Event Detection Through Differential Pattern Mining in Cyber-Physical Systems

False data injection (FDI) attack is a hot topic in cyber-physical systems (CPSs). Attackers inject bad data into sensors or return false data to the controller to cause the inaccurate state estimation. Although there exists many detection approaches, such as bad data detector (BDD), sequence pattern mining, and machine learning methods, a smart attacker still can inject perfectly false data to go undetected. In this paper, we focus on the advanced false data injection (AFDI) attack and its detection method. An AFDI attack refers to the attack where a malicious entity accurately and successively changes sensory data, making the normal system state continuously evaluated as other legal system states, causing wrong outflow of controllers. The attack can lead to an automatic and long-term system failure/performance degradation. We first depict the AFDI attack model and analyze limitations of existing detectors for detecting AFDI. Second, we develop an approach based on machine learning, which utilizes the k-Nearest Neighbor (KNN) technique and heterogeneous data including sensory data and system commands to implement a classifier for detecting AFDI attacks. Finally, simulation experiments are given to demonstrate AFDI attack impact and the effectiveness of the proposed method for detecting AFDI attacks.

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In-network generalized trustworthy data collection for event detection in cyber-physical systems

PeerJ Computer Science ◽

10.7717/peerj-cs.504 ◽

2021 ◽

Vol 7 ◽

pp. e504

Author(s):

Hafiz Ur Rahman ◽

Guojun Wang ◽

Md Zakirul Alam Bhuiyan ◽

Jianer Chen

Keyword(s):

Data Collection ◽

Event Detection ◽

Resource Constraints ◽

Main Idea ◽

Region Of Interest ◽

Gray Code ◽

Cyber Physical Systems ◽

Logic Device ◽

Physical Systems ◽

Faulty Sensors

Sensors in Cyber-Physical Systems (CPS) are typically used to collect various aspects of the region of interest and transmit the data towards upstream nodes for further processing. However, data collection in CPS is often unreliable due to severe resource constraints (e.g., bandwidth and energy), environmental impacts (e.g., equipment faults and noises), and security concerns. Besides, detecting an event through the aggregation in CPS can be intricate and untrustworthy if the sensor's data is not validated during data acquisition, before transmission, and before aggregation. This paper introduces In-network Generalized Trustworthy Data Collection (IGTDC) framework for event detection in CPS. This framework facilitates reliable data for aggregation at the edge of CPS. The main idea of IGTDC is to enable a sensor's module to examine locally whether the event's acquired data is trustworthy before transmitting towards the upstream nodes. It further validates whether the received data can be trusted or not before data aggregation at the sink node. Additionally, IGTDC helps to identify faulty sensors. For reliable event detection, we use collaborative IoT tactics, gate-level modeling with Verilog User Defined Primitive (UDP), and Programmable Logic Device (PLD) to ensure that the event's acquired data is reliable before transmitting towards the upstream nodes. We employ Gray code in gate-level modeling. It helps to ensure that the received data is reliable. Gray code also helps to distinguish a faulty sensor. Through simulation and extensive performance analysis, we demonstrate that the collected data in the IGTDC framework is reliable and can be used in the majority of CPS applications.

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Emerging Cyber-Physical Systems : An Overview

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit183862 ◽

2018 ◽

pp. 306-316

Author(s):

Okolie S.O. ◽

Kuyoro S.O. ◽

Ohwo O. B

Keyword(s):

Health Care ◽

Economic Benefit ◽

Physical World ◽

Cyber Physical Systems ◽

Cyber Physical System ◽

Economic Sectors ◽

Strategic Research ◽

Physical Systems ◽

Security Issues ◽

Wide Range

Cyber-Physical Systems (CPS) will revolutionize how humans relate with the physical world around us. Many grand challenges await the economically vital domains of transportation, health-care, manufacturing, agriculture, energy, defence, aerospace and buildings. Exploration of these potentialities around space and time would create applications which would affect societal and economic benefit. This paper looks into the concept of emerging Cyber-Physical system, applications and security issues in sustaining development in various economic sectors; outlining a set of strategic Research and Development opportunities that should be accosted, so as to allow upgraded CPS to attain their potential and provide a wide range of societal advantages in the future.

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Resource-aware Automotive Control Systems Design: A Cyber-Physical Systems Approach

10.1561/9781680832396 ◽

2016 ◽

Author(s):

Chang Wanli ◽

Chakraborty Samarjit

Keyword(s):

Control Systems ◽

Systems Approach ◽

Systems Design ◽

Cyber Physical Systems ◽

Automotive Control ◽

Physical Systems ◽

Control Systems Design ◽

Resource Aware

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

Vanderbilt Undergraduate Research Journal ◽

10.15695/vurj.v9i0.3765 ◽

2013 ◽

Vol 9 ◽

Cited By ~ 4

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.

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