False Negative Ratio Research Articles

An incrementally deployable path address scheme

The research community has proposed numerous network security solutions, each dealing with a specific problem such as address spoofing, denial-of-service attacks, denial-of-quality attacks, reflection attacks, viruses, or worms. However, due to the lack of fundamental support from the Internet, individual solutions often share little common ground in their design, which causes a practical problem: deploying all these vastly different solutions will add exceedingly high complexity to the Internet routers. In this paper, we propose a simple generic extension to the Internet, providing a new type of information, called path addresses, that simplify the design of security systems for packet filtering, fair resource allocation, packet classification, IP traceback, filter push-back, etc. IP addresses are owned by end hosts; path addresses are owned by the network core, which is beyond the reach of the hosts. We describe how to enhance the Internet protocols for path addresses that meet the uniqueness requirement, completeness requirement, safety requirement, and incrementally deployable requirement. We evaluate the performance of our scheme both analytically and by simulations, which show that, at small overhead, the false positive ratio and the false negative ratio can both be made negligibly small.

Comparaison de trois modèles prédictifs d’envahissement des ganglions non sentinelles en cas de métastases des ganglions sentinelles dans le cancer du sein : une étude rétrospective sur 77 patientes

Various statistic predictive models have been developed to predict the status of the non-sentinel lymph nodes (NSLN) when the sentinel one was invaded by tumor cells in the case of breast invasive carcinoma. The objective of this study was to compare the accuracy of three of these statistic models: the MSKCC nomogram, the Tenon score and the MDA score. The study was conducted at Croix Rousse Hospital, Lyon, France. When sentinel lymph node is invaded by metastasis, complete lymph node dissection of the axilla can be avoided using statistic predictive models especially when talking about micrometastases and moreover about isolated tumor cells in the sentinel lymph node. Over the different existing models, we tested the three most used ones to determine the model that is most adapted to our everyday practice. The study population consisted of 77 women with an invasive breast cancer treated by complete axillary lymph node dissection for metastatic sentinel lymph node. Over the nine models already published, we focused on three of them due to their ease to use as well as their good preliminary results: the Memorial Sloan-Kettering Cancer Center nomogram (MSKCC), the Tenon score and the M.D Anderson score (MDA). Different criteria are used by these models, but all of them take in consideration: tumor size, multifocality, tumor type, lymphovascular invasion, hormonal receptors, number of sentinel lymph node resected, degree of sentinel lymph node invasion (i.e. macrometastasis, micrometastasis or isolated tumor cells) and histologic method used to diagnose sentinel lymph node invasion. These scores are validated depending on their false negative ratio as well as the proportion of patient selected as having a low risk of non-sentinel lymph node invasion. The major criterion for the MSKCC score is the calibration that compares the predicted likelihood of invasion and the histologically proved one. The rate of non-sentinel lymph node invasion was 37.6%. The discrimination of the three models was good with an AUC of 0.74 for both MSKCC nomogram and Tenon score and of 0.72% for MDA score. MSKCC nomogram was well calibrated (P=0.23). The false negative ratio was 0 (CI 95%: 0-18.1%) for MSKCC nomogram, 16.7% (CI 95%: 8.6%-27.8%) for Tenon score and 16% (CI 95%: 6.8-31%) for MDA score. The percentage of patients considered as having a low risk of invasion of the non-sentinel lymph node was 20.5% for MSKCC nomogram, 34.7% for MDA score and 47.4% for Tenon score. MSKCC nomogram seems to be the statistic model that is most adapted to the population we studied. To validate the Tenon and MDA score, we should have a most important population.

Fully Automatic Segmentations of Liver and Hepatic Tumors From 3-D Computed Tomography Abdominal Images: Comparative Evaluation of Two Automatic Methods

An adaptive initialization method was developed to produce fully automatic processing frameworks based on graph-cut and gradient flow active contour algorithms. This method was applied to abdominal Computed Tomography (CT) images for segmentation of liver tissue and hepatic tumors. Twenty-five anonymized datasets were randomly collected from several radiology centres without specific request on acquisition parameter settings nor patient clinical situation as inclusion criteria. Resulting automatic segmentations of liver tissue and tumors were compared to their reference standard delineations manually performed by a specialist. Segmentation accuracy has been assessed through the following evaluation framework: dice similarity coefficient (DSC), false negative ratio (FNR), false positive ratio (FPR) and processing time. Regarding liver surfaces, graph-cuts achieved a DSC of 95.49% ( FPR=2.35% and FNR=5.10%), while active contours reached a DSC of 96.17% (FPR=3.35% and FNR=3.87%). The analyzed datasets presented 52 tumors: graph-cut algorithm detected 48 tumors with a DSC of 88.65%, while active contour algorithm detected only 44 tumors with a DSC of 87.10%. In addition, in terms of time performances, less time was requested for graph-cut algorithm with respect to active contour one. The implemented initialization method allows fully automatic segmentation leading to superior overall performances of graph-cut algorithm in terms of accuracy and processing time. The initialization method here presented resulted suitable and reliable for two different segmentation techniques and could be further extended.

