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BackgroundConsideration is needed when using Janus kinase (JAK) inhibitors to treat RA in pts aged ≥65 years or those with cardiovascular (CV) risk factors. The JAK1 preferential inhibitor FIL was generally well tolerated in clinical trials[1];safety has not been determined in a real-world setting.ObjectivesTo report baseline characteristics and up to 6-month safety data from the first 480 pts treated with FIL in the FILOSOPHY study (NCT04871919), and in two mutually exclusive subgroups based on age and CV risk.MethodsFILOSOPHY is an ongoing, phase 4, non-interventional, European study of pts with RA who have been prescribed FIL for the first time and in accordance with the product label in daily practice. Baseline characteristics and the incidence of select adverse events (AEs) are assessed in pts aged ≥65 years and/or with ≥1 CV risk factor (Table 1), and in those aged <65 years with no CV risk factors.ResultsAs of the end of June 2022, 480 pts had been treated: 441 received FIL 200 mg and 39 received FIL 100 mg. Of the 480 pts, 148 (30.8%) were aged ≥65 years;332 (69.2%) were aged <65 years. In total, 86 (17.9%) were former smokers, 81 (16.9%) were current smokers and 203 (42.3%) were non-smokers (data were missing for 110 pts [22.9%]). In addition to smoking, the most frequent CV risk factors included a history of hypertension (32.3%), a history of dyslipidemia (10.2%) and a family history of myocardial infarction (8.5%;Table 1).23 pts (4.8%) discontinued treatment due to AEs. Of the 354 pts aged ≥65 years or with ≥1 CV risk factor, infections affected 64 pts (18.1%), 34 (9.6%) had COVID-19, 2 (0.6%) had herpes zoster, and cardiac disorders (angina pectoris, atrial fibrillation, palpitations and tachycardia) affected 5 pts (1.4%);no cases of malignancies were observed. In the subgroup aged <65 years and with no CV risk factors (n=126), infections occurred in 18 pts (14.3%) (9 [7.1%] had COVID-19;3 [2.4%] had herpes zoster) and malignancies (myeloproliferative neoplasm) affected 1 pt (0.8%);no pts had cardiac disorders. There were no cases of deep vein thrombosis or pulmonary embolism in either subgroup.ConclusionIn this interim analysis of FILOSOPHY, no unexpected safety signals emerged at up to 6 months. Although infections and cardiac disorders affected a numerically greater proportion of pts aged ≥65 years or with ≥1 CV risk vs those aged <65 years with no CV risk, longer follow-up on a broader cohort is necessary to further characterize the safety of FIL in different groups of pts with RA.Reference[1]Winthrop K, et al. Ann Rheum Dis 2022;81:184–92Table 1.Baseline characteristics and CV risk factorsBaseline demographics/CV risk factorsAll FIL-treated pts (N=480)≥65 years or with ≥1 CV risk factor (n=354)<65 years and no CV risk factor (n=126)*Female sex, n (%)351 (73.1)252 (71.2)99 (78.6)Age, years, mean (SD)57.6 (11.5)60.4 (10.8)49.6 (9.6)Rheumatoid factor positive, n (%)†228 (47.5)167 (47.2)61 (48.4)Anti-citrullinated protein antibody positive, n (%)‡243 (50.6)176 (49.7)67 (53. 2)Body mass index, kg/m2, mean (SD)27.6 (5.7) n=43728.0 (5.4) n=33126.3 (6.4) n=106RA disease duration, years, mean (SD)10.4 (9.4) n=47810.5 (9.5) n=35310.0 (8.8) n=125Tender joint count 28, mean (SD)8.6 (6.9) n=4578.7 (7.1) n=3408.3 (6.3) n=117Swollen joint count 28, mean (SD)5.6 (5.2) n=4525.7 (5.4) n=3365.4 (4.4) n=116Former smoker, n (%)§86 (17.9)86 (24.3)0Current smoker, n (%)§81 (16.9)81 (22.9)0Non-smoker, n (%)§203 (42.3)130 (36.7)73 (57.9)Family history of myocardial infarction, n (%)41 (8.5)41 (11.6)0Medical history of: n (%) CV disease33 (6.9)33 (9.3)0 Diabetes35 (7.3)35 (9.9)0 Dyslipidemia49 (10.2)49 (13.8)0 Hypertension155 (32.3)155 (43.8)0 Ischemic CNS vascular disorders11 (2.3)11 (3.1)0 Peripheral vascular disease17 (3.5)17 (4.8)0*Includes 53 pts with missing smoking status data who were aged <65 years with no other CV risk factors.