Blockchain Based Solutions to Mitigate Distributed Denial of Service (DDoS) Attacks in the Internet of Things (IoT): A Survey.

Internet of Things (IoT) devices are widely used in many industries including smart cities, smart agriculture, smart medical, smart logistics, etc. However, Distributed Denial of Service (DDoS) attacks pose a serious threat to the security of IoT. Attackers can easily exploit the vulnerabilities of IoT devices and control them as part of botnets to launch DDoS attacks. This is because IoT devices are resource-constrained with limited memory and computing resources. As an emerging technology, Blockchain has the potential to solve the security issues in IoT. Therefore, it is important to analyse various Blockchain-based solutions to mitigate DDoS attacks in IoT. In this survey, a detailed survey of various Blockchain-based solutions to mitigate DDoS attacks in IoT is carried out. First, we discuss how the IoT networks are vulnerable to DDoS attacks, its impact over IoT networks and associated services, the use of Blockchain as a potential technology to address DDoS attacks, in addition to challenges of Blockchain implementation in IoT. We then discuss various existing Blockchain-based solutions to mitigate the DDoS attacks in the IoT environment. Then, we classify existing Blockchain-based solutions into four categories i.e., Distributed Architecture-based solutions, Access Management-based solutions, Traffic Control-based solutions and the Ethereum Platform-based solutions. All the solutions are critically evaluated in terms of their working principles, the DDoS defense mechanism (i.e., prevention, detection, reaction), strengths and weaknesses. Finally, we discuss future research directions that can be explored to design and develop better Blockchain-based solutions to mitigate DDoS attacks in IoT.

Automated cardiac motion compensation in PET/CT for accurate reconstruction of PET myocardial perfusion images.

Error-free reconstruction of PET data with a registered CT attenuation map is essential for accurate quantification and interpretation of cardiac perfusion. Misalignment of the CT and PET data can produce an erroneous attenuation map that projects lung attenuation parameters onto the heart wall, thereby underestimating the attenuation and creating artifactual areas of hypoperfusion that can be misinterpreted as myocardial ischemia or infarction. The major causes of misregistration between CT and PET images are the respiratory motion, cardiac motion and gross physical motion of the patient. The misalignment artifact problem is overcome with automated cardiac registration software that minimizes the alignment error between the two modalities. Results show that the automated registration process works equally well for any respiratory phase in which the CT scan is acquired. Further evaluation of this procedure on 50 patients demonstrates that the automated registration software consistently aligns the two modalities, eliminating artifactual hypoperfusion in reconstructed PET images due to PET/CT misregistration. With this registration software, only one CT scan is required for PET/CT imaging, which reduces the radiation dose required for CT-based attenuation correction and improves the clinical workflow for PET/CT.

MRI based automated diagnosis of Alzheimer's: Fusing 3D wavelet-features with clinical data.

This paper presents a novel algorithm for classification of patients with Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) from the healthy controls (HC) using structural MRI. Feature extraction is based on discrete 3D wavelet transform followed by PCA for transforming the feature space into linearly uncorrelated variables. Linear SVM is used for classification purposes with clinical dementia rating used as the target vector. Proposed methodology is fully automated and independent of the annotation of region of interest. The importance of MRI, demographical data, neuro-psychiatric test scores and statistics calculated over the wavelet coefficients for the classification is studied. Proposed methodology is applied on 197 subjects from a public database. A classification accuracy of 95% was achieved for the case of HC vs AD. For the case of HC vs MCI, and MCI vs AD the classification accuracy of 78% and 81% were achieved. The results are compared with an existing state of the art technique.

Automated PET/CT brain registration for accurate attenuation correction.

Computed Tomography (CT) is used for the attenuation correction of Positron Emission Tomography (PET) to enhance the efficiency of data acquisition process and to improve the quality of the reconstructed PET data in the brain. Due to the use of two different modalities, chances of misalignment between PET and CT images are quite significant. The main cause of this misregistration is the motion of the patient during the PET scan and between the PET and CT scans. This misalignment produces an erroneous CT attenuation map that can project the bone and water attenuation parameters onto the brain, thereby under- or over-estimating the attenuation. To avoid the misregistration artifact and potential diagnostic misinterpretation, automated software for PET/CT brain registration has been developed. This software extracts the brain surface information from the CT and PET images and compensates for the translational and rotational misalignment between the two scans. This procedure has been applied to the dataset of a patient with visible perfusion defect in the brain, and the results show that the CTAC produced after the image registration eliminates that hypoperfusion artifact caused by the erroneous attenuation of the PET images.

An unusual N-terminal deletion of the laminin alpha3a isoform leads to the chronic granulation tissue disorder laryngo-onycho-cutaneous syndrome.

Laryngo-onycho-cutaneous (LOC or Shabbir) syndrome (OMIM 245660) is an autosomal recessive epithelial disorder confined to the Punjabi Muslim population. The condition is characterized by cutaneous erosions, nail dystrophy and exuberant vascular granulation tissue in certain epithelia, especially conjunctiva and larynx. Genome-wide homozygosity mapping localized the gene to a 2 Mb region on chromosome 18q11.2 with an LOD score of 19.8 at theta=0. This region includes the laminin alpha3 gene (LAMA3), in which loss-of-expression mutations cause the lethal skin blistering disorder Herlitz junctional epidermolysis bullosa. Detailed investigation showed that this gene possesses a further 38 exons (76 exons in total) spanning 318 kb of genomic DNA, and encodes three distinct proteins, designated laminin alpha3a, alpha3b1 and alpha3b2. The causative mutation in 15 families was a frameshift mutation 151insG predicting a stop codon 7 bp downstream in an exon that is specific to laminin alpha3a. This protein is secreted only by the basal keratinocytes of stratified epithelia, implying that LOC is caused by dysfunction of keratinocyte-mesenchymal communication. Surprisingly, the 151insG mutation does not result in nonsense-mediated mRNA decay due to rescue of the transcript by an alternative translation start site 6 exons downstream. The resultant N-terminal deletion of laminin alpha3a was confirmed by immunoprecipitation of secreted proteins from LOC keratinocytes. These studies show that the laminin alpha3a N-terminal domain is a key regulator of the granulation tissue response, with important implications not only in LOC but in a range of other clinical conditions associated with abnormal wound healing.