Likelihood Ratio-Based Scheduler for Secure Detection in Cyber Physical Systems

Ding, Jian-Ya; You, Keyou; Song, Sejun; Wu, Cheng

doi:10.1109/tcns.2017.2670326

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…With heuristic optimization techniques and fuzzy logic, a hybrid optimization algorithm for schedule of appliances in home is suggested [15]. In the sensor, a probability ratio-dependent scheduling is built to smartly pick informative transmission sensors measurement along a moderate rate of transmission limit for networks that are secure [16]. For cyber-physical system (CPS) along a flexible hierarchical time-division multiple access, an arbitrated networked control system (ANCS) along with schedule actions or arbitration networks in control system suggested by experts [17].…”

Section: Introductionmentioning

confidence: 99%

Algorithm fuzzy scheduling (AFS) for realtime jobs on multiprocessor systems

Holagundi¹,

Ashwathsetty²,

Basthikodi

2022

IJEECS

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The computing in Real-time is rapidly focusing much developments in technologies so that the real-time jobs are to be scheduled and executed on computing systems in particular time frame. The scheduling and load balancing techniques in distributed systems face numerous challenges because of lack of centralized strategy to dispatch the jobs in multiprocessors systems. In this work, we propose an Algorithm Fuzzy Scheduling (AFS) for real-time jobs that includes of Arrival time, Deadline and Computation time as the scheduling parameters of input. The approach AFS is analyzed and compared with Existing Fuzzy Algorithm (EFA) model for evaluation of performances from the outcome of the simulation. The jobs are scheduled on multiprocessor at higher system load by making use of fuzzy mechanisms in the algorithms. The experimental results prove that the proposed AFS achieves a better performance comparatively to EFA at various system load factors with respect to mean turnaroundtime, mean response time and count of missed deadlines. This is the initial phase of the algorithm, that will be enhanced to consider a greater number of parameters to be associated with jobs for better decision making and to investigate the scope for algorithm level parallelism.

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Section: Introductionmentioning

confidence: 99%

Algorithm fuzzy scheduling (AFS) for realtime jobs on multiprocessor systems

Holagundi¹,

Ashwathsetty²,

Basthikodi

2022

IJEECS

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Hybrid deep architecture for intrusion detection in cyber‐physical system: An optimization‐based approach

Arumugam,

Paul,

Issac

et al. 2024

Adaptive Control & Signal

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SummaryIntrustion Detection System (IDS) refers to the gear or software that monitors a network or system for malicious activity or policy violations. Periodically, the system records any intrusion action or breach, which frequently modifies the administrator. Cyber Physical System (CPS) is particularly called as networked connected system, in which the system components are spatially distributed and integrated via the communication network. The control mechanism ensures computation significance; however, the system does affect attacks. Researchers are trying to handle this issue via the existing anomaly datasets. In this way, this paper follows an intrusion detection system under three major stages including extraction of features, selection of feature, and detection. The primary stage is the extraction of Statistical features like standard deviation, mean, mode, variance, and median, as well as higher‐order statistical features like moment, percentile, improved correlation, kurtosis, mutual information, skewness, flow‐based features, and information gain‐based features. The curse of dimensionality becomes a significant problem in this scenario, so it is crucial to choose the right features. Improved Linear Discriminant Analysis (LDA) is utilized to choose the right features. The selected features are subjected to a Hybrid classifier for final detection. Here, models like CNN (Convolutional Neural Network) and Bi‐GRU (Bidirectional Gated Recurrent Unit) are combined. A new Bernoulli Map Estimated Arithmetic Optimization Algorithm (BMEAOA) is added to train the system by adjusting the ideal weights of the two classifiers, leading to improved detection outcomes. Ultimately, the effectiveness is assessed in comparison to the other traditional techniques.

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RETRACTED ARTICLE: Adaptive neural fuzzy inference system-based scheduler for cyber–physical system

Padmajothi

Iqbal

2020

Soft Comput

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Likelihood Ratio-Based Scheduler for Secure Detection in Cyber Physical Systems

Cited by 4 publications

References 30 publications

Algorithm fuzzy scheduling (AFS) for realtime jobs on multiprocessor systems

Algorithm fuzzy scheduling (AFS) for realtime jobs on multiprocessor systems

Hybrid deep architecture for intrusion detection in cyber‐physical system: An optimization‐based approach

RETRACTED ARTICLE: Adaptive neural fuzzy inference system-based scheduler for cyber–physical system

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