Backing and Undercutting in Defeasible Logic Programming

Relating defeasible and normal logic programming through transformation properties

We revise some results in Argumentation-based Logic Programming under the umbrella of Satisfiability Modulo CHR (SMCHR), specifically considering Defeasible Logic Programming (DeLP). Strict and defeasible rules in DeLP can be cast to SMCHR rules, which act as conflict “disentanglers” and implement the Theory part. At the same time, we inherit several built-in theory solvers, as SAT, unification, or linear arithmetic ones, which implement the Satisfiability-modulo part. Moreover, we show how to deal with possibilistic extensions of DeLP, i.e., Possibilistic-DeLP, where certainty scores describing the possibility of some events are associated with rules.

In this article we introduce the basics for understanding the mechanisms of Argument Theory Change. In particular we reify it using Defeasible Logic Programming. In this formalism, knowledge bases are represented through defeasible logic programs. The main change operation we deflne over a defeasible logic program is a special kind of revision that inserts a new argument and then modifles the resulting program seeking for the argument's warrant. Since the notion of argument refers to a set of defeasible rules, we generalize this technique in order to handle extended arguments, i.e., arguments containing also strict rules. Hence, revision using extended arguments allows us to consider program-independent arguments, which brings about new issues. A single notion of minimal change is analyzed, which refers to keep the contents of the program as much as possible. Finally, a brief discussion about the relation between our approach and the basic theory of belief revision is exposed, along with a description of other possible (more complex) minimal change principles.

In an agent system that needs to operate in a real world, the problem of maintaining a consistent world model in the face of unreliable, incomplete and inconsistent sensory data should be solved. In this paper, we present an approach that addresses this problem by applying an argumentation-based scene interpretation framework for accurately modelling and representing the observations and beliefs of an agent. Our approach is based on temporal and probabilistic defeasible logic programming for reasoning. The performance of our approach is evaluated on simulation experiments in the Stage Robot Simulator. We also show that our approach is applicable to real world scenarios with an autonomous Pioneer 3-AT robot.

A major challenge in cyber-threat analysis is combining information from different sources to find the person or the group responsible for the cyber-attack. It is one of the most important technical and policy challenges in cyber-security. The lack of ground truth for an individual responsible for an attack has limited previous studies. In this paper, we take a first step towards overcoming this limitation by building a dataset from the capture-the-flag event held at DEFCON, and propose an argumentation model based on a formal reasoning framework called DeLP (Defeasible Logic Programming) designed to aid an analyst in attributing a cyber-attack. We build models from latent variables to reduce the search space of culprits (attackers), and show that this reduction significantly improves the performance of classification-based approaches from 37% to 62% in identifying the attacker.

Robustness of keystroke-dynamics based biometrics against synthetic forgeries

Security and Privacy Aspect of Cyber Physical Systems

The information available to intelligent agents is often uncertain, inconsistent and incomplete. It is then crucially important to develop logics for uncertainty to achieve different complex reasoning tasks. Uncertainty is present in many applications and may be represented using different frameworks: plausibility orderings, probability theory frameworks, argumentation systems, etc. This special issue gathers five contributions that cover different aspects of reasoning under uncertainty using non classical logics. Four of the five papers are fully revised and extended versions of contributions initially presented at the Fifth International Conference on Scalable Uncertainty Management (SUM’11) The two first papers concern extensions of logic-based programming languages to deal with uncertain information. The paper by Pere Pardo, Lluis Godo entitled “t-DeLP: an argumentation-based Temporal Defeasible Logic Programming framework” deals with handling temporal information using defeasible logic. The proposed framework, called t-DeLP, allows to extend argumentation-based defeasible logic in order to represent temporal processes. The idea is to associate temporal information to literals and to define temporal logic programs are sets of basic temporal facts and (strict or defeasible) durative rules. Concepts of argumentation systems, such as the one of undefeated arguments, are redefined in this t-DeLP framework. A procedure for determining a set of warranted literals is proposed. The paper also discusses rational properties satisfied by the t-DeLP framework. The paper by Georg Gottlob,Thomas Lukasiewicz, Maria Vanina Martinez and Gerardo I. Simari entitled “Query Answering under Probabilistic Uncertainty in Datalog+/ Ontologies” concerns an extension of Datalog+/− to deal with uncertainty. The authors use Markov Logic Networks to provide a probabilistic semantics for the probabilistic Datalog+/−. They provide scalable and sound algorithms for

The Industrial Control System (ICS) is a public facility that provides services to lots of users; thus, its security has always been a critical factor in measuring its availability. Recently, a new type of attack on ICS has occurred frequently, which realizes the extortion of users by invading the information domain and destroying the physical domain. However, due to the diversity and unavailability of an ICS control logic, the targets of such attacks are usually limited to PCs and servers, leaving more disruptive attack methods unexplored. To contribute more possible attack methods to strengthen the immunity of ICS, in this paper, we propose a novel ransomware attack method named Industrial Control System Automatic Ransomware Constructor (ICS-ARC). Compared to existing ICS ransomware, ICS-ARC can automatically generate an International Electrotechnical Commission (IEC) compliant payload to compromise the Programmable Logic Controller (PLC) without a pre-known control logic, dramatically reducing adversary requirements and leaving room for error. To evaluate the attack capability of ICS-ARC, we built a tap water treatment system as the simulation experiment target for verification. The experimental results determine that ICS-ARC can automatically generate malicious code without the control logic and complete the attack against target PLCs. In addition, to assist the related research on future attacks and defenses, we present the statistical results and corresponding analysis of PLC based on Shodan.