†Missing/unknown in 154 pts;‡Missing in 153 pts;§Smoking status data missing in 110 pts (22.9%).AcknowledgementsWe thank the physicia s and patients who participated in this study. The study was funded by Galapagos NV, Mechelen, Belgium. Publication coordination was provided by Fabien Debailleul, PhD, of Galapagos NV. Medical writing support was provided by Debbie Sherwood, BSc, CMPP (Aspire Scientific, Bollington, UK), and funded by Galapagos NV.Disclosure of InterestsPatrick Verschueren Speakers bureau: AbbVie, Eli Lilly, Galapagos, Roularta, Consultant of: Celltrion, Eli Lilly, Galapagos, Gilead, Nordic Pharma, Sidekick Health, Grant/research support from: Galapagos, Pfizer, Jérôme Avouac Speakers bureau: AbbVie, AstraZeneca, BMS, Eli Lilly, Galapagos, MSD, Novartis, Pfizer, Sandoz, Sanofi, Consultant of: AbbVie, Fresenius Kabi, Galapagos, Sanofi, Grant/research support from: BMS, Fresenius Kabi, Novartis, Pfizer, Karen Bevers Grant/research support from: Galapagos, Susana Romero-Yuste Speakers bureau: AbbVie, Biogen, BMS, Lilly, Pfizer, Consultant of: Sanofi, Lilly, Grant/research support from: Lilly, MSD, Roberto Caporali Speakers bureau: AbbVie, Amgen, BMS, Celltrion, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Pfizer, Sandoz, UCB, Consultant of: AbbVie, Amgen, BMS, Celltrion, Eli Lilly, Fresenius Kabi, Galapagos, Janssen, MSD, Novartis, Pfizer, Roche, Sandoz, UCB, Thomas Debray Consultant of: Biogen, Galapagos, Gilead, Francesco De Leonardis Employee of: Galapagos, James Galloway Speakers bureau: AbbVie, Biogen, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, Roche, UCB, Consultant of: AbbVie, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, Grant/research support from: AstraZeneca, Celgene, Gilead, Janssen, Medicago, Novavax, Pfizer, Monia Zignani Shareholder of: Galapagos, Employee of: Galapagos, Gerd Rüdiger Burmester Speakers bureau: AbbVie, Amgen, BMS, Chugai, Galapagos, Lilly, Pfizer, Sanofi, Consultant of: AbbVie, Amgen, BMS, Galapagos, Lilly, Pfizer, Sanofi.
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Information Technology (IT) has become the integral part of majority of businesses. Healthcare sector is also one such sector where IT adoption is increased in recent times. This adoption of IT has increased the internet exposure and hence increased the attack surface of the organisations working in healthcare sector. During covid outbreak, we have observed various cyber-attack and threats on organisations operating in healthcare sector. This paper focuses on cyber threat pattern in healthcare sector during covid-19 outbreak and post-covid-19 period. This research paper also aims to generate basic cyber awareness through generic cyber sanity checks to secure healthcare sector from malicious threat actors. The adaptation of proactive measures required to enhance the cyber hygiene of organisations becomes very essential in this sector. © 2023 IEEE.
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Cybersecurity is an increasingly important factor in consumer attitudes toward online shopping. Online shopping has become an essential part of our lives in this digital era. As the popularity of online and e-commerce shopping continues to grow, so does the potential for cyber threats and attacks. As more and more consumers turn to online shopping, cyber threats such as hacking, identity theft, and credit card fraud have become more frequent. Therefore, understanding the factors of cybersecurity that affect consumer attitude is essential to build trust and creating a safe and sound shopping environment. This research explores the factors of cybersecurity that affect consumers' attitudes to shopping online and uses a survey to test several hypotheses related to influential cyber factors. Bangladesh is a developing country in Southeast Asia, and like many other countries, has experienced an increase in cyber threats and attacks in recent years. Consumers in Bangladesh face many of the same cyber threats, such as phasing attacks, malware, data breach, and other types of cyber security threats over online shopping. As a result of these cyber threats, online consumers are increasingly concerned about online security risks which may impact their willingness to engage in online shopping. Therefore, it is essential to identify critical factors of cyber security that impact consumers's attitudes toward online shopping to mitigate cyber risk and improve consumer trust in online shopping. This paper provides the result of a research study that will provide a better understanding of factors that influence consumer's trust and engagement with online and E-commerce platforms in Bangladesh) . © 2023 IEEE.
