AndroDex: Android Dex Images of Obfuscated Malware.

With the emergence of technology and the usage of a large number of smart devices, cyber threats are increasing. Therefore, research studies have shifted their attention to detecting Android malware in recent years. As a result, a reliable and large-scale malware dataset is essential to build effective malware classifiers. In this paper, we have created AndroDex: an Android malware dataset containing a total of 24,746 samples that belong to more than 180 malware families. These samples are based on .dex images that truly reflect the characteristics of malware. To construct this dataset, we first downloaded the APKs of the malware, applied obfuscation techniques, and then converted them into images. We believe this dataset will significantly enhance a series of research studies, including Android malware detection and classification, and it will also boost deep learning classification efforts, among others. The main objective of creating images based on the Android dataset is to help other malware researchers better understand how malware works. Additionally, an important result of this study is that most malware nowadays employs obfuscation techniques to hide their malicious activities. However, malware images can overcome such issues. The main limitation of this dataset is that it contains images based on .dex files that are based on static analysis. However, dynamic analysis takes time, therefore, to overcome the issue of time and space this dataset can be used for the initial examination of any .apk files.

Hierarchical lifelong topic modeling using rules extracted from network communities.

Topic models extract latent concepts from texts in the form of topics. Lifelong topic models extend topic models by learning topics continuously based on accumulated knowledge from the past which is updated continuously as new information becomes available. Hierarchical topic modeling extends topic modeling by extracting topics and organizing them into a hierarchical structure. In this study, we combine the two and introduce hierarchical lifelong topic models. Hierarchical lifelong topic models not only allow to examine the topics at different levels of granularity but also allows to continuously adjust the granularity of the topics as more information becomes available. A fundamental issue in hierarchical lifelong topic modeling is the extraction of rules that are used to preserve the hierarchical structural information among the rules and will continuously update based on new information. To address this issue, we introduce a network communities based rule mining approach for hierarchical lifelong topic models (NHLTM). The proposed approach extracts hierarchical structural information among the rules by representing textual documents as graphs and analyzing the underlying communities in the graph. Experimental results indicate improvement of the hierarchical topic structures in terms of topic coherence that increases from general to specific topics.

A Hybrid Intelligent Framework to Combat Sophisticated Threats in Secure Industries.

With the new advancements in Internet of Things (IoT) and its applications in different sectors, such as the industrial sector, by connecting billions of devices and instruments, IoT has evolved as a new paradigm known as the Industrial Internet of Things (IIoT). Nonetheless, its benefits and applications have been approved in different areas, but there are possibilities for various cyberattacks because of its extensive connectivity and diverse nature. Such attacks result in financial loss and data breaches, which urge a consequential need to secure IIoT infrastructure. To combat the threats in the IIoT environment, we proposed a deep-learning SDN-enabled intelligent framework. A hybrid classifier is used for threat detection purposes, i.e., Cu-LSTMGRU + Cu-BLSTM. The proposed model achieved a better detection accuracy with low false-positive rate. We have conducted 10-fold cross-validation to show the unbiasdness of the results. The proposed scheme results are compared with Cu-DNNLSTM and Cu-DNNGRU classifiers, which were tested and trained on the same dataset. We have further compared the proposed model with other existing standard classifiers for a thorough performance evaluation. Results achieved by our proposed scheme are impressive with respect to speed efficiency, F1 score, accuracy, precision, and other evaluation metrics.

Gender and racial trends among neurology residents: an overview.

Diversification of academic medicine improves healthcare standards and patient outcomes. Gender and racial inequalities are major challenges faced by the healthcare system. This article reviews the trends of gender and racial disparity among residents of neurology. This retrospective analysis of the annual Accreditation Council for Graduate Medical Education Data Resource Books encompassed all residents at US neurology residency training programmes from the year 2007 to 2018. The representation of women steadily increased, with an absolute increase of 3% from the year 2007 to 2018. Although the absolute change (%) increased for the White race, Asian/Pacific Islander, Black/African Americans, there was a decrease seen in the Hispanic representation in neurology residents from the year 2011 to 2018. There was no change seen for the Native Americans/Alaskans. Our study concluded that gender and racial disparity persists in the recruitment of residents in neurology. This study highlights the need for targeted interventions to address gender and racial disparity among residents of neurology. Further studies are needed to explore etiological factors to address gender and racial disparity.

A Hybrid Deep Learning-Driven SDN Enabled Mechanism for Secure Communication in Internet of Things (IoT).