Analysis and Identification of Malicious JavaScript Code

ABSTRACT Malicious JavaScript code has been actively and recently utilized as a vehicle for Web-based security attacks. By exploiting vulnerabilities such as cross-site scripting (XSS), attackers are able to spread worms, conduct Phishing attacks, and do Web page redirection to “typically” porn Web sites. These attacks can be preemptively prevented if the malicious code is detected before executing. Based on the fact that a malignant code will exhibit certain features, we propose a novel classification-based detection approach that will identify Web pages containing infected code. Using datasets of trusted and malicious Web sites, we analyze the behavior and properties of JavaScript code to point out its key features. These features form the basis of our identification system and are used to properly train the various classifiers on malicious and benign data. Performance evaluation results show that our approach achieves a 95% or higher detection accuracy, with very small (less than 3%) false positive and false negative ratios. Our solution surpasses the performance of the comparable literature.

Effects of Sampling and Spatio/Temporal Granularity in Traffic Monitoring on Anomaly Detectability

We quantitatively evaluate how sampling and spatio/temporal granularity in traffic monitoring affect the detectability of anomalous traffic. Those parameters also affect the monitoring burden, so network operators face a trade-off between the monitoring burden and detectability and need to know which are the optimal paramter values. We derive equations to calculate the false positive ratio and false negative ratio for given values of the sampling rate, granularity, statistics of normal traffic, and volume of anomalies to be detected. Specifically, assuming that the normal traffic has a Gaussian distribution, which is parameterized by its mean and standard deviation, we analyze how sampling and monitoring granularity change these distribution parameters. This analysis is based on observation of the backbone traffic, which exhibits spatially uncorrelated and temporally long-range dependence. Then we derive the equations for detectability. With those equations, we can answer the practical questions that arise in actual network operations: what sampling rate to set to find the given volume of anomaly, or, if the sampling is too high for actual operation, what granularity is optimal to find the anomaly for a given lower limit of sampling rate.

MANET adaptive structured P2P overlay

This paper presents an efficient structured P2P overlay over MANET which better matches to the physical network in term of routing. This feature is achieved by locally building up the minimum-spanning tree (MST) at each peer using the information of the peer’s logical neighbors which are either directly connected (1-hop) or 2-hops away. Using this interconnection structure among the peers, we design a new algorithm to distribute the contents information in the overlay by partitioning the identifier (ID) space among the peers. In this algorithm, each peer maintains a disjoint portion of the ID space. The ID space at a peer may be non-contiguous and each contiguous part is consecutive to the ID space of its directly connected neighbor peers. To route the file-lookup query, each peer builds up a binary search tree (BST) using the knowledge of the ID space of itself and of its directly connected neighbor peers. Simulation results show that our approach outperforms the existing approaches in term of routing overhead, average file-discovery delay, false-negative ratio and average path-stretch value.

Is “X”-WAS the Future for All of Epidemiology?

Is “X”-WAS the Future for All of Epidemiology?

The role of [18F]‐2‐fluoro‐2‐deoxy‐d‐glucose–positron emission tomography in thyroid nodules with indeterminate fine‐needle aspiration biopsy

Indeterminate results at fine-needle aspiration biopsy (FNAB) of thyroid nodules pose a clinical dilemma, because only 20% to 30% of patients suffer from malignancy. Previous studies suggested that the false-negative ratio of [(18)F]-2-fluoro-2-deoxy-D-glucose-positron emission tomography (FDG-PET) is very low; therefore, it may help identify patients who would benefit from (hemi)thyroidectomy. A systematic literature search was performed in 5 databases. After assessment, the identified studies were analyzed for heterogeneity, and the extracted data of test characteristics were pooled using a random-effects model. Threshold effects were examined, and publication bias was assessed. The query resulted in 239 records, of which 6 studies met predefined inclusion criteria. Data from 225 of the 241 described patients could be extracted. There was mild to moderate heterogeneity in study results (inconsistency index [I(2)] = 0.390-0.867). The pooled prevalence of malignancy was 26%. Pooled sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 95% (95% confidence interval [95% CI], 86%-99%), 48% (95% CI, 40%-56%), 39% (95% CI, 31%-47%), 96% (95% CI, 90%-99%), and 60% (95% CI, 53%-67%), respectively. Sensitivity increased to 100% for the 164 lesions that measured >15 mm in greatest dimension. There was no evidence of threshold effects or publication bias. A negative FDG-PET scan in patients who had thyroid nodules >15 mm with indeterminate FNAB results excluded thyroid cancer in a pooled population of 225 patients. Conversely, a positive FDG-PET result did not identify cancer, because approximately 50% of these patients had benign nodules. The authors concluded that the incorporation of FDG-PET into the initial workup of such patients before surgery deserves further investigation.

