Efficient Non-Linear Covert Channel Detection in TCP Data Streams

Cyber-attacks are causing losses amounted to billions of dollars every year due to data breaches and vulnerabilities. The existing tools for data leakage prevention and detection are often bypassed by using various different types of sophisticated techniques such as network steganography for stealing the data. This is due to several weaknesses which can be exploited by a threat actor in existing detection systems. The weaknesses are high time and memory training complexities as well as large training datasets. These challenges become worse when the amount of generated data increases in every second in many realms. In addition, the number of false positives is high which makes them inaccurate. Finally, there is a lack of a framework catering for the needs such as raising alerts as well as data monitoring and updating/adapting of a threshold value used for checking the data packets for covert data. In order to overcome these weaknesses, this paper proposes a novel framework that includes elements such as continuous data monitoring, threshold maintenance, and alert notification. This paper also proposes a model based on statistical measures to detect covert data leakages, especially for non-linear chaotic data. The main advantage of the proposed model is its capability to provide results with tolerance/threshold values much more efficiently. Our experiments indicate that the proposed framework has low false positives and outperforms various existing techniques in terms of accuracy and efficiency

Neurocomputing for internet of things: object recognition and detection strategy

Modern and new integrated technologies have changed the traditional systems by using more advanced machine learning, artificial intelligence methods, new generation standards, and smart and intelligent devices. The new integrated networks like the Internet of Things (IoT) and 5G standards offer various benefits and services. However, these networks have suffered from multiple object detection, localization, and classification issues. Conventional Neural Networks (CNN) and their variants have been adopted for object detection, classification, and localization in IoT networks to create autonomous devices to make decisions and perform tasks without human intervention and helpful to learn in-depth features. Motivated by these facts, this paper investigates existing object detection and recognition techniques by using CNN models used in IoT networks. This paper presents a Conventional Neural Networks for 5G-Enabled Internet of Things Network (CNN-5GIoT) model for moving and static objects in IoT networks after a detailed comparison. The proposed model is evaluated with existing models to check the accuracy of real-time tracking. The proposed model is more efficient for real-time object detection and recognition than conventional methods

Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markers

Objective: To devise and validate radiomic signatures of impending hematoma expansion (HE) based on admission non-contrast head computed tomography (CT) of patients with intracerebral hemorrhage (ICH). Methods: Utilizing a large multicentric clinical trial dataset of hypertensive patients with spontaneous supratentorial ICH, we developed signatures predictive of HE in a discovery cohort (n = 449) and confirmed their performance in an independent validation cohort (n = 448). In addition to n = 1,130 radiomic features, n = 6 clinical variables associated with HE, n = 8 previously defined visual markers of HE, the BAT score, and combinations thereof served as candidate variable sets for signatures. The area under the receiver operating characteristic curve (AUC) quantified signatures’ performance. Results: A signature combining select radiomic features and clinical variables attained the highest AUC (95% confidence interval) of 0.67 (0.61–0.72) and 0.64 (0.59–0.70) in the discovery and independent validation cohort, respectively, significantly outperforming the clinical (pdiscovery = 0.02, pvalidation = 0.01) and visual signature (pdiscovery = 0.03, pvalidation = 0.01) as well as the BAT score (pdiscovery < 0.001, pvalidation < 0.001). Adding visual markers to radiomic features failed to improve prediction performance. All signatures were significantly (p < 0.001) correlated with functional outcome at 3-months, underlining their prognostic relevance. Conclusion: Radiomic features of ICH on admission non-contrast head CT can predict impending HE with stable generalizability; and combining radiomic with clinical predictors yielded the highest predictive value. By enabling selective anti-expansion treatment of patients at elevated risk of HE in future clinical trials, the proposed markers may increase therapeutic efficacy, and ultimately improve outcomes

The coronal plane maximum diameter of deep intracerebral hemorrhage predicts functional outcome more accurately than hematoma volume

