237 research outputs found
Reachability in Concurrent Uninterpreted Programs
We study the safety verification (reachability problem) for concurrent programs with uninterpreted functions/relations. By extending the notion of coherence, recently identified for sequential programs, to concurrent programs, we show that reachability in coherent concurrent programs under various scheduling restrictions is decidable by a reduction to multistack pushdown automata, and establish precise complexity bounds for them. We also prove that the coherence restriction for these various scheduling restrictions is itself a decidable property
An Intelligent Multicriteria Model for Diagnosing Dementia in People Infected with Human Immunodeficiency Virus
Hybrid models to detect dementia based on Machine Learning can provide accurate
diagnoses in individuals with neurological disorders and cognitive complications caused by Human
Immunodeficiency Virus (HIV) infection. This study proposes a hybrid approach, using Machine
Learning algorithms associated with the multicriteria method of Verbal Decision Analysis (VDA).
Dementia, which affects many HIV-infected individuals, refers to neurodevelopmental and mental
disorders. Some manuals standardize the information used in the correct detection of neurological
disorders with cognitive complications. Among the most common manuals used are the DSM-5
(Diagnostic and Statistical Manual of Mental Disorders, 5th edition) of the American Psychiatric
Association and the International Classification of Diseases, 10th edition (ICD-10)—both published
byWorld Health Organization (WHO). The model is designed to explore the predictive of specific
data. Furthermore, a well-defined database data set improves and optimizes the diagnostic models
sought in the research.info:eu-repo/semantics/publishedVersio
S2TD: a Separation Logic Verifier that Supports Reasoning of the Absence and Presence of Bugs
Heap-manipulating programs are known to be challenging to reason about. We
present a novel verifier for heap-manipulating programs called S2TD, which
encodes programs systematically in the form of Constrained Horn Clauses (CHC)
using a novel extension of separation logic (SL) with recursive predicates and
dangling predicates. S2TD actively explores cyclic proofs to address the path
explosion problem. S2TD differentiates itself from existing CHC-based verifiers
by focusing on heap-manipulating programs and employing cyclic proof to
efficiently verify or falsify them with counterexamples. Compared with existing
SL-based verifiers, S2TD precisely specifies the heaps of de-allocated pointers
to avoid false positives in reasoning about the presence of bugs. S2TD has been
evaluated using a comprehensive set of benchmark programs from the SV-COMP
repository. The results show that S2TD is more effective than state-of-art
program verifiers and is more efficient than most of them.Comment: 24 page
Automated Verification of Complete Specification with Shape Inference
Ph.DDOCTOR OF PHILOSOPH
Identifying and Detecting Attacks in Industrial Control Systems
The integrity of industrial control systems (ICS) found in utilities, oil and natural gas pipelines, manufacturing plants and transportation is critical to national wellbeing and security. Such systems depend on hundreds of field devices to manage and monitor a physical process. Previously, these devices were specific to ICS but they are now being replaced by general purpose computing technologies and, increasingly, these are being augmented with Internet of Things (IoT) nodes. Whilst there are benefits to this approach in terms of cost and flexibility, it has attracted a wider community of adversaries. These include those with significant domain knowledge, such as those responsible for attacks on Iran’s Nuclear Facilities, a Steel Mill in Germany, and Ukraine’s power grid; however, non specialist attackers are becoming increasingly interested in the physical damage it is possible to cause. At the same time, the approach increases the number and range of vulnerabilities to which ICS are subject; regrettably, conventional techniques for analysing such a large attack space are inadequate, a cause of major national concern. In this thesis we introduce a generalisable approach based on evolutionary multiobjective algorithms to assist in identifying vulnerabilities in complex heterogeneous ICS systems. This is both challenging and an area that is currently lacking research. Our approach has been to review the security of currently deployed ICS systems, and then to make use of an internationally recognised ICS simulation testbed for experiments, assuming that the attacking community largely lack specific ICS knowledge. Using the simulator, we identified vulnerabilities in individual components and then made use of these to generate attacks. A defence against these attacks in the form of novel intrusion detection systems were developed, based on a range of machine learning models. Finally, this was further subject to attacks created using the evolutionary multiobjective algorithms, demonstrating, for the first time, the feasibility of creating sophisticated attacks against a well-protected adversary using automated mechanisms
- …