Cybercrime precursors: towards a model of offender resources

This thesis applies Ekblom and Tilley’s concept of offender resources to the study of criminal behaviour on the Internet. Offender predispositions are influenced by situational, that is the environmental incentives to commit crime. This thesis employs non-participation observation of online communities involved in activities linked to malicious forms of software. Actual online conversations are reproduced, providing rich ethnographic detail of activities that have taken place between 2008 and 2012 from eight discussion forums where malicious software and cases of hacking are openly discussed among actors. A purposeful sample of key frontline cybercrime responders (N=12) were interviewed about crimeware and their views of the activity observed in the discussion forums. Based on the empirical data, this thesis tests a number of criminological theories and assesses their relative compatibility with social interactions occurring in various online forum sites frequented by persons interested in the formation and use of malicious code. The thesis illustrates three conceptual frameworks of offender resources, based on different criminological theories. The first model ties ‘offender resources’ to the actual offender, suggesting that certain malicious software and its associated activities derive from the decisions, knowledge and abilities of the individual agent. The second model submits that ‘offender resources’ should be viewed more as a pathway leading to offending behaviour that must be instilled and then indoctrinated over a length of time through social interaction with other offenders. The third model emphasises the complex relationships that constitute or interconnect with ‘offender resources’ such as the nexus of relevant social groups and institutions in society. These include the Internet security industry, the law, and organised crime. Cybercrime is facilitated by crimeware, a specific type of computer software, and a focus on this element can help better understand how cybercrime evolves

Organizations and cyber crime: An analysis of the nature of groups engaged in cyber crime

This paper explores the nature of groups engaged in cyber crime. It briefly outlines the definition and scope of cyber crime, theoretical and empirical challenges in addressing what is known about cyber offenders, and the likely role of organized crime groups. The paper gives examples of known cases that illustrate individual and group behaviour, and motivations of typical offenders, including state actors. Different types of cyber crime and different forms of criminal organization are described drawing on the typology suggested by McGuire (2012). It is apparent that a wide variety of organizational structures are involved in cyber crime. Enterprise or profit-oriented activities, and especially cyber crime committed by state actors, appear to require leadership, structure, and specialisation. By contrast, protest activity tends to be less organized, with weak (if any) chain of command

Breathing 100% oxygen during water immersion improves postimmersion cardiovascular responses to orthostatic stress

Physiological compensation to postural stress is weakened after long-duration water immersion (WI), thus predisposing individuals to orthostatic intolerance. This study was conducted to compare hemodynamic responses to postural stress following exposure to WI alone (Air WI), hyperbaric oxygen alone in a hyperbaric chamber (O2HC), and WI combined with hyperbaric oxygen (O2WI), all at a depth of 1.35 ATA, and to determine whether hyperbaric oxygen is protective of orthostatic tolerance. Thirty-two healthy men underwent up to 15 min of 70° head-up tilt (HUT) testing before and after a single 6-h resting exposure to Air WI (N = 10), O2HC (N = 12), or O2WI (N = 10). Heart rate (HR), blood pressure (BP), cardiac output (Q), stroke volume (SV), forearm blood flow (FBF), and systemic and forearm vascular resistance (SVR and FVR) were measured. Although all subjects completed HUT before Air WI, three subjects reached presyncope after Air WI exposure at 10.4, 9.4, and 6.9 min. HUT time did not change after O2WI or O2HC exposures. Compared to preexposure responses, HR increased (+10 and +17%) and systolic BP (-13 and -8%), and SV (-16 and -23%) decreased during HUT after Air WI and O2WI, respectively. In contrast, HR and SV did not change, and systolic (+5%) and diastolic BP (+10%) increased after O2HC. Q decreased (-13 and -7%) and SVR increased (+12 and +20%) after O2WI and O2HC, respectively, whereas SVR decreased (-9%) after Air WI. Opposite patterns were evident following Air WI and O2HC for FBF (-26 and +52%) and FVR (+28 and -30%). Therefore, breathing hyperbaric oxygen during WI may enhance post-WI cardiovascular compensatory responses to orthostatic stress

Respiratory rate derived from smartphone-camera-acquired pulse photoplethysmographic signals

A method for deriving respiratory rate from smartphone-camera-acquired pulse photoplethysmographic (SCPPG) signal is presented. Our method exploits respiratory information by examining the pulse wave velocity and dispersion from the SCPPG waveform and we term these indices as the pulse width variability (PWV). A method to combine information from several derived respiration signals is also presented and it is used to combine PWV information with other methods such as pulse amplitude variability (PAV), pulse rate variability (PRV), and respiration-induced amplitude and frequency modulations (AM and FM) in SCPPG signals Evaluation is performed on a database containing SCPPG signals recorded from 30 subjects during controlled respiration experiments at rates from 0.2 to 0.6 Hz with an increment of 0.1 Hz, using three different devices: iPhone 4S, iPod 5, and HTC One M8. Results suggest that spontaneous respiratory rates (0.2–0.4 Hz) can be estimated from SCPPG signals by the PWV- and PRVbased methods with low relative error (median of order 0.5% and interquartile range of order 2.5%). The accuracy can be improved by combining PWV and PRV with other methods such as PAV, AM and/or FM methods. Combination of these methods yielded low relative error for normal respiratory rates, and Institute of Physics and Engineering in Medicine maintained good performance at higher rates (0.5–0.6 Hz) when using the iPhone 4S or iPod 5 devices