Background: Among prognostic imaging variables, the hematoma volume on admission computed tomography (CT) has long been considered the strongest predictor of outcome and mortality in intracerebral hemorrhage. Aims: To examine whether different features of hematoma shape are associated with functional outcome in deep intracerebral hemorrhage. Methods: We analyzed 790 patients from the ATACH-2 trial, and 14 shape features were quantified. We calculated Spearman’s Rho to assess the correlation between shape features and three-month modified Rankin scale (mRS) score, and the area under the receiver operating characteristic curve (AUC) to quantify the association between shape features and poor outcome defined as mRS>2 as well as mRS > 3. Results: Among 14 shape features, the maximum intracerebral hemorrhage diameter in the coronal plane was the strongest predictor of functional outcome, with a maximum coronal diameter >∼3.5 cm indicating higher three-month mRS scores. The maximum coronal diameter versus hematoma volume yielded a Rho of 0.40 versus 0.35 (p = 0.006), an AUC[mRS>2] of 0.71 versus 0.68 (p = 0.004), and an AUC[mRS>3] of 0.71 versus 0.69 (p = 0.029). In multiple regression analysis adjusted for known outcome predictors, the maximum coronal diameter was independently associated with three-month mRS (p < 0.001). Conclusions: A coronal-plane maximum diameter measurement offers greater prognostic value in deep intracerebral hemorrhage than hematoma volume. This simple shape metric may expedite assessment of admission head CTs, offer a potential biomarker for hematoma size eligibility criteria in clinical trials, and may substitute volume in prognostic intracerebral hemorrhage scoring systems

Protocol for a prospective collaborative systematic review and meta-analysis of individual patient data from randomised controlled trials of vasoactive drugs in acute stroke: the Blood pressure in Acute Stroke Collaboration, stage-3 (BASC-3)

Rationale Despite several large clinical trials assessing blood pressure lowering in acute stroke, equipoise remains, particularly for ischaemic stroke. The ‘Blood pressure in Acute Stroke Collaboration’ (BASC) commenced in the mid 1990s focusing on systematic reviews and meta-analysis of blood pressure lowering in acute stroke. From the start, BASC planned to assess safety and efficacy of blood pressure lowering in acute stroke using individual patient data. Aims To determine the optimal management of blood pressure in patients with acute stroke, encompassing both intracerebral haemorrhage and ischaemic stroke. Secondary aims are to assess which clinical and therapeutic factors may alter the optimal management of high blood pressure in patients with acute stroke and to assess the effect of vasoactive treatments on haemodynamic variables. Methods and design Individual patient data from randomised controlled trials of blood pressure management in participants with ischaemic stroke and/or intracerebral haemorrhage enrolled during the ultra-acute (pre-hospital), hyper-acute (<6 hours), acute (<48 hours) and sub-acute (<168 hours) phases of stroke. Study outcomes The primary effect variable will be functional outcome defined by the ordinal distribution of the modified Rankin Scale; analyses will also be carried out in prespecified subgroups to assess the modifying effects of stroke-related and pre-stroke patient characteristics. Key secondary variables will include clinical, haemodynamic and neuroradiological variables; safety variables will comprise death and serious adverse events. Discussion Study questions will be addressed in stages, according to the protocol, before integrating these into a final overreaching analysis. We invite eligible trials to join the collaboration

