Формальная модель функционирования процесса в операционной системе

The article presents a formal model of the functioning of the process in the operating system, created on the basis of a subject-object approach to the separation of the main elements of the operating system. A feature of the presented model is a high-level abstraction of the interaction between the operating system processes and resources, which allows applying the obtained results to a wide range of similar systems. The use of this model is necessary for carrying out the transition from the real world object (process) to a formal model to take into account the significant properties of the behavior of the process both during the static analysis phase of a binary executable file and the dynamic phase of monitoring its implementation. The system of safe execution of code is an extension of the composition of such approaches to the detection of malicious software as the application of the formal verification method «Model checking» and the use of machine safety to monitor the implementation of the studied program. This system allows using in corporate information and computer networks only such software, reliability of which is confirmed by a formal mathematical proof and continuous monitoring of its execution.В статье представлена формальная модель функционирования процесса в операционной системе, построенная на основе применения субъектно-объектного подхода к разделению основных элементов операционной системы. Особенностью представленной модели является высокоуровневая абстракция описания взаимодействия процесса с ресурсами операционной системы, что позволяет применить полученные на ее основе результаты к широкому классу аналогичных систем. Применение данной модели необходимо для совершения перехода от реального процесса к его формальной модели, позволяющей учитывать значимые свойства поведения процесса как на статическом этапе анализа бинарного исполняемого файла, так и на динамическом этапе контроля за его выполнением. Предложена структура системы безопасного исполнения программного кода, являющаяся расширенной композицией таких подходов к обнаружению вредоносного программного обеспечения, как применение метода формальной верификации «Model checking» и использования автомата безопасности для контроля за выполнением исследуемой программы. Применение данной системы позволит использовать в корпоративных информационно-вычислительных сетях только программное обеспечение, уровень доверия к которому подтверждается формальным математическим доказательством и непрерывным контролем за его функционированием

Efficient CTL Verification via Horn Constraints Solving

The use of temporal logics has long been recognised as a fundamental approach to the formal specification and verification of reactive systems. In this paper, we take on the problem of automatically verifying a temporal property, given by a CTL formula, for a given (possibly infinite-state) program. We propose a method based on encoding the problem as a set of Horn constraints. The method takes a program, modeled as a transition system, and a property given by a CTL formula as input. It first generates a set of forall-exists quantified Horn constraints and well-foundedness constraints by exploiting the syntactic structure of the CTL formula. Then, the generated set of constraints are solved by applying an off-the-shelf Horn constraints solving engine. The program is said to satisfy the property if and only if the generated set of constraints has a solution. We demonstrate the practical promises of the method by applying it on a set of challenging examples. Although our method is based on a generic Horn constraint solving engine, it is able to outperform state-of-art methods specialised for CTL verification.Comment: In Proceedings HCVS2016, arXiv:1607.0403

Proving Nontermination via safety

We show how the problem of nontermination proving can be reduced to a question of underapproximation search guided by a safety prover. This reduction leads to new nontermination proving implementation strategies based on existing tools for safety proving. Our preliminary implementation beats existing tools. Furthermore, our approach leads to easy support for programs with unbounded nondeterminism

Extending Temporal Logics with Data Variable Quantifications

Although data values are available in almost every computer system, reasoning about them is a challenging task due to the huge data size or even infinite data domains. Temporal logics are the well-known specification formalisms for reactive and concurrent systems. Various extensions of temporal logics have been proposed to reason about data values, mostly in the last decade. Among them, one natural idea is to extend temporal logics with variable quantifications ranging over an infinite data domain. In this paper, we focus on the variable extensions of two widely used temporal logics, Linear Temporal Logic (LTL) and Computation Tree Logic (CTL). Grumberg, Kupferman and Sheinvald recently investigated the extension of LTL with variable quantifications. They defined the extension as formulas in the prenex normal form, that is, all the variable quantifications precede the LTL formulas. Our goal in this paper is to do a relatively complete investigation on this topic. For this purpose, we define the extensions of LTL and CTL by allowing arbitrary nestings of variable quantifications, Boolean and temporal operators (the resulting logics are called respectively variable-LTL, in brief VLTL, and variable-CTL, in brief VCTL), and identify the decidability frontiers of both the satisfiability and model checking problem. In particular, we obtain the following results: 1) Existential variable quantifiers or one single universal quantifier in the beginning already entails undecidability for the satisfiability problem of both VLTL and VCTL, 2) If only existential path quantifiers are used in VCTL, then the satisfiability problem is decidable, no matter which variable quantifiers are available. 3) For VLTL formulas with one single universal variable quantifier in the beginning, if the occurrences of the non-parameterized atomic propositions are guarded by the positive occurrences of the quantified variable, then its satisfiability problem becomes decidable. Based on these results of the satisfiability problem, we deduce the (un)decidability results of the model checking problem