The Internet of Things (IoT) has emerged as a new technological world connecting billions of devices. Despite providing several benefits, the heterogeneous nature and the extensive connectivity of the devices make it a target of different cyberattacks that result in data breach and financial loss. There is a severe need to secure the IoT environment from such attacks. In this paper, an SDN-enabled deep-learning-driven framework is proposed for threats detection in an IoT environment. The state-of-the-art Cuda-deep neural network, gated recurrent unit (Cu- DNNGRU), and Cuda-bidirectional long short-term memory (Cu-BLSTM) classifiers are adopted for effective threat detection. We have performed 10 folds cross-validation to show the unbiasedness of results. The up-to-date publicly available CICIDS2018 data set is introduced to train our hybrid model. The achieved accuracy of the proposed scheme is 99.87%, with a recall of 99.96%. Furthermore, we compare the proposed hybrid model with Cuda-Gated Recurrent Unit, Long short term memory (Cu-GRULSTM) and Cuda-Deep Neural Network, Long short term memory (Cu- DNNLSTM), as well as with existing benchmark classifiers. Our proposed mechanism achieves impressive results in terms of accuracy, F1-score, precision, speed efficiency, and other evaluation metrics.

Bilateral thalamic infarction due to artery of Percheron occlusion: A case report.

The artery of Percheron is a rare variant of the posterior cerebral circulation. It is characterised by a single arterial trunk that supplies blood to bilateral paramedian thalami and rostral midbrain. Its occlusion can have a very wide range of presentation, and initial imaging including CT of the head maybe normal. Diagnosis and eventual treatment is usually delayed. We describe the case of an elderly man who presented with loss of consciousness, aphasia, and bilateral lower limb weakness. He was diagnosed with bilateral thalamic infarction due to the occlusion of the artery of Percheron only after an MRI of the brain was performed. Despite treatment his symptoms did not resolve completely.

Cerebral air embolism from a Central Venous Catheter: A timely reminder of the importance of rapid diagnosis.

Cerebral air embolism (CAE) is a rare, avoidable and potentially fatal iatrogenic complication. Here, we report a case of CAE associated with a central venous catheter in the internal jugular vein that resulted in neurological deficits and generalised epileptic seizures. A 64-year-old man admitted for fasciotomy for compartment syndrome developed CAE with left-sided neurological deficits. The suspected origin was retrograde air flow from the right internal jugular venous catheter. The air spontaneously resorbed without the need for specific therapy, and he made a good recovery. CAE is an infrequent iatrogenic complication that requires prompt diagnosis to avoid significant morbidity and mortality. This case serves as a timely reminder that adverse outcome such as stroke, seizures or death can be avoided by a high index of suspicion and prompt diagnosis. Hyperbaric oxygen is the prime therapeutic measure, but high-quality evidence on its clinical value is lacking.

Online Knowledge-Based Model for Big Data Topic Extraction.

Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half.

Successful Use of Intravenous Tissue Plasminogen Activator as Treatment for a Patient with Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy: A Case Report and Review of Literature.

BACKGROUND: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is considered a common cause of hereditary stroke caused by mutation of the NOTCH3 gene. Evidence against the use of intravenous tissue plasminogen activator (IV tPA) has been suggested due to possibility of hemorrhage. We present a case of a patient with CADASIL who was successfully treated using IV tPA. METHODS: A case description of a female patient who presented with stroke-like symptoms was a previously known case of CADASIL. Review of literature was done using search terms such as CADASIL, NOTCH3, and intracranial hemorrhage or brain hemorrhage. RESULTS: A 35-year-old female patient presented to the emergency department with acute onset hemiparesis, hemiparesthesia, and motor aphasia with a National Institutes of Health Stroke Scale score of 8. The patient was a previously diagnosed case of CADASIL with a positive NOTCH3 mutation. Computed tomography scan showed no large vessel occlusion with no perfusion deficient. Patient was within window for IV tPA treatment which was administered, and she subsequently had marked improvement of all symptoms. CONCLUSION: There is slight evidence against the use of IV tPA for CADASIL patients who present with stroke-like symptoms but nothing is concrete. It has also been suggested that some patients who are undiagnosed have been treated with IV tPA with favorable results but unfortunately are not reported. Further studies and or large clinical trials could be beneficial for those patients who may benefit from IV tPA and who have previously been diagnosed with CADASIL.

Transient Global Amnesia as the First Clinical Symptom for Malignant B-Cell Lymphoma with Central Nervous System Involvement.

We present the case of an elderly male who was diagnosed with transient global amnesia (TGA), only to be diagnosed with B-cell lymphoma with central nervous system involvement a few weeks later. This is the first ever case reported in literature with lymphoma presenting as TGA. Literature review and pertinent points regarding high-yield imaging protocol for presumed TGA patients are discussed.