Assessment of lymphatic drainage patterns and implications for the extent of neck dissection in head and neck melanoma patients

The aim of this study is to evaluate lymphatic drainage with sentinel node location data in patients with head and neck cutaneous melanoma, and to determine the implications for the extent of therapeutic neck dissections. Sixty-five patients with head and neck cutaneous melanoma without evidence of regional metastases at ultrasound guided fine needle aspiration cytology examination were included. Lymphatic drainage patterns were investigated using planar and dynamic lymphoscintigraphy, and SPECT/CT. Biopsy of sentinel nodes was guided by images and gamma probe. The incidence of discordant sentinel nodes was determined by comparing actual drainage patterns to "O'Briens map" and to the treatment guidelines of The Netherlands Cancer Institute. Sentinel node identification was successful in 98% of the patients. Fifteen patients (23%) were diagnosed with a tumor-positive sentinel node. Two sentinel node-negative patients (3%) developed a regional lymph node metastasis (false-negative ratio: 12%). Twenty-three percent of the harvested sentinel nodes were discordant according to "O'Brien's map," while 14% were discordant according to the treatment guidelines of The Netherlands Cancer Institute (P < 0.001). Almost a quarter of head and neck melanomas metastasize outside clinically predicted neck levels. Neck surgery guidelines of The Netherlands Cancer Institute provide for a smaller number of discordant sentinel nodes.

Hepatic Vessel Segmentation for 3D Planning of Liver Surgery: Experimental Evaluation of a New Fully Automatic Algorithm

The aim of this study was to identify the optimal parameter configuration of a new algorithm for fully automatic segmentation of hepatic vessels, evaluating its accuracy in view of its use in a computer system for three-dimensional (3D) planning of liver surgery. A phantom reproduction of a human liver with vessels up to the fourth subsegment order, corresponding to a minimum diameter of 0.2 mm, was realized through stereolithography, exploiting a 3D model derived from a real human computed tomographic data set. Algorithm parameter configuration was experimentally optimized, and the maximum achievable segmentation accuracy was quantified for both single two-dimensional slices and 3D reconstruction of the vessel network, through an analytic comparison of the automatic segmentation performed on contrast-enhanced computed tomographic phantom images with actual model features. The optimal algorithm configuration resulted in a vessel detection sensitivity of 100% for vessels > 1 mm in diameter, 50% in the range 0.5 to 1 mm, and 14% in the range 0.2 to 0.5 mm. An average area overlap of 94.9% was obtained between automatically and manually segmented vessel sections, with an average difference of 0.06 mm(2). The average values of corresponding false-positive and false-negative ratios were 7.7% and 2.3%, respectively. A robust and accurate algorithm for automatic extraction of the hepatic vessel tree from contrast-enhanced computed tomographic volume images was proposed and experimentally assessed on a liver model, showing unprecedented sensitivity in vessel delineation. This automatic segmentation algorithm is promising for supporting liver surgery planning and for guiding intraoperative resections.

DDoS detection and traceback with decision tree and grey relational analysis

In Distributed Denial-of-Service (DDoS) Attack, an attacker breaks into many innocent computers (called zombies). Then, the attacker sends a large number of packets from zombies to a server, to prevent the server from conducting normal business operations. We design a DDoS-detection system based on a decision-tree technique and, after detecting an attack, to trace back to the attacker's locations with a traffic-flow pattern-matching technique. Our system could detect DDoS attacks with the false positive ratio about 1.2-2.4%, false negative ratio about 2-10%, and find the attack paths in traceback with the false negative rate 8-12% and false positive rate 12-14%.