Mining Malware Specifications through Static Reachability Analysis

International audienceAbstract. The number of malicious software (malware) is growing out of control. Syntactic signature based detection cannot cope with such growth and manual construction of malware signature databases needs to be replaced by computer learning based approaches. Currently, a single modern signature capturing the semantics of a malicious behavior can be used to replace an arbitrarily large number of old-fashioned syntactical signatures. However teaching computers to learn such behaviors is a challenge. Existing work relies on dynamic analysis to extract malicious behaviors, but such technique does not guarantee the coverage of all behaviors. To sidestep this limitation we show how to learn malware signatures using static reachability analysis. The idea is to model binary programs using pushdown systems (that can be used to model the stack operations occurring during the binary code execution), use reachability analysis to extract behaviors in the form of trees, and use subtrees that are common among the trees extracted from a training set of malware files as signatures. To detect malware we propose to use a tree automaton to compactly store malicious behavior trees and check if any of the subtrees extracted from the file under analysis is malicious. Experimental data shows that our approach can be used to learn signatures from a training set of malware files and use them to detect a test set of malware that is 5 times the size of the training set

Adaptive synchronisation of pushdown automata

We introduce the notion of adaptive synchronisation for pushdown automata, in which there is an external observer who has no knowledge about the current state of the pushdown automaton, but can observe the contents of the stack. The observer would then like to decide if it is possible to bring the automaton from any state into some predetermined state by giving inputs to it in an adaptive manner, i.e., the next input letter to be given can depend on how the contents of the stack changed after the current input letter. We show that for non-deterministic pushdown automata, this problem is 2-EXPTIME-complete and for deterministic pushdown automata, we show EXPTIME-completeness. To prove the lower bounds, we first introduce (different variants of) subset-synchronisation and show that these problems are polynomial-time equivalent with the adaptive synchronisation problem. We then prove hardness results for the subset-synchronisation problems. For proving the upper bounds, we consider the problem of deciding if a given alternating pushdown system has an accepting run with at most k leaves and we provide an n^O(k²) time algorithm for this problem

Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection

A well-known curse of computer security research is that it often produces systems that, while technically sound, fail operationally. To overcome this curse, the community generally seeks to assess proposed systems under a variety of settings in order to make explicit every potential bias. In this respect, recently, research achievements on machine learning based malware detection are being considered for thorough evaluation by the community. Such an effort of comprehensive evaluation supposes first and foremost the possibility to perform an independent reproduction study in order to sharpen evaluations presented by approaches’ authors. The question Can published approaches actually be reproduced? thus becomes paramount despite the little interest such mundane and practical aspects seem to attract in the malware detection field. In this paper, we attempt a complete reproduction of five Android Malware Detectors from the literature and discuss to what extent they are “reproducible”. Notably, we provide insights on the implications around the guesswork that may be required to finalise a working implementation. Finally, we discuss how barriers to reproduction could be lifted, and how the malware detection field would benefit from stronger reproducibility standards—like many various fields already have

A Survey of Practical Formal Methods for Security

In today's world, critical infrastructure is often controlled by computing systems. This introduces new risks for cyber attacks, which can compromise the security and disrupt the functionality of these systems. It is therefore necessary to build such systems with strong guarantees of resiliency against cyber attacks. One way to achieve this level of assurance is using formal verification, which provides proofs of system compliance with desired cyber security properties. The use of Formal Methods (FM) in aspects of cyber security and safety-critical systems are reviewed in this article. We split FM into the three main classes: theorem proving, model checking, and lightweight FM. To allow the different uses of FM to be compared, we define a common set of terms. We further develop categories based on the type of computing system FM are applied in. Solutions in each class and category are presented, discussed, compared, and summarised. We describe historical highlights and developments and present a state-of-the-art review in the area of FM in cyber security. This review is presented from the point of view of FM practitioners and researchers, commenting on the trends in each of the classes and categories. This is achieved by considering all types of FM, several types of security and safety-critical systems, and by structuring the taxonomy accordingly. The article hence provides a comprehensive overview of FM and techniques available to system designers of security-critical systems, simplifying the process of choosing the right tool for the task. The article concludes by summarising the discussion of the review, focusing on best practices, challenges, general future trends, and directions of research within this field

