The results of the implementation of elliptic curve cryptography (ECC) over the field G@) on an 80MHz, 32-bit ARM microprocessor are presented. A practical software library has been produced which supports variable length implementation of the elliptic curve digital signature algorithm (ECDSA). The ECDSA and a recently proposed ECC-based wireless authentication protocol are implemented using the library. Timing results show that the 160-bit ECDSA signature generation and verification operations take around 46ms and 94ms, respectively. With these timings, the execution of the ECC-based wireless authentication protocol takes around 140ms on the ARM7TDMI processor, which is a widely used, low-power core processor for wireless applications. ,./

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IntroductionUrinary tract infection (UTI) is one of the most common types of infection. Currently, diagnosis is primarily based on microbiologic culture, which is time- and labor-consuming. The aim of this study was to assess the diagnostic accuracy of urinalysis results from UriSed (77 Electronica, Budapest, Hungary), an automated microscopic image-based sediment analyzer, in predicting positive urine cultures.Materials and methods:We examined a total of 384 urine specimens from hospitalized patients and outpatients attending our hospital on the same day for urinalysis, dipstick tests and semi-quantitative urine culture. The urinalysis results were compared with those of conventional semi-quantitative urine culture.Results:Of 384 urinary specimens, 68 were positive for bacteriuria by culture, and were thus considered true positives. Comparison of these results with those obtained from the UriSed analyzer indicated that the analyzer had a specificity of 91.1%, a sensitivity of 47.0%, a positive predictive value (PPV) of 53.3% (95% confidence interval (CI) = 40.8–65.3), and a negative predictive value (NPV) of 88.8% (95% Cl = 85.0–91.8%). The accuracy was 83.3% when the urine leukocyte parameter was used, 76.8% when bacteriuria analysis of urinary sediment was used, and 85.1% when the bacteriuria and leukocyturia parameters were combined. The presence of nitrite was the best indicator of culture positivity (99.3% specificity) but had a negative likelihood ratio of 0.7, indicating that it was not a reliable clinical test.Conclusions:Although the specificity of the UriSed analyzer was within acceptable limits, the sensitivity value was low. Thus, UriSed urinalysis results do not accurately predict the outcome of culture.

Abstract. We describe relay attacks on Bluetooth authentication protocol. The aim of these attacks is impersonation. The attacker does not need to guess or obtain a common secret known to both victims in order to set up these attacks, merely to relay the information it receives from one victim to the other during the authentication protocol run. Bluetooth authentication protocol allows such a relay if the victims do not hear each other. Such a setting is highly probable. We analyze the attacks for several scenarios and propose practical solutions. Moreover, we simulate attacks to make sure about their feasibility. These simulations show that current Bluetooth specifications do not have defensive mechanisms for relay attacks. However, relay attacks create a significant partial delay during the connection that might be useful for detection.

The most important cause of erectile dysfunction (ED) among aging men is organic disease due to vascular disturbance that is often caused by atherosclerosis. Recently, studies have shown that atherosclerosis can manifest as an active inflammatory process rather than as passive vascular injury caused by lipid infiltration. Our study aimed to examine the association of ED with the neutrophil/lymphocyte ratio (NLR) and the platelet/lymphocyte ratio (PLR), both of which are markers of inflammation. Between December 2014 and May 2015, 101 male patients aged 40-70 years who were seen at our institute due to ED were included in this study. Thirty-one sexually active men with similar clinical and demographic characteristics without ED were included in our study as a control group. The control and patient groups were compared with respect to their NLR and PLR values as well as other hormonal, biochemical, hematological parameters. The median ages of the patient and control groups were 49 (40-69) and 48 (43-65) years old, respectively. Comorbidities such as hypertension, diabetes, chronic obstructive pulmonary disease (COPD), and coronary artery disease were not significantly different between the groups (p > 0.05). The neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios were significantly higher in the patient group than in the control group (p < 0.05). Furthermore, the detected CRP levels were also significantly higher in the patient group than in the control group (p < 0.001). In the correlation analysis, the NLR, PLR, and CRP levels were negatively correlated with the IIEF-5 scores. A multivariate analysis was performed to determine the independent predictors of ED. PLR was identified as an independent predictor for ED. The neutrophil-to-lymphocyte and especially platelet-to-lymphocyte ratios are correlated with a diagnosis of ED, and these ratios could serve as practical parameters that will not elicit additional costs.

Phishing, a continuously growing cyber threat, aims to obtain innocent users' credentials by deceiving them via presenting fake web pages which mimic their legitimate targets. To date, various attempts have been carried out in order to detect phishing pages. In this study, we treat the problem of phishing web page identification as an image classification task and propose a machine learning augmented pure vision based approach which extracts and classifies compact visual features from web page screenshots. For this purpose, we employed several MPEG7 and MPEG7-like compact visual descriptors (SCD, CLD, CEDD, FCTH and JCD) to reveal color and edge based discriminative visual cues. Throughout the feature extraction process we have followed two different schemas working on either whole screenshots in a "holistic" manner or equal sized "patches" constructing a coarse-to-fine "pyramidal" representation. Moreover, for the task of image classification, we have built SVM and Random Forest based machine learning models. In order to assess the performance and generalization capability of the proposed approach, we have collected a mid-sized corpus covering 14 distinct brands and involving 2852 samples. According to the conducted experiments, our approach reaches up to 90.5% F1 score via SCD. As a result, compared to other studies, the suggested approach presents a lightweight schema serving competitive accuracy and superior feature extraction and inferring speed that enables it to be used as a browser plugin.

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Internet of Things is the next-generation Internet network created by intelligent objects with software and sensors, employed in a wide range of fields such as automotive, construction, health, textile, education and transportation. With the advent of Industry 4.0, Internet of Things has been started to be used and it has led to the emergence of innovative business models. The processing and production capabilities of Internet of Things objects in hidden and critical data provide great advantages for the next generation of Internet. However, the integrated features of Internet of Things objects cause vulnerabilities in terms of security, making them the target of cyber threats. In this study, a security model which offers an integrated risk-based Internet of Things security approach for the Internet of Things vulnerabilities while providing detailed information about Internet of Things and the types of attacks targeting Internet of Things is proposed. In addition, in this study, the vulnerabilities of Internet of Things were explained by classifying attack types threatening the physical layer, network layer, data processing layer and application layer. Moreover, the risk-based security model has been proposed by examining the vulnerabilities and threats of smart objects that generate the Internet of Things. The proposed Internet of Things model is a holistic security model that separately evaluates the Internet of Things layers against vulnerabilities and threats based on the risk-level approach.