Design of a sliding window scheme for detecting high packet-rate flows via random packet sampling

We discuss the design of a sliding window scheme for detecting high packet-rate flows via random packet sampling. We determine the values of control parameters, such as the sampling rate and window length, to minimize the false positive ratio, while keeping the false negative ratio sufficiently low and making the on-line processing possible. Under mild assumptions, we formulate this problem as a nonlinear program and provide its numerically feasible global optimal solution. We then conduct sampling experiments with public trace data and discuss the fundamental characteristics of the sliding window scheme with random packet sampling.

ISPY: Detecting IP Prefix Hijacking on My Own

IP prefix hijacking remains a major threat to the security of the Internet routing system due to a lack of authoritative prefix ownership information. Despite many efforts in designing IP prefix hijack detection schemes, no existing design can satisfy all the critical requirements of a truly effective system: real-time, accurate, lightweight, easily and incrementally deployable, as well as robust in victim notification. In this paper, we present a novel approach that fulfills all these goals by monitoring network reachability from key external transit networks to one's own network through lightweight prefix-owner-based active probing. Using the prefix-owner's view of reachability, our detection system, iSPY, can differentiate between IP prefix hijacking and network failures based on the observation that hijacking is likely to result in topologically more diverse polluted networks and unreachability. Through detailed simulations of Internet routing, 25-day deployment in 88 autonomous systems (ASs) (108 prefixes), and experiments with hijacking events of our own prefix from multiple locations, we demonstrate that iSPY is accurate with false negative ratio below 0.45% and false positive ratio below 0.17%. Furthermore, iSPY is truly real-time; it can detect hijacking events within a few minutes.

Using Hidden Markov Model to detect rogue access points

AbstractOne of the most challenging security concerns for network administrators is the presence of rogue access points (RAPs). The challenge is to detect and disable a RAP before it poses a serious security risk. In this paper, we propose a statistical based approach to detect RAPs using a Hidden Markov Model (HMM), which is applied to passively measure packet‐header data collected at a gateway router. The main idea is to process the sequence of packet traces in order to distinguish the normal packets from the abnormal ones. Our approach utilizes variations in packet inter‐arrival time to differentiate between authorized access points and RAPs. We used the inter‐arrival time of a packet as a distinguishing parameter because it varies drastically for a normal activity and an intrusive activity. We developed our HMM by analyzing Denial of Service (DoS) attacks of 802.11 based wireless local area networks. Our trained HMM can detect the presence of a RAP promptly within a second with extreme accuracy (very low false positive and false negative ratios are obtained). The success of our approach lies in the fact that it leverages knowledge about the behavior of the traffic characteristics of 802.11 based wireless local area networks and the properties of DoS attacks. Experiments were also performed to improve the accuracy of our HMM model. Our approach is scalable and non‐intrusive, requiring little deployment cost and effort, and is easy to manage and maintain. Copyright © 2010 John Wiley &amp; Sons, Ltd.

Automated Segmentation of Ultrasonic Breast Lesions Using Statistical Texture Classification and Active Contour Based on Probability Distance

Because of its complicated structure, low signal/noise ratio, low contrast and blurry boundaries, fully automated segmentation of a breast ultrasound (BUS) image is a difficult task. In this paper, a novel segmentation method for BUS images without human intervention is proposed. Unlike most published approaches, the proposed method handles the segmentation problem by using a two-step strategy: ROI generation and ROI segmentation. First, a well-trained texture classifier categorizes the tissues into different classes, and the background knowledge rules are used for selecting the regions of interest (ROIs) from them. Second, a novel probability distance-based active contour model is applied for segmenting the ROIs and finding the accurate positions of the breast tumors. The active contour model combines both global statistical information and local edge information, using a level set approach. The proposed segmentation method was performed on 103 BUS images (48 benign and 55 malignant). To validate the performance, the results were compared with the corresponding tumor regions marked by an experienced radiologist. Three error metrics, true-positive ratio (TP), false-negative ratio (FN) and false-positive ratio (FP) were used for measuring the performance of the proposed method. The final results (TP = 91.31%, FN = 8.69% and FP = 7.26%) demonstrate that the proposed method can segment BUS images efficiently, quickly and automatically. (E-mail: hengda.cheng@usu.edu)

Milan Criteria Versus San Francisco Criteria in Hepatocellular Carcinoma: Our Center's Experience

