Concept Drift Analysis for Improving Anomaly Detection Systems in Cybersecurity

The distributed deployment of wireless sensor networks (WSNs) makes the network more convenient, but it also causes more hidden security hazards that are difficult to be solved. For example, the unprotected deployment of sensors makes distributed anomaly detection systems for WSNs more vulnerable to internal attacks, and the limited computing resources of WSNs hinder the construction of a trusted environment. In recent years, the widely observed blockchain technology has shown the potential to strengthen the security of the Internet of Things. Therefore, we propose a blockchain-based ensemble anomaly detection (BCEAD), which stores the model of a typical anomaly detection algorithm (isolated forest) in the blockchain for distributed anomaly detection in WSNs. By constructing a suitable block structure and consensus mechanism, the global model for detection can iteratively update to enhance detection performance. Moreover, the blockchain guarantees the trust environment of the network, making the detection algorithm resistant to internal attacks. Finally, compared with similar schemes, in terms of performance, cost, etc., the results prove that BCEAD performs better.

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A data correlation method for anomaly detection systems using regression relations

2009 First International Conference on Future Information Networks ◽

10.1109/icfin.2009.5339579 ◽

2009 ◽

Cited By ~ 2

Author(s):

Amin Hassanzadeh ◽

Babak Sadeghiyan

Keyword(s):

Anomaly Detection ◽

Correlation Method ◽

Data Correlation ◽

Detection Systems ◽

Regression Relations

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Detecting Intrusions in Cyber-Physical Systems of Smart Cities

Secure Cyber-Physical Systems for Smart Cities - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-7189-6.ch004 ◽

2019 ◽

pp. 74-102 ◽

Cited By ~ 2

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.

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CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Mining

Algorithms ◽

10.3390/a13070161 ◽

2020 ◽

Vol 13 (7) ◽

pp. 161

Author(s):

Ghada Elkhawaga ◽

Mervat Abuelkheir ◽

Sherif I. Barakat ◽

Alaa M. Riad ◽

Manfred Reichert

Keyword(s):

Systematic Review ◽

Business Process ◽

Continuous Monitoring ◽

Business Processes ◽

Process Mining ◽

Concept Drift ◽

Business Environments ◽

Drift Analysis ◽

Comprehensive Framework ◽

Over Time

Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (SLR) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts.

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