Ondrej Linda

Neural Network based Intrusion Detection System for critical infrastructures

Resiliency and security in control systems such as SCADA and Nuclear plants in todays world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM - Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms - the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

Uncertainty-Robust Design of Interval Type-2 Fuzzy Logic Controller for Delta Parallel Robot

Type-2 Fuzzy Logic Controllers (T2 FLCs) have been recently applied in many engineering areas. While understanding the control potentials of T2 FLCs can still be considered an open question researchers, commonly claim superiority of T2 FLCs based on a limited exploration of the space of design parameters. The contribution of this work is based on a problem-driven design of uncertainty-robust Interval T2 (IT2) FLCs. The presented methodology starts with a baseline optimized T1 FLC. Next, a group of IT2 FLCs is designed using partially dependent approach by symmetrically blurring the membership functions around the original T1 fuzzy sets. This constrained design space allows for its systematic exploration and analysis. The performance of the designed controllers was evaluated on delta parallel robot hardware under two kinds of commonly encountered uncertainties: i) sensory noise and ii) uncertain system parameters. The experimental results showed that IT2 FLCs provide improved control performance against T1 FLCs when appropriate design of IT2 fuzzy sets is performed. In addition, it was demonstrated that excessive amount of “type-2 fuzziness” in the IT2 FLC design leads to rapid performance degradation.

General Type-2 Fuzzy C-Means Algorithm for Uncertain Fuzzy Clustering

Pattern recognition in real-world data is subject to various sources of uncertainty that should be appropriately managed. The focus of this paper is the management of uncertainty associated with parameters of fuzzy clustering algorithms. Type-2 fuzzy sets (T2 FSs) have received increased research interest over the past decade, primarily due to their potential to model various uncertainties. However, because of the computational intensity of the processing of general T2 fuzzy sets (GT2 FSs), only their constrained version, i.e., the interval T2 (IT2) FSs, were typically used. Fortunately, the recently introduced concepts of α-planes and zSlices allow for efficient representation and computation with GT2 FSs. Following this recent development, this paper presents a novel approach for uncertain fuzzy clustering using the general type-2 fuzzy C-means (GT2 FCM) algorithm. The proposed method builds on top of the previously published IT2 FCM algorithm, which is extended via the α- planes representation theorem. The fuzzifier parameter of the FCM algorithm can be expressed using linguistic terms such as “small” or “high,” which are modeled as T1 FSs. This linguistic fuzzifier value is then used to construct the GT2 FCM cluster membership functions. The linguistic uncertainty is transformed into uncertain fuzzy positions of the extracted clusters. The GT2 FCM algorithm was found to balance the performance of T1 FCM algorithms in various uncertain pattern recognition tasks and to provide increased robustness in situations where noisy or insufficient training data are present.

Interval Type-2 fuzzy voter design for fault tolerant systems

A voting scheme constitutes an essential component of many fault tolerant systems. Two types of voters are commonly used in applications of real-valued systems: the inexact majority and the amalgamating voters. The inexact majority voter effectively isolates erroneous modules and is capable of reporting benign outputs when a significant disagreement is detected. However, an application specific voter threshold must be provided. On the other hand, amalgamating voter, such as the weighted average voter, reduces the influence of faulty modules by averaging the input values together. Unlike the majority voters, amalgamating voters are not capable of producing benign outputs. In the past, a Type-1 (T1) fuzzy voting scheme was introduced, allowing for both smooth amalgamation of voter inputs and effective signalization of benign outputs. The presented paper proposes an extension to the fuzzy voting scheme via incorporating Interval Type-2 (IT2) fuzzy logic. The IT2 fuzzy logic allows for an improved handling of uncertain assumptions about the distributions of noisy and erroneous inputs which are essential for correct design of the fuzzy voting scheme. The proposed voter design features robust performance when the uncertainty assumptions dynamically change over time. The IT2 fuzzy voter architecture was compared against the average voter, inexact majority voter, and the T1 fuzzy voter using a refined experimental harness. The reported results demonstrate improved availability, safety and reliability of the presented IT2 fuzzy voting scheme.

Self-Organizing Fuzzy Haptic Teleoperation of Mobile Robot Using Sparse Sonar Data

Mobile robot teleoperation has been used in many areas of industrial automation, such as explosives disposal, nuclear waste manipulation, freight handling, or transportation. Here, the commonly provided audio-visual feedback often resulted in an inadequate perception of the remote environment. Haptic augmentation was shown to improve and positively enhance the control of the mobile robot. This paper presents novel self-organizing fuzzy adaptive mapping algorithm (SOFAMap) for a haptic teleoperation of mobile robots. The SOFAMap algorithm was specifically developed for a mobile robot with a rotational sonar sensory system, constituting an alternative to a traditionally used multisonar array. The main contributions of this paper are the following: 1) development of a specific self-organizing environment mapping structure inspired by the growing neural gas algorithm; 2) incorporating a fuzzy controller into the algorithm to adapt to robots motion; and 3) resolving typical issues such as sensor noise, communication time delay, and low sampling rate. The experimental testing was performed in both virtual environment and on a real robotic platform, consisting of a Lego NXT mobile robot and a Novint Falcon 3-DOF haptic interface. The results showed that a high-fidelity haptic feedback can be successfully generated using a simpler and more affordable rotational sonar sensory system, as opposed to the typical multisonar array. Furthermore, it was demonstrated that the SOFAMap algorithm improves the operators awareness of unstructured environments, making it applicable to wide range of mobile robot teleoperation systems.