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Objectives: This case intended to explore the effectiveness of cognitive behavioral approach via electronic means in treating symptoms associated with Panic Disorder and pre-occupation with health-related concerns. Design of the study: It's a single-case study design for an in-depth understanding of client and the disorder dynamics. Place and Duration of the study: The case study was done via electronic means during Covid-19, from June 2020 to October 2020 in Lahore, Pakistan. Sample and Method: Case study was done on a 24 years old female having Panic Disorder, along with sub-threshold features of Illness Anxiety Disorder. Psycho-diagnostic interview (DSM-V Criteria), CBT assessment form, Panic Disorder Checklist, HFD and TAT used in the initial phase for case conceptualization. Further, techniques from the Cognitive Behavioral Therapy were used for the client's catastrophic thoughts and cognitive distortions to alter her overestimation of threat in reducing her panic attacks. Cognitive Behavior Therapy techniques such as thought reconstruction, grounding techniques along with mindful relaxation techniques also helped the client gain control over her anxious thinking process and pre-occupation with health. Results and Conclusion: CBT techniques helped improve the client's overall functioning, panic attacks were eliminated and preoccupation with health was reduced. The results and client's recovery established that Cognitive Behavior Therapy via online means is an effective approach to treat Panic Disorder and negative thinking process.
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In this paper, we present the technique for investigating attacks on a company's reputation on a social media platform as a part of an arsenal of digital forensics investigators. The technique consists of several methods, including (1) identifying the attack based on sentiment analysis, (2) identifying the actors of the attack, (3) determining the attack's impact, and (4) determining core actors to identify the strategy of the attacker, including (4a) usage of bots, (4b) attempts to conflict initiation, (4c) competitor promotion, (4d) uncoordinated user attack. In the paper we also present the evaluation of this technique using the real investigation of use-case, where we investigate the attack on a retail company X, that occurs after the company changed its policy dedicated to COVID-19 QR codes for their visitors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Mervyn George Bishop/Fairfax Media/Getty Images Peter Sleight, a professor at Oxford University, helped to transform heart attack treatment and prevent cardiovascular disease with angiotensin converting enzyme inhibitors and statins. Isis methodology influenced the design of studies into other conditions, including the Recovery trial, which showed that dexamethasone reduces covid-19 mortality. [...]unlike many eminent men, he was able, endearingly, to laugh at himself—for example, when medical students lampooned him as Professor BA Flight after he had flown to Tokyo for the day. In the last 10 years of his life, he generated global media interest after demonstrating with his Italian colleague Luciano Bernardi that certain musical rhythms lowered blood pressure.
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The COVID-19 pandemic has increased demand for face mask detection systems that utilize deep learning and machine learning algorithms. However, these systems are susceptible to adversarial attacks, where an attacker can manipulate the system to make incorrect predictions. This study aimed to test the vulnerability of a deep learning-based face mask detection model to a specific type of attack called a black box adversarial attack in which the attacker possesses only partial information about the target model. The study's findings showed that the attack successfully reduced the model's accuracy from 96.48% to 49.25%. This emphasizes the need for more robust defense mechanisms in face mask detection systems to ensure their reliability. © 2023 Bharati Vidyapeeth, New Delhi.