Software defined networks (SDN) remain a hot research field as it provides controllable networking operations. The SDN controller can be treated as the operating system of the SDN model and it holds the responsibility of performing different networking applications. Despite the benefits of SDN, security remains a challenging problem. At the same time, distributed denial of services (DDoS) is a typical attack on SDN owing to centralized architecture, especially at the control layer of the SDN. This article develops a new Cat Swarm Optimization with Fuzzy Rule Base Classification (CSO-FRBCC) model for cybersecurity in SDN. The presented CSO-FRBCC model intends to effectually categorize the occurrence of DDoS attacks in SDN. To achieve this, the CSO-FRBCC model primarily pre-processes the input data to transform it to a uniform format. Besides, the CSO-FRBCC model employs FRBCC classifier for the recognition and classification of intrusions. Moreover, the parameter optimization of the FRBCC classification model is adjusted by the use of cat swarm optimization (CSO) algorithm which results in improved performance. A comprehensive set of simulations were carried out on benchmark dataset and the results highlighted the enhanced outcomes of the CSO-FRBCC model over the other recent approaches.

This work addresses the problem of providing explanation capabilities to an argumentation system. Explanation in defeasible argumentation is an important, and yet undeveloped field in the area. Therefore, we move in this direction by defining a concrete argument system with explanation facilities.We consider the structures that provide information on the warrant status of a literal. Our focus is put on argumentation systems based on a dialectical proof procedure, therefore we study dialectical explanations. Although arguments represent a form of explanation for a literal, we study the complete set of dialectical trees that justifies the warrant status of a literal, since this set has proved to be a useful tool to comprehend, analyze, develop, and debug argumentation systems.KeywordsBelief RevisionWarrant StatusStrong NegationDialectical ProcessDefeat RelationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In previous works, a recursive warrant semantics for Defeasible Logic Programming extended with levels of possibilistic uncertainty for defeasible rules was introduced. The resulting argumentation framework, called RP-DeLP, is based on a general notion of collective (non-binary) conflict among arguments allowing to ensure direct and indirect consistency properties with respect to the strict knowledge. In this paper we propose an efficient and scalable implementation of an interpreter for RP-DeLP using Answer Set Programming (ASP) encodings for the two main queries of the system: looking for valid arguments and finding collective conflicts among arguments. We perform an experimental evaluation of our ASP approach and we compare the results with a previously proposed SAT based approach. The results show that with ASP we are able to scale up to bigger problem instances.

In this paper we define a recursive semantics for warrant in a general defeasible argumentation framework by formalizing a notion of collective (non-binary) conflict among arguments. This allows us to ensure direct and indirect consistency (in the sense of Caminada and Amgoud) without distinguishing between direct and indirect conflicts. Then, the general defeasible argumentation framework is extended by allowing to attach levels of preference to defeasible knowledge items and by providing a level-wise definition of warranted and blocked conclusions. Finally, we formalize the warrant recursive semantics for the particular framework of Possibilistic Defeasible Logic Programming, characterize the unique output program property and design an efficient algorithm for computing warranted conclusions in polynomial space.

In real-world applications, knowledge bases consisting of all the available information for a specific domain, along with the current state of affairs, will typically contain contradictory data, coming from different sources, as well as data with varying degrees of uncertainty attached. An important aspect of the effort associated with maintaining such knowledge bases is deciding what information is no longer useful; pieces of information may be outdated; may come from sources that have recently been discovered to be of low quality; or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing these basic issues. The formalism is capable of handling contradictory and uncertain data, and we study non-prioritized belief revision over probabilistic PreDeLP programs that can help with knowledge-base maintenance. For belief revision, we propose a set of rationality postulates -- based on well-known ones developed for classical knowledge bases -- that characterize how these belief revision operations should behave, and study classes of operators along with theoretical relationships with the proposed postulates, including representation theorems stating the equivalence between classes of operators and their associated postulates. We then demonstrate how our framework can be used to address the attribution problem in cyber security/cyber warfare.

Association rules are nowadays regarded as robust vehicles for creating Web recommendations. We present an induction-based technique for creating a compact representation of sets of association rules, particularly intended for publishing the compact representation in the Web. Our technique efficiently induces a defeasible logic program from a set of association rules, in a manner that the complete set of the given associations can be concluded, when integrating the induced compact program within a defeasible logic reasoning framework.