In network management, when it comes to security breaches, human error constitutes a dominant factor. We present our tool topoS which automatically synthesizes low-level network configurations from high-level security goals. The automation and a feedback loop help to prevent human errors. Except for a last serialization step, topoS is formally verified with Isabelle HOL, which prevents implementation errors. In a case study, we demonstrate topoS by example. For the first time, the complete transition from high-level security goals to both firewall and SDN configurations is presented.
[0] Fireman @cite_20 [0] Fireman @cite_20 ; HSA @cite_8 ; Anteater @cite_13 ; Config -Checker @cite_19 ; VeriFlow @cite_28 [0] Xie @cite_16 ; Lopes @cite_21 [0] HSA @cite_8 ; Anteater @cite_13 ; Config -Checker @cite_19 [0] one big ; switch @cite_23 ; Firmato @cite_5 ; FLIP @cite_2 ; FortNOX @cite_22 ; Merlin @cite_24 ; Kinetic @cite_25 ; [0] step [0] Firmato @cite_5 ; FLIP @cite_2 ; NetKAT @cite_12 ; step + [0] rcp @cite_29 ; OpenFlow @cite_17 ; Merlin @cite_24 ; optimized one ; big switch @cite_6 ; NetKAT @cite_12 ; VeriFlow @cite_28 ; [0] Iptables Semantics @cite_18
@cite_12 @cite_18 @cite_22 @cite_8 @cite_28 @cite_29 @cite_21 @cite_6 @cite_24 @cite_19 @cite_23 @cite_2 @cite_5 @cite_16 @cite_13 @cite_25 @cite_20 @cite_17
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References.- Technical Foundations.- 1 Introduction.- 2 Graphs and Their Representation.- 3 Graph Planarity and Embeddings.- 4 Graph Drawing Methods.- References.- WilmaScope - A 3D Graph Visualization System.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- Pajek - Analysis and Visualization of Large Networks.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- Tulip - A Huge Graph Visualization Framework.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- Graphviz and Dynagraph - Static and Dynamic Graph Drawing Tools.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- AGD - A Library of Algorithms for Graph Drawing.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- yFiles - Visualization and Automatic Layout of Graphs.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- GDS - A Graph Drawing Server on the Internet.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- BioPath - Exploration and Visualization of Biochemical Pathways.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- DBdraw - Automatic Layout of Relational Database Schemas.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- GoVisual - A Diagramming Software for UML Class Diagrams.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- CrocoCosmos - 3D Visualization of Large Object-oriented Programs.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- ViSta - Visualizing Statecharts.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- visone - Analysis and Visualization of Social Networks.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- Polyphemus and Hermes - Exploration and Visualization of Computer Networks.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References. It is known that there exists a PCP characterization of NP where the verifier makes 3 queries and has a one-sided error that is bounded away from 1; and also that 2 queries do not suffice for such a characterization. Thus PCPs with 3 queries possess non-trivial verification power and motivate the task of determining the lowest error that can be achieved with a 3-query PCP. Recently, Hastad (1997) has shown a tight characterization of NP by constructing a 3-query PCP verifier with "error" arbitrarily close to 1 2. Unfortunately this verifier makes two-sided error and Hastad makes essential use of this feature. One-sided error, on the other hand, is a natural notion to associate with a proof system, since it has the desirable property that every rejected proof has a short counterexample. The question of determining the smallest error for which there exists a 3-query PCP verifier making one-sided error and accepting an NP-complete language, however, remained open. We resolve this question by showing that NP has a 3-query PCP with a one-sided error that is arbitrarily close to 1 2. This characterization is tight, i.e., the error cannot be lower. This result is in seeming contradiction with the results of Trevisan (1997) and Zwick (1998) who show that in order to recognize an NP-complete language, the error probability of a PCP verifier making 3 non-adaptive queries and having one-sided error must be at least 5 8. We get around this bottleneck by designing an adaptive 3-query PCP for NP. Our result yields the first tight analysis of an adaptive PCP; and reveals a previously unsuspected separation between the powers of adaptive and non-adaptive PCPs. Our design and analysis of adaptive PCPs can be extended to higher number of queries as well and we give an example of such a proof system with 5 queries. Our adaptive verifiers yield proof systems whose error probabilities match those of previous constructions, while also achieving one-sidedness in the error. This raises new questions about the power of adaptive PCPs, which deserve further study. Resume Les procedures d'apprentissage de descente de gradient sont le plus souvent utilisees dans la modelisation de reseaux neuronaux. Lorsque ces algorithmes (e.g., la retropropagation) sont appliques a des tâches d'apprentissage sequentiel, un defaut majeur, appele oubli catastrophique (ou interference catastrophique), survient generalement: lorsqu'un reseau ayant deja appris un premier ensemble d'items est ensuite entraine sur un deuxieme ensemble d'items, alors l'information nouvellement apprise peut completement detruire celle anterieurement apprise. Pour eviter ce defaut peu plausible, il est ici propose une architecture a deux reseaux neuronaux ou les nouveaux items sont appris par un premier reseau conjointement avec des pseudo-items internes provenant d'un second reseau. Comme il est demontre que ces pseudo-items refletent la structure des items anterieurement appris par le premier reseau, le modele implemente ainsi un processus de rafraichissement par l'ancienne information. Le point crucial est que ce mecanisme de rafraichissement repose sur des reseaux neuronaux reverberants qui n'ont besoin que de stimulations aleatoires pour operer. Le modele fournit ainsi un moyen de reduire d'une maniere spectaculaire l'interference retroactive tout en conservant la nature essentiellement distribuee de l'information et propose une hypothese originale et plausible sur la maniere de ≪ copier et coller ≫ la memoire distribuee au sein d'une structure cerebrale vers une autre. We consider the problem of using a large unlabeled sample to boost performance of a learning algorit,hrn when only a small set of labeled examples is available. In particular, we consider a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views. For example, the description of a web page can be partitioned into the words occurring on that page, and the words occurring in hyperlinks t,hat point to that page. We assume that either view of the example would be sufficient for learning if we had enough labeled data, but our goal is to use both views together to allow inexpensive unlabeled data to augment, a much smaller set of labeled examples. Specifically, the presence of two distinct views of each example suggests strategies in which two learning algorithms are trained separately on each view, and then each algorithm’s predictions on new unlabeled examples are used to enlarge the training set of the other. Our goal in this paper is to provide a PAC-style analysis for this setting, and, more broadly, a PAC-style framework for the general problem of learning from both labeled and unlabeled data. We also provide empirical results on real web-page data indicating that this use of unlabeled examples can lead to significant improvement of hypotheses in practice. *This research was supported in part by the DARPA HPKB program under contract F30602-97-1-0215 and by NSF National Young investigator grant CCR-9357793. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. TO copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and or a fee. COLT 98 Madison WI USA Copyright ACM 1998 l-58113-057--0 98 7... 5.00 92 Tom Mitchell School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3891 mitchell+@cs.cmu.edu