ABSTRACT
BackgroundSystemic sclerosis (SSc) is a severe, progressive multisystem rheumatic disease with high mortality, but without approved disease-modifying treatment to stop or reverse course of disease. Intravenous immunoglobulin G (IgG) may have a positive impact on SSc based upon available literature reports. However, to date, there have been no clinical trials evaluating subcutaneous IgG (SCIG) in SSc. In particular, the impact of pathologically altered skin in SSc on local safety and pharmacokinetics (PK) of SCIG has not been explored yet.ObjectivesThe primary and secondary objectives of this trial (NCT04137224) included safety, including local infusion safety, and bioavailability of subcutaneous IgG (IgPro20) in adults with diffuse cutaneous SSc (dcSSc).MethodsThis was a randomized, open-label, crossover study. Adult subjects with dcSSc diagnosis within 5 years from first non-Raynaud's phenomenon and modified Rodnan Skin Score of 15-45 at screening were randomized 1:1 to sequence A (IgPro20, 20% normal human subcutaneous immunoglobulin followed by IgPro10, 10% normal human intravenous immunoglobulin) or sequence B (IgPro10 followed by IgPro20). Each subject was to complete two treatment periods (16 weeks each), with up to 40 weeks (including screening) study duration for an individual subject. Doses received were 0.5g/kg/week split over two sessions for IgPro20, and 2g/kg/4 weeks split over 2-5 days for IgPro10. The primary endpoint was safety of IgPro20, described as treatment-emergent adverse events (TEAEs) and changes in clinical observations.Results27 subjects were randomized, with 13 subjects to sequence A and 14 subjects to sequence B. In total, 25 subjects completed the study. Of 27 treated subjects, 107 TEAEs occurred in 22 subjects (81.5%) over the 36-week study period, the majority of which were mild or moderate. The most common TEAEs (>10% of subjects) by preferred term (PT) were headache (12 events occurring in 6 subjects [22.2%]), COVID-19 (3 events occurring in 3 subjects [11.1%]), diarrhoea (3 events occurring in 3 subjects [11.1%]), and vomiting (3 events occurring in 3 subjects [11.1%]).A total of 10 serious AEs (SAEs) were reported in 6 subjects (Viral infection, Chronic gastritis, Vomiting, Dehydration, Upper gastrointestinal haemorrhage, Chest pain, Myocardial infarction, Myocardial ischemia, Breast cancer, Interstitial lung disease). Among these, one subject experienced 2 SAEs (myocardial ischemia & myocardial infarction) and was discontinued from study treatment. None of the SAEs were considered related to study treatment by the investigator, and no deaths were reported.For IgPro20, 14 infusion site reactions (ISRs) occurred in 5 subjects (19.2%), all were mild or moderate in severity. The most common ISRs were infusion site pain and infusion site swelling (3 events in 2 subjects each, 7.7%). In total, 686 IgPro20 infusions were performed, resulting in an overall ISR rate per infusion of 0.02, ie 2 ISRs per 100 infusions. No ISRs were reported for IgPro10.No clinically relevant trends in vital signs, body weight, clinical laboratory tests, electrocardiograms, or pulmonary function tests were observed.PK profiles and bioavailability in dcSSc subjects were similar to those observed in other approved indications such as Primary Immunodeficiency. Population relative bioavailability of IgPro20, based on dose-normalized, baseline-corrected AUC0-tau was 0.761 (90% CI: 0.7033, 0.8232), ie 76.1% compared to IgPro10 (intravenous IgG).ConclusionThe overall safety profiles of IgPro20 and IgPro10 in subjects with dcSSc were consistent with that in approved indications such as CIDP, including a relatively low ISR rate for IgPro20. PK profiles and bioavailability were also similar to other indications. This study indicates that subcutaneous administration of IgPro20 has acceptable safety, bioavailability and PK profiles in patients with dcSSc. AcknowledgementsEditorial assistance was provided by Meridian HealthComms Ltd., funded by CSL Behring.Disclosure of InterestsChristopher P Denton Speakers bureau: Ja ssen, Boehringer Ingelheim, Consultant of: GSK, CSL Behring, Boehringer Ingelheim, Merck, Roche, Sanofi, Grant/research support from: GSK, CSL Behring, Inventiva, Horizon, Otylia Kowal-Bielecka Speakers bureau: Abbvie, Janssen-Cilag, Boehringer Ingelheim, Medac, MSD, Novartis, Pfizer, Sandoz, Consultant of: Boehringer Ingelheim and Novartis, Grant/research support from: Received congress support from Abbvie, Boehringer Ingelheim, and Medac, Susanna Proudman Speakers bureau: Boehringer Ingelheim, Grant/research support from: Janssen, Marzena Olesińska Consultant of: AstraZeneca, Margitta Worm Consultant of: Novartis Pharma GmbH, Sanofi-Aventis Deutschland