Background and aim Liver transplantation (OLT) represents the best treatment for hepatocellular carcinoma (HCC) in advanced cirrhosis showing a 70% 5-year survival rate. Our study sought to compare overall survivals among patients who underwent OLT under Milan Criteria (MC) or San Francisco Criteria (UCSFC). Methods We retrospectively analyzed patients who underwent liver transplantation for HCC in our institution from November 2001 to December 2007. We analyzed age, gender, OLT indication, maximal tumor size, histology, and survival. We compared survival among patients who met MC versus UCSFC. Results From November 2001 to December 2007, 48/177 (27%) liver transplantations performed in our hospital were indicated due to HCC. The two patients who did not show any tumor in the explanted liver (false-positive ratio 4.2%) were excluded from the analysis. Another two patients were included who showed incidental HCC lesions (false-negative ratio 1.7%), yielding 48 analyzed patients. The mean diameter of the HCC nodules were 3.1 cm before OLT and 3.8 cm in the pathologic examination, a statistically significant difference. Two patients exceeded MC before OLT, and six patients showed this feature in the explanted liver. There was a significant difference in the degree of vascular invasion between the two groups. Overall mortality was 25.9% at 4 years; the MC group show an 11.9% versus UCSFC group, a 66.6% rate. Conclusions HCC is a common indication for OLT. Hepatitis C virus is the most common etiology. Survival among the MC group was significantly better than that of subjects beyond the MC, a difference that supports the use of MC for HCC.

Passive Online Detection of 802.11 Traffic Using Sequential Hypothesis Testing with TCP ACK-Pairs

In this paper, we propose two online algorithms to detect 802.11 traffic from packet-header data collected passively at a monitoring point. These algorithms have a number of applications in real-time wireless LAN management, for instance, in detecting unauthorized access points and detecting/predicting performance degradations. Both algorithms use sequential hypothesis tests and exploit fundamental properties of the 802.11 CSMA/CA MAC protocol and the half-duplex nature of wireless channels. They differ in that one requires training sets, while the other does not. We have built a system for online wireless traffic detection using these algorithms and deployed it at a university gateway router. Extensive experiments have demonstrated the effectiveness of our approach: the algorithm that requires training provides rapid detection and is extremely accurate (the detection is mostly within 10 seconds, with very low false-positive and false-negative ratios), the algorithm that does not require training detects 60 percent to 76 percent of the wireless hosts without any false positives, and both algorithms are lightweight, with computation and storage overhead well within the capability of commodity equipment.

Problems in the accuracy control of gastroendoscopy, in particular the false negative ratio

Problems in the accuracy control of gastroendoscopy, in particular the false negative ratio

Performance evaluation and relevance of the CellaVisionTM DM96 system in routine analysis and in patients with malignant hematological diseases

The CellaVision™ DM96 is an automated image analysis system dedicated to locating and preclassifying the various types of white blood cells in peripheral blood smears. The system also partially characterizes of the red blood cell morphology and is able to perform platelet counts. We routinely analyzed the blood samples from 440 patients with quantitative and/or qualitative abnormalities detected by the XE-2100 Sysmex™. Only 2.6% of cells are not identified by DM96™. After classification of the unidentified cells very good correlation coefficients are observed between DM96™ and manual microscopy for most hematological parameters and accuracy is judged excellent up to 98%. For most common parameters, false positive and false negative ratios are also very good. Whatever the pathology and the number of blasts on smear, all patients were positive for blast detection on DM96™. The system is a useful tool for assisting in the diagnosis and classification of most acute or chronic leukemia. Automatic cell location and preclassification, along with unique cell views on the computer screen, could reduce the time spent performing differentials and make real-time collaboration between colleagues a natural part of the classification process. The workstation also provides an ergonomically correct and relaxed working environment. We suggest its use in routine analysis; the system could be very helpful for the accurate morphological diagnosis of samples from patients with malignant hematological disease.

Ispy

IP prefix hijacking remains a major threat to the security of the Internet routing system due to a lack of authoritative prefix ownership information. Despite many efforts in designing IP prefix hijack detection schemes, no existing design can satisfy all the critical requirements of a truly effective system: real-time, accurate, light-weight, easily and incrementally deployable, as well as robust in victim notification. In this paper, we present a novel approach that fulfills all these goals by monitoring network reachability from key external transit networks to one's own network through lightweight prefix-owner-based active probing. Using the prefix-owner's view of reachability, our detection system, iSPY, can differentiate between IP prefix hijacking and network failures based on the observation that hijacking is likely to result in topologically more diverse polluted networks and unreachability. Through detailed simulations of Internet routing, 25-day deployment in 88 ASes (108 prefixes), and experiments with hijacking events of our own prefix from multiple locations, we demonstrate that iSPY is accurate with false negative ratio below 0.45% and false positive ratio below 0.17%. Furthermore, iSPY is truly real-time; it can detect hijacking events within a few minutes.