An Optimum-Path Forest framework for intrusion detection in computer networks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Histological evaluation of dog permanent teeth after traumatic intrusion of their primary predecessors

The aims of this study were to analyze the histomorphology of developing permanent teeth whose primary teeth had suffered traumatic intrusion, as well as to compare the influence of immediate extraction of the intruded tooth to passive re-eruption. Nine dogs from 45 to 50 days old were submitted to the intrusion of the maxillary central and lateral primary incisors using a force applicator adapted to the teeth incisal cuspids. The right side intruded teeth were kept in their sockets and the ones on the left side were extracted 30 min later. After a postoperatory periods of 30 and 60 days, four (group 1) and five (group 2) dogs, respectively, were killed by perfusion. The histological evaluations showed that, in group 1, alterations had occurred in the odontoblastic layer and deposition of the enamel matrix had taken place in some specimens while in group 2, a portion of non-mineralized matrix was observed. We concluded that the morphological changes were because of the immediate trauma of intrusion. No differences were found between the groups where the primary tooth was immediately extracted or left to passively re-erupt.

Intrusion of teeth with class III furcation: a clinical, histologic and histometric study in dogs

Aim: To assess orthodontic intrusion effects on periodontal tissues in dogs' pre-molars with class III furcations treated with open flap debridement (OFD) or with guided tissue regeneration (GTR) associated to bone autograft (BA).Material and Methods: Class III furcations were created in the pre-molars of seven mongrel dogs. After 75 days, teeth were randomly treated with OFD or GTR/BA. After 1 month, metallic crowns were assembled on pre-molars and connected apically to mini-implants by nickel-titanium springs. Teeth were randomly assigned to orthodontic intrusion (OFD+I and GTR/BA+I) groups or no movement (OFD and GTR/BA) groups. Dogs were sacrificed after 3 months of movement and 1 month retention.Results: All class III furcations were closed or reduced to class II or I in the intrusion groups, while 50% of the lesions in non-moved teeth remained unchanged. Intruded teeth presented higher probing depth and lower gingival marginal level than non-moved teeth (p < 0.01). Clinical attachment gain was reduced in the intrusion groups by the end of retention (p < 0.05). OFD+I presented smaller soft tissue area and larger bone tissue area than other groups (p < 0.05).Conclusion: Orthodontic intrusion with anchorage via mini-implants improved the healing of class III furcation defects after OFD in dogs. GTR/BA impaired those results.

Influence of surgical repositioning of mature permanent dog teeth following experimental intrusion: a histologic assessment

The aim of this study was to evaluate, through histologic examination, the effect of surgical repositioning of intruded dog teeth upon the pulpal and surrounding tissues. Thirty teeth in 10 adult dogs, aged 2-3 years, were used. Fifteen teeth were intruded, surgically repositioned and fixed using orthodontics wire, composite resin, and enamel acid conditioning. All these teeth served as the experimental group. The remaining intruded teeth were not treated ( control group). The animals were sacrificed to allow observations at 7,15, and 30 post-operative days. The maxillary and mandibular archs were removed and processed for histologic exam. Based on the methodology and observed results, we concluded that: pulpal necrosis, external root resorption and ankylosis were common sequelae to severe traumatic intrusion; a careful immediate surgical repositioning of severed intruded permanent tooth with complete root formation has many advantages with few disadvantages.

An attack signature model to computer security intrusion detection

Internal and external computer network attacks or security threats occur according to standards and follow a set of subsequent steps, allowing to establish profiles or patterns. This well-known behavior is the basis of signature analysis intrusion detection systems. This work presents a new attack signature model to be applied on network-based intrusion detection systems engines. The AISF (ACME! Intrusion Signature Format) model is built upon XML technology and works on intrusion signatures handling and analysis, from storage to manipulation. Using this new model, the process of storing and analyzing information about intrusion signatures for further use by an IDS become a less difficult and standardized process.

Conservative management of severe intrusion in a primary tooth: A 4-year follow-up

The purpose of this report was to describe the case of an 18-month-old boy who was referred to the pediatric clinic of the School of Dentistry of Araçatuba, São Paulo State University, Araçatuba, São Paulo, Brazil, 3 days after sustaining severe trauma that led to the complete intrusion of the primary maxillary right lateral incisor, a crown fracture of the primary maxillary right central incisor without pulp involvement, and disruption of the superior labial frenum. Four months later, spontaneous re-eruption was observed in the intruded tooth and no endodontic intervention was necessary in either traumatized teeth. Four years after the trauma, a morphological change in the germ of the permanent successor was noted. Clinical follow-up and periodic radiographies are necessary after traumatic intrusion of primary teeth to monitor possible sequelae in the permanent successors.