GmbH, DBV Technologies S.A, Aimmune Therapeutics UK Limited, Regeneron Pharmaceuticals, Inc, Leo Pharma GmbH, Boehringer Ingelheim Pharma GmbH &Co.KG, ALK-Abelló Arzneimittel GmbH, Kymab Limited, Amgen GmbH, Abbvie Deutschland GmbH & Co. KG, Pfizer Pharma GmbH, Mylan Germany GmbH (A Viatris Company), AstraZeneca GmbH, Lilly Deutschland GmbH and GlaxoSmithKline GmbH & Co. KG., Nicoletta Del Papa Speakers bureau: Janssen Cilag, Boehringer Ingelheim., Marco Matucci-Cerinic Speakers bureau: Biogen, Sandoz, Boehringer Ingelheim, Consultant of: CSL Behring, Boehringer Ingelheim, Grant/research support from: MSD, Chemomab, Jana Radewonuk Shareholder of: CSL Behring, Employee of: CSL Behring, Jeanine Jochems Shareholder of: CSL Behring, Employee of: CSL Behring, Amgad Shebl Shareholder of: CSL Behring, Employee of: CSL Behring, Anna Krupa Shareholder of: CSL Behring, Employee of: CSL Behring, Jutta Hofmann Shareholder of: CSL Behring, Employee of: CSL Behring, Maria Gasior Shareholder of: CSL Behring, Employee of: CSL Behring.
ABSTRACT
The Internet of Medical Things (IoMT) has been applied to provide health care facilities for elders and parents. Remote health care is essential for providing scarce resources and facilities to coronavirus patients. Ongoing IoMT communication is susceptible to potential security attacks. In this research, an artificial intelligence-driven security model of the IoMT is also proposed to simulate and analyses the results. Under the proposed plan, only authorized users will be able to access private and sensitive patient information, and unauthorized users will be unable to access a secure healthcare network. The various phases for implementing artificial intelligence (AI) techniques in the IoMT system have been discussed. AI-driven IoMT is implemented using decision trees, logistic regression, support vector machines (SVM), and k-nearest neighbours (KNN) techniques. The KNN learning models are recommended for IoMT applications due to their low consumption time with high accuracy and effective prediction. © 2023 IEEE.
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Body weight and fat content are known to influence the timing of puberty but the association remained after adjustment for pre-pubertal body mass index. [...]a large retrospective study from the US finds that patients who underwent plasma exchange spent a week longer in hospital and were two to three times more likely to die than those who received immunoglobulin (Muscle Nerve doi:10.1002/mus.26831). A case study in NEJM Catalyst identifies early testing (particularly of people arriving from places where the disease was prevalent), rapid mobilisation of microbiological laboratories, and a coherent and consistent national strategy as crucial interventions.
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The advancement of information technology has stimulated the conversion of physical interactions to online activities, especially during the Covid-19 pandemic. Thus, users' awareness and cyber hygiene need to be emphasized when they are involved in the cyber world. A browser extension named 'BEsafe' is developed to validate the websites and promote a safe browsing environment. It prevents users from falling prey to network-based attacks and raises their security awareness. To ensure users' privacy, the permissions needed for BEsafe are listed on the permission tab. Moreover, BEsafe will not be working on Incognito mode by default to promise that the private mode leaves no tracks. However, the user can still enable the extension to be functioning on Incognito mode by navigating to the Extension Details and turning on the relevant toggle. © 2023 IEEE.
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E-commerce sites are flourishing nowadays in lockdown. A lot of entrepreneurs are making their own sites and selling them online. In 2020, one of INTERPOL's private sector partners detected 907,000 spam messages, 737 malware incidents, and 48,000 malware URLs connected to COVID-19 during the period from January to April. 'Cybercriminals are developing and boosting their attacks at an alarming pace, exploiting the fear and uncertainty caused by the unstable social and economic situation created by COVID-19.' states Jürgen Stock, INTERPOL Secretary General. The main threats during this pandemic are Malware/Ransomware(36%), Phishing/Scam(59%), Fake News(14%) and Malicious Domains(22%). Cybercriminals are active in these pandemic times and the developers designing stunning user interfaces without basic cybersecurity knowledge is a great attraction for these criminals. Our goal is to explain how easily hackers gain access by selecting 10 top vulnerabilities from OWASP and exploiting them. © 2023 IEEE.
