Analyzing and comparing the effectiveness of malware detection: A study of machine learning approaches.

The Internet has become a vital source of knowledge and communication in recent times. Continuous technological advancements have changed the way businesses operate, and everyone today lives in the digital world of engineering. Because of the Internet of Things (IoT) and its applications, people's impressions of the information revolution have improved. Malware detection and categorization are becoming more of a problem in the cybersecurity world. As a result, strong security on the Internet could protect billions of internet users from harmful behavior. In malware detection and classification techniques, several types of deep learning models are used; however, they still have limitations. This study will explore malware detection and classification elements using modern machine learning (ML) approaches, including K-Nearest Neighbors (KNN), Extra Tree (ET), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and neural network Multilayer Perceptron (nnMLP). The proposed study uses the publicly available dataset UNSWNB15. In our proposed work, we applied the feature encoding method to convert our dataset into purely numeric values. After that, we applied a feature selection method named Term Frequency-Inverse Document Frequency (TFIDF) based on entropy for the best feature selection. The dataset is then balanced and provided to the ML models for classification. The study concludes that Random Forest, out of all tested ML models, yielded the best accuracy of 97.68 %.

Cyberterrorism as a global threat: a review on repercussions and countermeasures.

An act of cyberterrorism involves using the internet and other forms of information and communication technology to threaten or cause bodily harm to gain political or ideological power through threat or intimidation. Data theft, data manipulation, and disruption of essential services are all forms of cyberattacks. As digital infrastructure becomes more critical and entry barriers for malicious actors decrease, cyberterrorism has become a growing concern. Detecting, responding, and preventing this crime presents unique challenges for law enforcement and governments, which require a multifaceted approach. Cyberterrorism can have devastating effects on a wide range of people and organizations. A country's reputation and stability can be damaged, financial losses can occur, and in some cases, even lives can be lost. As a result of cyberattacks, critical infrastructure, such as power grids, hospitals, and transportation systems, can also be disrupted, leading to widespread disruptions and distress. The past ten years have seen several cyber-attacks around the globe including WannaCry attack (2017), Yahoo data breaches (2013-2014), OPM data breach (2015), SolarWinds supply chain attack (2020) etc. This study covers some of the cyberterrorism events that have happened in the past ten years, their target countries, their devastating effects, their impacts on nation's economy, political instability, and measures adopted to counter them over the passage of time. Our survey-based research on cyberterrorism will complement existing literature by providing valuable empirical data, understanding of perceptions and awareness, and insights into targeted populations. It can contribute to the development of better measurement tools, strategies, and policies for countering cyberterrorism.

An Urdu speech corpus for emotion recognition.

Emotion recognition from acoustic signals plays a vital role in the field of audio and speech processing. Speech interfaces offer humans an informal and comfortable means to communicate with machines. Emotion recognition from speech signals has a variety of applications in the area of human computer interaction (HCI) and human behavior analysis. In this work, we develop the first emotional speech database of the Urdu language. We also develop the system to classify five different emotions: sadness, happiness, neutral, disgust, and anger using different machine learning algorithms. The Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coefficient (LPC), energy, spectral flux, spectral centroid, spectral roll-off, and zero-crossing were used as speech descriptors. The classification tests were performed on the emotional speech corpus collected from 20 different subjects. To evaluate the quality of speech emotions, subjective listing tests were conducted. The recognition of correctly classified emotions in the complete Urdu emotional speech corpus was 66.5% with K-nearest neighbors. It was found that the disgust emotion has a lower recognition rate as compared to the other emotions. Removing the disgust emotion significantly improves the performance of the classifier to 76.5%.

The association between late preterm birth and cardiometabolic conditions across the life course: A systematic review and meta-analysis.

