Enrico Tronci

Bio: Enrico Tronci is an academic researcher from Sapienza University of Rome. The author has contributed to research in topics: Model checking & Formal verification. The author has an hindex of 22, co-authored 136 publications receiving 1569 citations. Previous affiliations of Enrico Tronci include Intel & Energy Institute.

System Level Formal Verification via Model Checking Driven Simulation

Abstract: We show how by combining Explicit Model Checking techniques and simulation it is possible to effectively carry out (bounded) System Level Formal Verification of large Hybrid Systems such as those defined using model-based tools like Simulink. We use an explicit model checker (namely, CMurphi) to generate all possible (finite horizon) simulation scenarios and then optimise the simulation of such scenarios by exploiting the ability of simulators to save and restore visited states. We show feasibility of our approach by presenting experimental results on the verification of the fuel control system example in the Simulink distribution. To the best of our knowledge this is the first time that (exhaustive) verification has been carried out for hybrid systems of such a size.

Residential Demand Management Using Individualized Demand Aware Price Policies

Abstract: This paper presents a novel approach to demand side management (DSM), using an “individualized” price policy, where each end user receives a separate electricity pricing scheme designed to incentivize demand management in order to optimally manage flexible demands. These pricing schemes have the objective of reducing the peaks in overall system demand in such a way that the average electricity price each individual user receives is non-discriminatory. It is shown in this paper that this approach has a number of advantages and benefits compared to traditional DSM approaches. The “demand aware price policy” approach outlined in this paper exploits the knowledge, or demand-awareness, obtained from advanced metering infrastructure. The presented analysis includes a detailed case study of an existing European distribution network where DSM trial data was available from the residential end-users.

Exploiting transition locality in automatic verification of finite-state concurrent systems

Abstract: In this paper we show that statistical properties of the transition graph of a system to be verified can be exploited to improve memory or time performances of verification algorithms. We show experimentally that protocols exhibit transition locality. That is, with respect to levels of a breadth-first state space exploration, state transitions tend to be between states belonging to close levels of the transition graph. We support our claim by measuring transition locality for the set of protocols included in the Mur? verifier distribution . We present a cache-based verification algorithm that exploits transition locality to decrease memory usage and a disk-based verification algorithm that exploits transition locality to decrease disk read accesses, thus reducing the time overhead due to disk usage. Both algorithms have been implemented within the Mur? verifier. Our experimental results show that our cache-based algorithm can typically save more than 40% of memory with an average time penalty of about 50% when using (Mur?) bit compression and 100% when using bit compression and hash compaction, whereas our disk-based verification algorithm is typically more than ten times faster than a previously proposed disk-based verification algorithm and, even when using 10% of the memory needed to complete verification, it is only between 40 and 530% (300% on average) slower than (RAM) Mur? with enough memory to complete the verification task at hand. Using just 300 MB of memory our disk-based Mur? was able to complete verification of a protocol with about 109 reachable states. This would require more than 5 GB of memory using standard Mur? .

Electric Power System Anomaly Detection Using Neural Networks

Abstract: The aim of this work is to propose an approach to monitor and protect Electric Power System by learning normal system behaviour at substations level, and raising an alarm signal when an abnormal status is detected; the problem is addressed by the use of autoassociative neural networks, reading substation measures. Experimental results show that, through the proposed approach, neural networks can be used to learn parameters underlaying system behaviour, and their output processed to detecting anomalies due to hijacking of measures, changes in the power network topology (i.e. transmission lines breaking) and unexpected power demand trend.

System Level Formal Verification via Distributed Multi-core Hardware in the Loop Simulation

Abstract: The goal of System Level Formal Verification (SLFV) is to show system correctness notwithstanding uncontrollable events (such as: faults, variation in system parameters, external inputs, etc). Hardware In the Loop Simulation (HILS) based SLFV attains such a goal by considering exhaustively all relevant simulation scenarios. We present a distributed multi-core algorithm for HILS-based SLFV. Our experimental results on the Fuel Control System example in the Simulink distribution show that by using 64 machines with an 8 core processor each we can complete the SLFV activity in about 27 hours whereas a sequential approach would require more than 200 days. To the best of our knowledge this is the first time that a distributed multi-core algorithm for HILS-based SLFV is presented.

Principles of Model Checking

Abstract: Our growing dependence on increasingly complex computer and software systems necessitates the development of formalisms, techniques, and tools for assessing functional properties of these systems. One such technique that has emerged in the last twenty years is model checking, which systematically (and automatically) checks whether a model of a given system satisfies a desired property such as deadlock freedom, invariants, and request-response properties. This automated technique for verification and debugging has developed into a mature and widely used approach with many applications. Principles of Model Checking offers a comprehensive introduction to model checking that is not only a text suitable for classroom use but also a valuable reference for researchers and practitioners in the field. The book begins with the basic principles for modeling concurrent and communicating systems, introduces different classes of properties (including safety and liveness), presents the notion of fairness, and provides automata-based algorithms for these properties. It introduces the temporal logics LTL and CTL, compares them, and covers algorithms for verifying these logics, discussing real-time systems as well as systems subject to random phenomena. Separate chapters treat such efficiency-improving techniques as abstraction and symbolic manipulation. The book includes an extensive set of examples (most of which run through several chapters) and a complete set of basic results accompanied by detailed proofs. Each chapter concludes with a summary, bibliographic notes, and an extensive list of exercises of both practical and theoretical nature.

The sector bound approach to quantized feedback control | NOVA. The University of Newcastle's Digital Repository

Abstract: This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.

Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction

Abstract: This paper proposes to use autoencoders with nonlinear dimensionality reduction in the anomaly detection task. The authors apply dimensionality reduction by using an autoencoder onto both artificial data and real data, and compare it with linear PCA and kernel PCA to clarify its property. The artificial data is generated from Lorenz system, and the real data is the spacecrafts' telemetry data. This paper demonstrates that autoencoders are able to detect subtle anomalies which linear PCA fails. Also, autoencoders can increase their accuracy by extending them to denoising autoenconders. Moreover, autoencoders can be useful as nonlinear techniques without complex computation as kernel PCA requires. Finaly, the authors examine the learned features in the hidden layer of autoencoders, and present that autoencoders learn the normal state properly and activate differently with anomalous input.

A survey of covert channels and countermeasures in computer network protocols

Abstract: Covert channels are used for the secret transfer of information. Encryption only protects communication from being decoded by unauthorised parties, whereas covert channels aim to hide the very existence of the communication. Initially, covert channels were identified as a security threat on monolithic systems i.e. mainframes. More recently focus has shifted towards covert channels in computer network protocols. The huge amount of data and vast number of different protocols in the Internet seems ideal as a high-bandwidth vehicle for covert communication. This article is a survey of the existing techniques for creating covert channels in widely deployed network and application protocols. We also give an overview of common methods for their detection, elimination, and capacity limitation, required to improve security in future computer networks.