74-102 ◽  
Author(s):  
Ismail Butun ◽  
Patrik Österberg
Keyword(s):  
Anomaly Detection ◽  
Smart Cities ◽  
Cyber Physical Systems ◽  
Security And Privacy ◽  
Security Vulnerabilities ◽  
Physical Systems ◽  
Detection Systems ◽  
Comprehensive Survey ◽  
Privacy Violation ◽  
Detection Schemes

Interfacing the smart cities with cyber-physical systems (CPSs) improves cyber infrastructures while introducing security vulnerabilities that may lead to severe problems such as system failure, privacy violation, and/or issues related to data integrity if security and privacy are not addressed properly. In order for the CPSs of smart cities to be designed with proactive intelligence against such vulnerabilities, anomaly detection approaches need to be employed. This chapter will provide a brief overview of the security vulnerabilities in CPSs of smart cities. Following a thorough discussion on the applicability of conventional anomaly detection schemes in CPSs of smart cities, possible adoption of distributed anomaly detection systems by CPSs of smart cities will be discussed along with a comprehensive survey of the state of the art. The chapter will discuss challenges in tailoring appropriate anomaly detection schemes for CPSs of smart cities and provide insights into future directions for the researchers working in this field.

scholarly journals Data Security and Privacy Preserving in Intrusion Detection Systems using Block-Chain Technology

2019 ◽  
Vol 8 (4) ◽  
pp. 7167-7170
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Data Security ◽  
Heterogeneous System ◽  
Security Protocols ◽  
Security And Privacy ◽  
Intrusion Detection Systems ◽  
Detection Systems ◽  
Mixed Network ◽  
Block Chain

Nowadays, for the purpose of investigate in any field; internet of things (IoT) occupies the main themes since datum that IoT has communications between different things, articles and gadgets. In numerous fields, for example, designs, security, protocols, communications and so forth. There have been many improvements and advancements are accomplished by these associations. The primary point of IoT is building a maintained security among objects and furthermore guarantee the talented communications among them by utilizing different sorts of applications. Mixed Network designs play a fundamental job in present day world correspondence that is for all intentions and determinations all interchanges and mystery asset exchanges are broadly relying upon the heterogeneous system engineering. Be that as it may, the current Intrusion location frameworks have various obstructions in framework effectiveness, security, protection, adaptability and versatility. Hence, Block chain innovation has been worried to give the security of information and save the protection of the information by forestalling the unapproved get to. It can bear the charge of high security, without trade of mean worth. The square chain guarantees high protection from assaults. The work proposed is another push to progress the current safety of the diverse design, and it tends to be most valuable in verified IoT.

A Survey on Network Intrusion Detection Using Deep Generative Networks for Cyber-Physical Systems

2021 ◽  
pp. 137-159
Author(s):  
Srikanth Yadav M. ◽  
Kalpana R.
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Critical Infrastructure ◽  
Cyber Physical Systems ◽  
Industrial Applications ◽  
Network Intrusion Detection ◽  
Primary Concern ◽  
Physical Systems ◽  
Detection Systems ◽  
Network Intrusion

In the present computing world, network intrusion detection systems are playing a vital part in detecting malicious activities, and enormous attention has been given to deep learning from several years. During the past few years, cyber-physical systems (CPSs) have become ubiquitous in modern critical infrastructure and industrial applications. Safety is therefore a primary concern. Because of the success of deep learning (DL) in several domains, DL-based CPS security applications have been developed in the last few years. However, despite the wide range of efforts to use DL to ensure safety for CPSs. The major challenges in front of the research community are developing an efficient and reliable ID that is capable of handling a large amount of data, in analyzing the changing behavioral patterns of attacks in real-time. The work presented in this manuscript reviews the various deep learning generative methodologies and their performance in detecting anomalies in CPSs. The metrics accuracy, precision, recall, and F1-score are used to measure the performance.

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

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.

Security of Cyber-Physical Systems: A Generalized Algorithm for Intrusion Detection and Determining Security Robustness of Cyber Physical Systems using Logical Truth Tables

2013 ◽  
Vol 9 ◽  
Author(s):  
Curtis G. Northcutt
Keyword(s):  
Intrusion Detection ◽  
Cyber Security ◽  
Logical Truth ◽  
Cyber Physical Systems ◽  
Security Model ◽  
Security Measures ◽  
Physical Systems ◽  
Multiple Signal ◽  
Security Attack ◽  
Truth Tables

The recent proliferation of embedded cyber components in modern physical systems [1] has generated a variety of new security risks which threaten not only cyberspace, but our physical environment as well. Whereas earlier security threats resided primarily in cyberspace, the increasing marriage of digital technology with mechanical systems in cyber-physical systems (CPS), suggests the need for more advanced generalized CPS security measures. To address this problem, in this paper we consider the first step toward an improved security model: detecting the security attack. Using logical truth tables, we have developed a generalized algorithm for intrusion detection in CPS for systems which can be defined over discrete set of valued states. Additionally, a robustness algorithm is given which determines the level of security of a discrete-valued CPS against varying combinations of multiple signal alterations. These algorithms, when coupled with encryption keys which disallow multiple signal alteration, provide for a generalized security methodology for both cyber-security and cyber-physical systems.

Disadvantages of Modern Intrusion Detection Systems

2006 ◽  
Vol 65 (10) ◽  
pp. 929-936
Author(s):  
A. V. Agranovskiy ◽  
S. A. Repalov ◽  
R. A. Khadi ◽  
M. B. Yakubets
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Detection Systems
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