A new approach to deploy a self-adaptive distributed firewall

Distributed firewall systems emerged with the proposal of protecting individual hosts against attacks originating from inside the network. In these systems, firewall rules are centrally created, then distributed and enforced on all servers that compose the firewall, restricting which services will be available. However, this approach lacks protection against software vulnerabilities that can make network services vulnerable to attacks, since firewalls usually do not scan application protocols. In this sense, from the discovery of any vulnerability until the publication and application of patches there is an exposure window that should be reduced. In this context, this article presents Self-Adaptive Distributed Firewall (SADF). Our approach is based on monitoring hosts and using a vulnerability assessment system to detect vulnerable services, integrated with components capable of deciding and applying firewall rules on affected hosts. In this way, SADF can respond to vulnerabilities discovered in these hosts, helping to mitigate the risk of exploiting the vulnerability. Our system was evaluated in the context of a simulated network environment, where the results achieved demonstrate its viability

Modeling Agents in Dialogue Systems

. In this paper we present an extended logic programming framework that allows the modeling of dialogues between agents with different levels of sincerity and credulity. An agent is modeled by a set of extended logic programming rules, and the agent , s mental state is defined by the well-founded model of the extended logic program. Using this modeling process, an agent is able to participate in dialogues, updating and revising its mental state after each sentence. 1 Introduction In order to participate in dialogues, an agent needs the capability of modeling mental states. Specifically, it is necessary to represent the agent attitudes (beliefs, intentions, and objectives), world knowledge, and temporal, reasoning and behavior rules. In this paper, we propose a logic programming framework that allows the representation of agent models. Agents are defined as logic programs that are extended with explicit negation. The semantics of the programs is given by the well-founded semantics of l..

LUPS - A language for updating logic programs

Most of the work conducted so far in the field of logic programming has focused on representing static knowledge, i.e. knowledge that does not evolve with time. To overcome this limitation, in a recent paper, the authors introduced the concept of dynamic logic programming. There, they studied and defined the declarative and operational semantics of sequences of logic programs (or dynamic logic programs), P-o + ... + P-n. Each such program contains knowledge about some given state, where different states may, e.g., represent different time periods or different sets of priorities. The role of dynamic logic programming is to employ relationships existing between the possibly mutually contradictory sequence of programs to precisely determine, at any given state, the declarative and procedural semantics of their combination. But how, in concrete situations, is a sequence of logic programs built? For instance, in the domain of actions, what are the appropriate sequences of programs that represent the performed actions and their effects? Whereas dynamic logic programming provides a way for determining what should follow, given the sequence of programs, it does not provide a good practical language for the specification of updates or changes in the knowledge represented by successive logic progams. In this paper we define a language designed for specifying changes to logic programs (LUPS - "Language for dynamic updates"). Given an initial knowledge base (in the form of a logic program) LUPS provides a way for sequentially updating it. The declarative meaning of a sequence of sets of update actions in LUPS is defined using the semantics of the dynamic logic program generated by those actions. We also provide a translation of the sequence of update statements sets into a single generalized logic program written in a meta-language, so that the stable models of the resulting program correspond to the previously defined declarative semantics. This meta-language is used in the actual implementation, although this is not the subject of this paper. Finally we briefly mention related work (lack of space prevents us from presenting more detailed comparisons).publishe

Probabilistic Hybrid Knowledge Bases under the Distribution Semantics

Since Logic Programming (LP) and Description Logics (DLs) are based on different assumptions (the closed and the open world assumption, respectively), combining them provides higher expressiveness in applications that require both assumptions. Several proposals have been made to combine LP and DLs. An especially successful line of research is the one based on the Lifschitz's logic of Minimal Knowledge with Negation as Failure (MKNF). Motik and Rosati introduced Hybrid knowledge bases (KBs), composed of LP rules and DL axioms, gave them an MKNF semantics and studied their complexity. Knorr et al. proposed a well-founded semantics for Hybrid KBs where the LP clause heads are non-disjunctive, which keeps querying polynomial (provided the underlying DL is polynomial) even when the LP portion is non-stratified. In this paper, we propose Probabilistic Hybrid Knowledge Bases (PHKBs), where the atom in the head of LP clauses and each DL axiom is annotated with a probability value. PHKBs are given a distribution semantics by defining a probability distribution over deterministic Hybrid KBs. The probability of a query being true is the sum of the probabilities of the deterministic KBs that entail the query. Both epistemic and statistical probability can be addressed, thanks to the integration of probabilistic LP and DLs

Stable Models and an Alternative Logic Programming Paradigm

Stable model semantics appeared... In this paper we argue that rather than to try to resolve these inconsistencies and force stable model semantics into a standard logic programming mold (this effort most likely is doomed to failure), a change of view is required. Therefore, we propose a perspective on the stable model semantics that departs from several basic tenets of logic programming. At the same time, this perspective leads to a computational system very much in the general spirit of logic programming. The system is declarative..