BACKGROUND: The effect of being born late preterm (34-36 weeks gestation) on cardiometabolic outcomes across the life course is unclear. OBJECTIVES: To systematically review the association between being born late preterm (spontaneous or indicated), compared to the term and cardiometabolic outcomes in children and adults. DATA SOURCES: EMBASE(Ovid), MEDLINE(Ovid), CINAHL. STUDY SELECTION AND DATA EXTRACTION: Observational studies up to July 2021 were included. Study characteristics, gestational age, cardiometabolic outcomes, risk ratios (RRs), odds ratios (ORs), hazard ratios (HRs), mean differences and 95% confidence intervals (CIs) were extracted. SYNTHESIS: We pooled converted RRs using random-effects meta-analyses for diabetes, hypertension, ischemic heart disease (IHD) and body mass index (BMI) with subgroups for children and adults. The risk of bias was assessed using the Newcastle-Ottawa scale and certainty of the evidence was assessed using the grading of recommendations, assessment, development and evaluation (GRADE) approach. RESULTS: Forty-one studies were included (41,203,468 total participants; median: 5.0% late preterm). Late preterm birth was associated with increased diabetes (RR 1.24, 95% CI 1.17, 1.32; nine studies; n = 6,056,511; incidence 0.9%; I2 51%; low certainty) and hypertension (RR 1.21, 95% CI 1.13, 1.30; 11 studies; n = 3,983,141; incidence 3.4%; I2 64%; low certainty) in children and adults combined. Late preterm birth was associated with decreased BMI z-scores in children (standard mean difference -0.38; 95% CI -0.67, -0.09; five studies; n = 32,602; proportion late preterm 8.3%; I2 96%; very low certainty). There was insufficient evidence that late preterm birth was associated with increased IHD risk in adults (HR 1.20, 95% CI 0.89, 1.62; four studies; n = 2,706,806; incidence 0.3%; I2 87%; very low certainty). CONCLUSIONS: Late preterm birth was associated with an increased risk of diabetes and hypertension. The certainty of the evidence was low or very low. Inconsistencies in late preterm and term definitions, confounding variables and outcome age limited the comparability of studies.

Impact of disasters, including pandemics, on cardiometabolic outcomes across the life-course: a systematic review.

BACKGROUND: Disasters are events that disrupt the daily functioning of a community or society, and may increase long-term risk of adverse cardiometabolic outcomes, including cardiovascular disease, obesity and diabetes. The objective of this study was to conduct a systematic review to determine the impact of disasters, including pandemics, on cardiometabolic outcomes across the life-course. DESIGN: A systematic search was conducted in May 2020 using two electronic databases, EMBASE and Medline. All studies were screened in duplicate at title and abstract, and full-text level. Studies were eligible for inclusion if they assessed the association between a population-level or community disaster and cardiometabolic outcomes ≥1 month following the disaster. There were no restrictions on age, year of publication, country or population. Data were extracted on study characteristics, exposure (eg, type of disaster, region, year), cardiometabolic outcomes and measures of effect. Study quality was evaluated using the Joanna Briggs Institute critical appraisal tools. RESULTS: A total of 58 studies were included, with 24 studies reporting the effects of exposure to disaster during pregnancy/childhood and 34 studies reporting the effects of exposure during adulthood. Studies included exposure to natural (n=35; 60%) and human-made (n=23; 40%) disasters, with only three (5%) of these studies evaluating previous pandemics. Most studies reported increased cardiometabolic risk, including increased cardiovascular disease incidence or mortality, diabetes and obesity, but not all. Few studies evaluated the biological mechanisms or high-risk subgroups that may be at a greater risk of negative health outcomes following disasters. CONCLUSIONS: The findings from this study suggest that the burden of disasters extend beyond the known direct harm, and attention is needed on the detrimental indirect long-term effects on cardiometabolic health. Given the current COVID-19 pandemic, these findings may inform public health prevention strategies to mitigate the impact of future cardiometabolic risk. PROSPERO REGISTRATION NUMBER: CRD42020186074.

GenInfoGuard--a robust and distortion-free watermarking technique for genetic data.

Genetic data, in digital format, is used in different biological phenomena such as DNA translation, mRNA transcription and protein synthesis. The accuracy of these biological phenomena depend on genetic codes and all subsequent processes. To computerize the biological procedures, different domain experts are provided with the authorized access of the genetic codes; as a consequence, the ownership protection of such data is inevitable. For this purpose, watermarks serve as the proof of ownership of data. While protecting data, embedded hidden messages (watermarks) influence the genetic data; therefore, the accurate execution of the relevant processes and the overall result becomes questionable. Most of the DNA based watermarking techniques modify the genetic data and are therefore vulnerable to information loss. Distortion-free techniques make sure that no modifications occur during watermarking; however, they are fragile to malicious attacks and therefore cannot be used for ownership protection (particularly, in presence of a threat model). Therefore, there is a need for a technique that must be robust and should also prevent unwanted modifications. In this spirit, a watermarking technique with aforementioned characteristics has been proposed in this paper. The proposed technique makes sure that: (i) the ownership rights are protected by means of a robust watermark; and (ii) the integrity of genetic data is preserved. The proposed technique-GenInfoGuard-ensures its robustness through the "watermark encoding" in permuted values, and exhibits high decoding accuracy against various malicious attacks.