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Many researchers attribute factors of identity, acculturation, sexual orientation, and substance use and other maladaptive behaviors as potential risk factors associated with increased mental illnesses reported by college students (Castillo & Schwartz, 2013;Wyatt & Oswalt, 2013). [...]the COVID-19 pandemic has further exacerbated mental health problems in college students (Lopez Steinmetz et al., 2021;Son et al., 2020), with 71% experiencing more stress, anxiety, problems with concentration, and worry about a loved one's health as well as their own (Son et al., 2020). Approximately 75% of first-year college students with any mental illness do not seek help (Bruffaerts et al, 2019). [...]there is a growing need to address possible factors contributing to reduced help seeking and to identify vulnerable groups in college populations (Castillo & Schwartz, 2013). Men's decreased treatment utilization may be in part due to shame, stigma, and parental and peer norms toward seeking out mental health services (Seehuus et al, 2021). Given the impact that gender-related stressors play in the rise of mental illness in college students, this study aims to bridge the gap in mental health clinicians' and researchers' understanding of how diverse gender identities influence mental health-related outcomes and mental illness. [...]this study addresses two research questions: (1) Do gender differences (i.e., between cisgender men, cisgender women, and TGNC individuals) exist among college students in the prevalence of mental illness, utilization of psychological services, and mental health-related outcomes (i.e., psychological distress, stress, resilience, overall health, and loneliness)? (2) Do mental health-related variables differentially predict mental illness diagnoses among college students? METHOD Participants and Data Collection The ACHA (2020) National College Health Assessment-Ill Fall 2020 (NCHA-III) is a survey that gathers information regarding students' health.
ABSTRACT
The face mask detection system has been a valuable tool to combat COVID-19 by preventing its rapid transmission. This article demonstrated that the present deep learning-based face mask detection systems are vulnerable to adversarial attacks. We proposed a framework for a robust face mask detection system that is resistant to adversarial attacks. We first developed a face mask detection system by fine-tuning the MobileNetv2 model and training it on the custom-built dataset. The model performed exceptionally well, achieving 95.83% of accuracy on test data. Then, the model's performance is assessed using adversarial images calculated by the fast gradient sign method (FGSM). The FGSM attack reduced the model's classification accuracy from 95.83% to 14.53%, indicating that the adversarial attack on the proposed model severely damaged its performance. Finally, we illustrated that the proposed robust framework enhanced the model's resistance to adversarial attacks. Although there was a notable drop in the accuracy of the robust model on unseen clean data from 95.83% to 92.79%, the model performed exceptionally well, improving the accuracy from 14.53% to 92% on adversarial data. We expect our research to heighten awareness of adversarial attacks on COVID-19 monitoring systems and inspire others to protect healthcare systems from similar attacks.
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Violent incidents, terrorist attacks, senseless shootings, health issues such as the Coronavirus, and natural disasters call attention to managerial leadership in crisis situations. Yukl and Van Fleet (1982) did the seminal work on this topic extended by Peterson and Van Fleet (2008) and Peterson et al. (2012). More recently, Geier (2016) reported findings based on firefighters while Htway and Casteel (2015) and Kapucu and Ustun (2018) studied public sector organizations. Since these studies all involved nonprofit organizations, an extension to for-profit organizations is warranted. There are differences between profit organizations and not-for-profit organizations (Collins, 2001;Collins, 2005). Because of the goals involved, there may be differences in the managerial leadership behaviors required by these types of organizations. Hannah and Parry (2013) specifically recommend expanding leadership research to many different extreme situations in an effort to understand different managerial leadership behaviors that adapt to varying crisis situations. Two samples reported here identify the critical managerial leadership behaviors desired by for-profit organizational participants in both stable and crisis situations. Finally, implications, limitations, and future research are discussed.