Glutathione and glutamate in schizophrenia: a 7T MRS study

In schizophrenia, abnormal neural metabolite concentrations may arise from cortical damage following neuroinflammatory processes implicated in acute episodes. Inflammation is associated with increased glutamate, whereas the antioxidant glutathione may protect against inflammation-induced oxidative stress. We hypothesized that patients with stable schizophrenia would exhibit a reduction in glutathione, glutamate and/or glutamine in the cerebral cortex, consistent with a postinflammatory response, and that this reduction would be most marked in patients with residual schizophrenia an early stage with positive psychotic symptoms has progressed to a late stage characterised by long-term negative symptoms and impairments. We recruited 28 patients with stable schizophrenia and 45 healthy participants matched for age, gender and parental socio-economic status. We measured glutathione, glutamate and glutamine concentrations in the anterior cingulate cortex (ACC), left insula, and visual cortex using 7T proton Magnetic Resonance Spectroscopy (MRS). Glutathione and glutamate were significantly correlated in all three voxels. Glutamine concentrations across the three voxels were significantly correlated with each other. Principal Components Analysis (PCA) produced three clear components: an ACC glutathione-glutamate component; an insula-visual glutathione-glutamate component; and a glutamine component. Patients with stable schizophrenia had significantly lower scores on the ACC glutathione-glutamate component, an effect almost entirely leveraged by the sub-group of patients with residual schizophrenia. All three metabolite concentration values in the ACC were significantly reduced in this group. These findings are consistent with the hypothesis that excito-toxicity during the acute phase of illness leads to reduced glutathione and glutamate in the residual phase of the illness

MRI compared to conventional diagnostic work-up in the detection and evaluation of invasive lobular carcinoma of the breast: a review of existing literature

Item does not contain fulltextPURPOSE: The clinical diagnosis and management of invasive lobular carcinoma (ILC) of the breast presents difficulties. Magnetic resonance imaging (MRI) has been proposed as the imaging modality of choice for the evaluation of ILC. Small studies addressing different aspects of MRI in ILC have been presented but no large series to date. To address the usefulness of MRI in the work-up of ILC, we performed a review of the currently published literature. MATERIALS AND METHODS: We performed a literature search using the query "lobular AND (MRI OR MR OR MRT OR magnetic)" in the Cochrane library, PubMed and scholar.google.com, to retrieve all articles that dealt with the use of MRI in patients with ILC. We addressed sensitivity, morphologic appearance, correlation with pathology, detection of additional lesions, and impact of MRI on surgery as different endpoints. Whenever possible we performed meta-analysis of the pooled data. RESULTS: Sensitivity is 93.3% and equal to overall sensitivity of MRI for malignancy in the breast. Morphologic appearance is highly heterogeneous and probably heavily influenced by interreader variability. Correlation with pathology ranges from 0.81 to 0.97; overestimation of lesion size occurs but is rare. In 32% of patients, additional ipsilateral lesions are detected and in 7% contralateral lesions are only detected by MRI. Consequently, MRI induces change in surgical management in 28.3% of cases. CONCLUSION: This analysis indicates MRI to be valuable in the work-up of ILC. It provides additional knowledge that cannot be obtained by conventional imaging modalities which can be helpful in patient treatment

Harnessing citizen science through mobile phone technology to screen for immunohistochemical biomarkers in bladder cancer

Background: Immunohistochemistry (IHC) is often used in personalisation of cancer treatments. Analysis of large data sets to uncover predictive biomarkers by specialists can be enormously time-consuming. Here we investigated crowdsourcing as a means of reliably analysing immunostained cancer samples to discover biomarkers predictive of cancer survival. Methods: We crowdsourced the analysis of bladder cancer TMA core samples through the smartphone app ‘Reverse the Odds’. Scores from members of the public were pooled and compared to a gold standard set scored by appropriate specialists. We also used crowdsourced scores to assess associations with disease-specific survival. Results: Data were collected over 721 days, with 4,744,339 classifications performed. The average time per classification was approximately 15 s, with approximately 20,000 h total non-gaming time contributed. The correlation between crowdsourced and expert H-scores (staining intensity × proportion) varied from 0.65 to 0.92 across the markers tested, with six of 10 correlation coefficients at least 0.80. At least two markers (MRE11 and CK20) were significantly associated with survival in patients with bladder cancer, and a further three markers showed results warranting expert follow-up. Conclusions: Crowdsourcing through a smartphone app has the potential to accurately screen IHC data and greatly increase the speed of biomarker discovery