Statistical model applied to NetFlow for network intrusion detection

The computers and network services became presence guaranteed in several places. These characteristics resulted in the growth of illicit events and therefore the computers and networks security has become an essential point in any computing environment. Many methodologies were created to identify these events; however, with increasing of users and services on the Internet, many difficulties are found in trying to monitor a large network environment. This paper proposes a methodology for events detection in large-scale networks. The proposal approaches the anomaly detection using the NetFlow protocol, statistical methods and monitoring the environment in a best time for the application. © 2010 Springer-Verlag Berlin Heidelberg.

Intrusion detection system using optimum-path forest

Intrusion detection systems that make use of artificial intelligence techniques in order to improve effectiveness have been actively pursued in the last decade. Neural networks and Support Vector Machines have been also extensively applied to this task. However, their complexity to learn new attacks has become very expensive, making them inviable for a real time retraining. In this research, we introduce a new pattern classifier named Optimum-Path Forest (OPF) to this task, which has demonstrated to be similar to the state-of-the-art pattern recognition techniques, but extremely more efficient for training patterns. Experiments on public datasets showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, as well as allow the algorithm to learn new attacks faster than the other techniques. © 2011 IEEE.

Performance evaluation of the fuzzy ARTMAP for network intrusion detection

Recently, considerable research work have been conducted towards finding fast and accurate pattern classifiers for training Intrusion Detection Systems (IDSs). This paper proposes using the so called Fuzzy ARTMAT classifier to detect intrusions in computer network. Our investigation shows, through simulations, how efficient such a classifier can be when used as the learning mechanism of a typical IDS. The promising evaluation results in terms of both detection accuracy and training duration indicate that the Fuzzy ARTMAP is indeed viable for this sort of application.

Boosting optimum-path forest clustering through harmony Search and its applications for intrusion detection in computer networks

In this paper we propose a nature-inspired approach that can boost the Optimum-Path Forest (OPF) clustering algorithm by optimizing its parameters in a discrete lattice. The experiments in two public datasets have shown that the proposed algorithm can achieve similar parameters' values compared to the exhaustive search. Although, the proposed technique is faster than the traditional one, being interesting for intrusion detection in large scale traffic networks. © 2012 IEEE.

Intrusion detection in computer networks using optimum-path forest clustering

Nowadays, organizations face the problem of keeping their information protected, available and trustworthy. In this context, machine learning techniques have also been extensively applied to this task. Since manual labeling is very expensive, several works attempt to handle intrusion detection with traditional clustering algorithms. In this paper, we introduce a new pattern recognition technique called Optimum-Path Forest (OPF) clustering to this task. Experiments on three public datasets have showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, since it outperformed some state-of-the-art unsupervised techniques. © 2012 IEEE.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

Platinum-Group Minerals in Chromitites of the Niquelandia Layered Intrusion (Central Goias, Brazil): Their Magmatic Origin and Low-Temperature Reworking during Serpentinization and Lateritic Weathering

A variety of platinum-group-minerals (PGM) have been found to occur associated with the chromitite and dunite layers in the Niquelandia igneous complex. Two genetically distinct populations of PGM have been identified corresponding to phases crystallized at high temperatures (primary), and others formed or modified during post-magmatic serpentinization and lateritic weathering (secondary). Primary PGM have been found in moderately serpentinized chromitite and dunite, usually included in fresh chromite grains or partially oxidized interstitial sulfides. Due to topographically controlled lateritic weathering, the silicate rocks are totally transformed to a smectite-kaolinite-garnierite-amorphous silica assemblage, while the chromite is changed into a massive aggregate of a spinel phase having low-Mg and a low Fe3+/Fe2+ ratio, intimately associated with Ti-minerals, amorphous Fe-hydroxides, goethite, hematite and magnetite. The PGM in part survive alteration, and in part are corroded as a result of deep chemical weathering. Laurite is altered to Ru-oxides or re-crystallizes together with secondary Mg-ilmenite. Other PGM, especially the Pt-Fe alloys, re-precipitate within the altered chromite together with kaolinite and Fe-hydroxides. Textural evidence suggests that re-deposition of secondary PGM took place during chromite alteration, controlled by variation of the redox conditions on a microscopic scale.

A Dataset for Evaluating Intrusion Detection Systems in IEEE 802.11 Wireless Networks

Internet access by wireless networks has grown considerably in recent years. However, these networks are vulnerable to security problems, especially those related to denial of service attacks. Intrusion Detection Systems(IDS)are widely used to improve network security, but comparison among the several existing approaches is not a trivial task. This paper proposes building a datasetfor evaluating IDS in wireless environments. The data were captured in a real, operating network. We conducted tests using traditional IDS and achieved great results, which showed the effectiveness of our proposed approach.