Une approche sémantique de détection de maliciel Android basée sur la vérification de modèles et l'apprentissage automatique

Le nombre croissant de logiciels malveillants Android s’accompagne d’une préoccupation profonde liée aux problèmes de la sécurité des terminaux mobiles. Les enjeux deviennent sans conteste de plus en plus importants, suscitant ainsi beaucoup d’attention de la part de la communauté des chercheurs. En outre, la prolifération des logiciels malveillants va de pair avec la sophistication et la complexité de ces derniers. En effet, les logiciels malveillants plus élaborés, tels que les maliciels polymorphes et métamorphiques, utilisent des techniques d’obscurcissement du code pour créer de nouvelles variantes qui préservent la sémantique du code original tout en modifiant sa syntaxe, échappant ainsi aux méthodes de détection usuelles. L’ambition de notre recherche est la proposition d’une approche utilisant les méthodes formelles et l’apprentissage automatique pour la détection des maliciels sur la plateforme Android. L’approche adoptée combine l’analyse statique et l’apprentissage automatique. En effet, à partir des applications Android en format APK, nous visons l’extraction d’un modèle décrivant de manière non ambiguë le comportement de ces dernières. Le langage de spécification formelle choisi est LNT. En se basant sur le modèle généré, les comportements malicieux exprimés en logique temporelle sont vérifiés à l’aide d’un vérificateur de modèle. Ces propriétés temporelles sont utilisées comme caractéristiques par un algorithme d’apprentissage automatique pour classifier les applications Android.The ever-increasing number of Android malware is accompanied by a deep concern about security issues in the mobile ecosystem. Unquestionably, Android malware detection has received much attention in the research community and therefore it becomes a crucial aspect of software security. Actually, malware proliferation goes hand in hand with the sophistication and complexity of malware. To illustrate, more elaborated malware like polymorphic and metamorphic malware, make use of code obfuscation techniques to build new variants that preserve the semantics of the original code but modify it’s syntax and thus escape the usual detection methods. In the present work, we propose a model-checking based approach that combines static analysis and machine learning. Mainly, from a given Android application we extract an abstract model expressed in terms of LNT, a process algebra language. Afterwards, security related Android behaviours specified by temporal logic formulas are checked against this model, the satisfaction of a specific formula is considered as a feature, finally machine learning algorithms are used to classify the application as malicious or not

Behaviour-based Virus Analysis and Detection

Every day, the growing number of viruses causes major damage to computer systems, which many antivirus products have been developed to protect. Regrettably, existing antivirus products do not provide a full solution to the problems associated with viruses. One of the main reasons for this is that these products typically use signature-based detection, so that the rapid growth in the number of viruses means that many signatures have to be added to their signature databases each day. These signatures then have to be stored in the computer system, where they consume increasing memory space. Moreover, the large database will also affect the speed of searching for signatures, and, hence, affect the performance of the system. As the number of viruses continues to grow, ever more space will be needed in the future. There is thus an urgent need for a novel and robust detection technique. One of the most encouraging recent developments in virus research is the use of formulae, which provides alternatives to classic virus detection methods. The proposed research uses temporal logic and behaviour-based detection to detect viruses. Interval Temporal Logic (ITL) will be used to generate virus specifications, properties and formulae based on the analysis of the behaviour of computer viruses, in order to detect them. Tempura, which is the executable subset of ITL, will be used to check whether a good or bad behaviour occurs with the help of ITL description and system traces. The process will also use AnaTempura, an integrated workbench tool for ITL that supports our system specifications. AnaTempura will offer validation and verification of the ITL specifications and provide runtime testing of these specifications