Wearable armband device for daily life electrocardiogram monitoring

A wearable armband electrocardiogram (ECG) monitor has been used for daily life monitoring. The armband records three ECG channels, one electromyogram (EMG) channel, and tri-axial accelerometer signals. Contrary to conventional Holter monitors, the armband-based ECG device is convenient for long-term daily life monitoring because it uses no obstructive leads and has dry electrodes (no hydrogels), which do not cause skin irritation even after a few days. Principal component analysis (PCA) and normalized least mean squares (NLMS) adaptive filtering were used to reduce the EMG noise from the ECG channels. An artifact detector and an optimal channel selector were developed based on a support vector machine (SVM) classifier with a radial basis function (RBF) kernel using features that are related to the ECG signal quality. Mean HR was estimated from the 24-hour armband recordings from 16 volunteers in segments of 10 seconds each. In addition, four classical HR variability (HRV) parameters (SDNN, RMSSD, and powers at low and high frequency bands) were computed. For comparison purposes, the same parameters were estimated also for data from a commercial Holter monitor. The armband provided usable data (difference less than 10% from Holter-estimated mean HR) during 75.25%/11.02% (inter-subject median/interquartile range) of segments when the user was not in bed, and during 98.49%/0.79% of the bed segments. The automatic artifact detector found 53.85%/17.09% of the data to be usable during the non-bed time, and 95.00%/2.35% to be usable during the time in bed. The HRV analysis obtained a relative error with respect to the Holter data not higher than 1.37% (inter-subject median/interquartile range). Although further studies have to be conducted for specific applications, results suggest that the armband device has a good potential for daily life HR monitoring, especially for applications such as arrhythmia or seizure detection, stress assessment, or sleep studies

A robust ECG denoising technique using variable frequency complex demodulation

Background and Objective Electrocardiogram (ECG) is widely used for the detection and diagnosis of cardiac arrhythmias such as atrial fibrillation. Most of the computer-based automatic cardiac abnormality detection algorithms require accurate identification of ECG components such as QRS complexes in order to provide a reliable result. However, ECGs are often contaminated by noise and artifacts, especially if they are obtained using wearable sensors, therefore, identification of accurate QRS complexes often becomes challenging. Most of the existing denoising methods were validated using simulated noise added to a clean ECG signal and they did not consider authentically noisy ECG signals. Moreover, many of them are model-dependent and sampling-frequency dependent and require a large amount of computational time. Methods This paper presents a novel ECG denoising technique using the variable frequency complex demodulation (VFCDM) algorithm, which considers noises from a variety of sources. We used the sub-band decomposition of the noise-contaminated ECG signals using VFCDM to remove the noise components so that better-quality ECGs could be reconstructed. An adaptive automated masking is proposed in order to preserve the QRS complexes while removing the unnecessary noise components. Finally, the ECG was reconstructed using a dynamic reconstruction rule based on automatic identification of the severity of the noise contamination. The ECG signal quality was further improved by removing baseline drift and smoothing via adaptive mean filtering. Results Evaluation results on the standard MIT-BIH Arrhythmia database suggest that the proposed denoising technique provides superior denoising performance compared to studies in the literature. Moreover, the proposed method was validated using real-life noise sources collected from the noise stress test database (NSTDB) and data from an armband ECG device which contains significant muscle artifacts. Results from both the wearable armband ECG data and NSTDB data suggest that the proposed denoising method provides significantly better performance in terms of accurate QRS complex detection and signal to noise ratio (SNR) improvement when compared to some of the recent existing denoising algorithms. Conclusions The detailed qualitative and quantitative analysis demonstrated that the proposed denoising method has been robust in filtering varieties of noises present in the ECG. The QRS detection performance of the denoised armband ECG signals indicates that the proposed denoising method has the potential to increase the amount of usable armband ECG data, thus, the armband device with the proposed denoising method could be used for long term monitoring of atrial fibrillation

Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients\u27 AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices

Using the redundant convolutional encoder–decoder to denoise QRS complexes in ECG signals recorded with an armband wearable device

Long-term electrocardiogram (ECG) recordings while performing normal daily routines are often corrupted with motion artifacts, which in turn, can result in the incorrect calculation of heart rates. Heart rates are important clinical information, as they can be used for analysis of heart-rate variability and detection of cardiac arrhythmias. In this study, we present an algorithm for denoising ECG signals acquired with a wearable armband device. The armband was worn on the upper left arm by one male participant, and we simultaneously recorded three ECG channels for 24 h. We extracted 10-s sequences from armband recordings corrupted with added noise and motion artifacts. Denoising was performed using the redundant convolutional encoder–decoder (R-CED), a fully convolutional network. We measured the performance by detecting R-peaks in clean, noisy, and denoised sequences and by calculating signal quality indices: signal-to-noise ratio (SNR), ratio of power, and cross-correlation with respect to the clean sequences. The percent of correctly detected R-peaks in denoised sequences was higher than in sequences corrupted with either added noise (70–100% vs. 34–97%) or motion artifacts (91.86% vs. 61.16%). There was notable improvement in SNR values after denoising for signals with noise added (7–19 dB), and when sequences were corrupted with motion artifacts (0.39 dB). The ratio of power for noisy sequences was significantly lower when compared to both clean and denoised sequences. Similarly, cross-correlation between noisy and clean sequences was significantly lower than between denoised and clean sequences. Moreover, we tested our denoising algorithm on 60-s sequences extracted from recordings from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and obtained improvement in SNR values of 7.08 ± 0.25 dB (mean ± standard deviation (sd)). These results from a diverse set of data suggest that the proposed denoising algorithm improves the quality of the signal and can potentially be applied to most ECG measurement devices