arXiv subjects

Faezeh Farivar

Publications and source records attributed to Faezeh Farivar.

4 recordsLinked to original sources

arXiv2026

Enzyme-Substrate Complex Formation Modulates Diffusion-Driven Patterning In Metabolic Pathways

Spatial organization in metabolic pathways can arise from the interplay between enzymatic reaction kinetics and diffusion-driven instabilities. In this work we investigate how reversible enzyme--substrate binding influences pattern formation in a two-step metabolic pathway. Starting from a mechanistic description in which the substrate reversibly binds to the first enzyme before catalytic conversion, we formulate a three-species reaction--diffusion system that explicitly incorporates the enzyme--substrate complex. We first analyse the homogeneous dynamics and determine the unique steady state of the kinetic system. Exploiting the separation of time scales between the rapid binding kinetics and the slower evolution of metabolite concentrations, we derive a reduced two-variable model using a quasi-steady-state approximation for the enzyme-substrate complex. This reduction preserves the essential nonlinear coupling between catalytic reactions and spatial transport. Linear stability and weakly nonlinear analysis reveal conditions for diffusion-driven (Turing) instability and show that reversible enzyme binding significantly modifies the location and extent of the instability region compared to models with effective kinetics. Numerical simulations confirm the analytical predictions and demonstrate how enzyme-substrate interactions reshape pattern selection and slow the emergence of spatial heterogeneity. These results provide a mechanistic link between enzyme binding kinetics, diffusion-driven pattern formation, and mesoscale metabolic organization. The proposed framework offers a tractable approach for studying spatial patterning in enzymatic networks and may help explain the emergence of structured biochemical domains such as those associated with liquid--liquid phase separation.

q-bio.MN↗

arXiv2020

Detection of Anomalies and Faults in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System

Industry 4.0 will make manufacturing processes smarter but this smartness requires more environmental awareness, which in case of Industrial Internet of Things, is realized by the help of sensors. This article is about industrial pharmaceutical systems and more specifically, water purification systems. Purified water which has certain conductivity is an important ingredient in many pharmaceutical products. Almost every pharmaceutical company has a water purifying unit as a part of its interdependent systems. Early detection of faults right at the edge can significantly decrease maintenance costs and improve safety and output quality, and as a result, lead to the production of better medicines. In this paper, with the help of a few sensors and data mining approaches, an anomaly detection system is built for CHRIST Osmotron water purifier. This is a practical research with real-world data collected from SinaDarou Labs Co. Data collection was done by using six sensors over two-week intervals before and after system overhaul. This gave us normal and faulty operation samples. Given the data, we propose two anomaly detection approaches to build up our edge fault detection system. The first approach is based on supervised learning and data mining e.g. by support vector machines. However, since we cannot collect all possible faults data, an anomaly detection approach is proposed based on normal system identification which models the system components by artificial neural networks. Extensive experiments are conducted with the dataset generated in this study to show the accuracy of the data-driven and model-based anomaly detection methods.

cs.CR↗

arXiv2020

On the Security of Networked Control Systems in Smart Vehicle and its Adaptive Cruise Control

With the benefits of Internet of Vehicles (IoV) paradigm, come along unprecedented security challenges. Among many applications of inter-connected systems, vehicular networks and smart cars are examples that are already rolled out. Smart vehicles not only have networks connecting their internal components e.g. via Controller Area Network (CAN) bus, but also are connected to the outside world through road side units and other vehicles. In some cases, the internal and external network packets pass through the same hardware and are merely isolated by software defined rules. Any misconfiguration opens a window for the hackers to intrude into vehicles' internal components e.g. central lock system, Engine Control Unit (ECU), Anti-lock Braking System (ABS) or Adaptive Cruise Control (ACC) system. Compromise of any of these can lead to disastrous outcomes. In this paper, we study the security of smart vehicles' adaptive cruise control systems in the presence of covert attacks. We define two covert/stealth attacks in the context of cruise control and propose a novel intrusion detection and compensation method to disclose and respond to such attacks. More precisely, we focus on the covert cyber attacks that compromise the integrity of cruise controller and employ a neural network identifier in the IDS engine to estimate the system output dynamically and compare it against the ACC output. If any anomaly is detected, an embedded substitute controller kicks in and takes over the control. We conducted extensive experiments in MATLAB to evaluate the effectiveness of the proposed scheme in a simulated environment.

cs.CR↗

arXiv2020

A Machine Learning-based Approach to Build Zero False-Positive IPSs for Industrial IoT and CPS with a Case Study on Power Grids Security

Intrusion Prevention Systems (IPS), have long been the first layer of defense against malicious attacks. Most sensitive systems employ instances of them (e.g. Firewalls) to secure the network perimeter and filter out attacks or unwanted traffic. A firewall, similar to classifiers, has a boundary to decide which traffic sample is normal and which one is not. This boundary is defined by configuration and is managed by a set of rules which occasionally might also filter normal traffic by mistake. However, for some applications, any interruption of the normal operation is not tolerable e.g. in power plants, water distribution systems, gas or oil pipelines, etc. In this paper, we design a learning firewall that receives labelled samples and configures itself automatically by writing preventive rules in a conservative way that avoids false alarms. We design a new family of classifiers, called $\mathfrak{z}$-classifiers, that unlike the traditional ones which merely target accuracy, rely on zero false-positive as the metric for decision making. First, we analytically show why naive modification of current classifiers like SVM does not yield acceptable results and then, propose a generic iterative algorithm to accomplish this goal. We use the proposed classifier with CART at its heart to build a firewall for a Power Grid Monitoring System. To further evaluate the algorithm, we additionally test it on KDD CUP'99 dataset. The results confirm the effectiveness of our approach.

cs.CR↗