Mining Building Energy Management System Data Using Fuzzy Anomaly Detection and Linguistic Descriptions

Building Energy Management Systems (BEMSs) are essential components of modern buildings that are responsible for minimizing energy consumption while maintaining occupant comfort. However, since indoor environment is dependent on many uncertain criteria, performance of BEMS can be suboptimal at times. Unfortunately, complexity of BEMSs, large amount of data, and interrelations between data can make identifying these suboptimal behaviors difficult. This paper proposes a novel Fuzzy Anomaly Detection and Linguistic Description (Fuzzy-ADLD)-based method for improving the understandability of BEMS behavior for improved state-awareness. The presented method is composed of two main parts: 1) detection of anomalous BEMS behavior; and 2) linguistic representation of BEMS behavior. The first part utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to build a model of normal BEMS behavior. The second part of the presented method computes the most relevant linguistic description of the identified anomalies. The presented Fuzzy-ADLD method was applied to real-world BEMS system and compared against a traditional alarm-based BEMS. Six different scenarios were tested, and the presented Fuzzy-ADLD method identified anomalous behavior either as fast as or faster (an hour or more) than the alarm based BEMS. Furthermore, the Fuzzy-ADLD method identified cases that were missed by the alarm-based system, thus demonstrating potential for increased state-awareness of abnormal building behavior.

Fuzzy Force-Feedback Augmentation for Manual Control of Multirobot System

Multirobot systems represent an enticing area of research with numerous real-world applications. Teams of multiple robots can achieve tasks that are more difficult or even impossible for a single robot, e.g., environment exploration, search and rescue, or surveillance operations. In a previous work, the authors developed a system for the single-operator manual control of a multirobot system. However, such teleoperation systems commonly suffer from inadequate perception of the remote environment. This paper extends the previously presented work by adding a fuzzy force-feedback (FFF) augmentation for the manual control of a multirobot system. The FFF augmentation delivers additional information to the operator. Moreover, it guides the operator toward the smooth control of the robotic group. The force feedback was generated by a system of fuzzy controllers monitoring the state of the multirobot group. The performance of the system was evaluated in a virtual environment, and the recorded forces were explored in various scenarios. The force-feedback augmentation demonstrated the following improvements: 1) operators increased obstacle awareness and 2) improved maneuvering performance.

Comparative analysis of Type-1 and Type-2 fuzzy control in context of learning behaviors for mobile robotics

Dynamic uncertainties, manifested as input noise or variable environment conditions, are an inherent part of most real world control applications. Recently, several researchers demonstrated that Type-2 Fuzzy Logic Controllers (T2 FLC) are able to cope with such uncertainty and reduce its negative effects. However, the design and optimization of T2 FLC and its subsequent unbiased comparison to T1 FLC are still an open question. This paper presents a comparative analysis of interval T2 (IT2) and T1 FLCs in the context of learning behaviors for mobile robotics. First, a T1 FLC is optimized using the Particle Swarm Optimization algorithm to mimic a wall-following behavior performed by an operator. Next, an IT2 FLC is constructed by symmetrically blurring the fuzzy sets of the original T1 FLC. The performance of the fuzzy controllers is compared using a wall-following sonar-equipped mobile robot in both noise-free and noisy environments. It is experimentally demonstrated that the IT2 FLC can cope better with dynamic uncertainties in the sensory inputs due to the softening and smoothing of the output control surface by the IT2 fuzzy sets. However, the IT2 FLC is outperformed by the T1 FLC when sudden and fast response of the controller is required, such as in the case of turning around corners. Those results suggest the difficulties of symmetrical blurring of T1 FLC (although commonly used) as a design methodology for obtaining the architecture of an IT2 FLC.

Monotone Centroid Flow Algorithm for Type Reduction of General Type-2 Fuzzy Sets

Recently, type-2 fuzzy logic systems (T2 FLSs) have received increased research attention due to their potential to model and cope with the dynamic uncertainties ubiquitous in many engineering applications. However, because of the complex nature and the computational intensity of the inference process, only the constrained version of T2 FLSs, i.e., the interval T2 FLSs, was typically used. Fortunately, the very recently introduced concepts of α-planes and zSlices allow for efficient representation, as well as a computationally fast inference process, with general T2 (GT2) FLSs. This paper addresses the type-reduction phase in GT2 FLSs, using GT2 fuzzy sets (FSs) represented in the α-plane framework. The monotone property of centroids of a set of α-planes is derived and leveraged toward developing a simple to implement but fast algorithm for type reduction of GT2 FSs - i.e., the monotone centroid flow (MCF) algorithm. When compared with the centroid flow (CF) algorithm, which was previously developed by Zhai and Mendel, the MCF algorithm features the following advantages. 1) The MCF algorithm computes numerically identical centroid as the Karnik-Mendel (KM) iterative algorithms, unlike the approximated centroid which is obtained with the CF algorithm; 2) the MCF algorithm is faster than the CF algorithm, as well as the independent application of the KM algorithms; 3) the MCF algorithm is easy to implement, unlike the CF algorithm, which requires computation of the derivatives of the centroid; and 4) the MCF algorithm completely eliminates the need to apply the KM iterative procedure to any α-planes of the GT2 FS. The performance of the algorithm is presented on benchmark problems and compared with other type-reduction techniques that are available in the literature.

Fuzzy logic based anomaly detection for embedded network security cyber sensor

Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule base modeling the normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.