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This paper examines the effect of security oversight on air cargo price and demand. We exploit variations in security oversight instituted by the International Civil Aviation Organization (ICAO). We estimate a simultaneous equation model using proprietary operations data from a major airline in South Korea over the period 2009–2013. This study explores the shipping-charge behavior of a service provider through a modeling approach that considers air cargo security. Our findings show that security oversight increases air cargo demand, controlling for the effect of price. Improving security measures increases the air cargo price, but the magnitude of this increase is small. Our results should help policymakers gauge the benefit of improved security and help airlines design an effective model to determine future air cargo shipping charges under high uncertainty to mitigate short- and long-term financial risks.
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In 2020, while the USA was experiencing suc-cessive waves of COVID-19, Universal Health Services experienced a major cyber attack that crippled electronic systems in over 200 hospitals, including a major academic medical centre that was playing a key regional role in COVID-19 care and clinical trials. This paper discusses the impact of the attack on clinical operations, infor-matics, research and teaching, contextualising the case study within more wide-scale trends driving the rise in cyber attacks on health-care systems. The compounding relationships between COVID-19, healthcare workforce depletion and cyber-security vulnerabilities form the framework of the discussion and action plan. Commitments to institutional best prac-tices, large-scale investments in infrastructure, and above all increasing support for the crit-ical human actors carrying out the work, are urgently needed to secure the healthcare system against these destabilising threats. Within this context, this paper argues that information security in the healthcare sector must be reimagined and integrated with greater support for the needs of frontline healthcare workers. © Henry Stewart Publications, 1749–9216.
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Temporal point processes have many applications, from crime forecasting to modeling earthquake aftershocks sequences. Due to the flexibility and expressiveness of deep learning, neural network-based approaches have recently shown promise for modeling point process intensities. However, there is a lack of research on the robustness of such models in regards to adversarial attacks and natural shocks to systems. Precisely, while neural point processes may outperform simpler parametric models on in-sample tests, how these models perform when encountering adversarial examples or sharp non-stationary trends remains unknown. Current work proposes several white-box and blackbox adversarial attacks against temporal point processes modeled by deep neural networks. Extensive experiments confirm that predictive performance and parametric modeling of neural point processes are vulnerable to adversarial attacks. Additionally, we evaluate the vulnerability and performance of these models in the presence of non-stationary abrupt changes, using the crimes dataset, during the Covid-19 pandemic, as an example. © 2022 IEEE.
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During COVID-19 pandemic, there has been unprecedented increase in the number of employees working outside an organisations IT infrastructure due to the use of personal devices. The scale and sophistication of cyberattacks also continue to increase post-COVID-19 and it has become critical for SMEs (Small and Medium Sized Enterprises) to safeguard their information and IT assets. COVID19 proved to be a major catalyst for the adoption of digital approaches to remote working that many organisations did not previously believe to be feasible. The systems are becoming increasingly exposed to cyber-attacks as a result of remote access technology and cloud networks. The literature points to a gap in the existing knowledge to address the cybersecurity requirements for SMEs in India working in a virtual setup. The purpose of this paper is to develop a cybersecurity evaluation model (CSEM) that can be leveraged by SMEs which will eventually help them assess their cyber-risk portfolio. Based on the research project and the methodology used in the past for similar research, a quantitative approach will be chosen for this research. This research requires the researcher to roll out an online survey, which will enable the participants to evaluate cybersecurity risks by responding to the survey questionnaire. Analysing and implementing a CSEM will not only assist SMEs in identifying their strengths and weaknesses but will also include simple best practice guidelines for effectively plugging their cybersecurity flaws while working remotely. © 2022 IEEE.
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Wireless sensor networks (WSN) playa significant role in the collection and transmission of data. The principal data collectors and broadcasters are small wireless sensor nodes. As a result of their disorganized layout, the nodes in this network are vulnerable to intrusion. Every aspect of human life includes some form of technological interaction. While the Covid-19 pandemic has been ongoing, the whole corporate and academic world has gone digital. As a direct result of digitization, there has been a rise in the frequency with which Internet-based systems are attacked and breached. The Distributed Denial of Service (DDoS) and Distributed Reflective Denial of Service (DRDoS) assaults are new and dangerous type of cyberattacks that can quickly bring down any service or application that relies on the Internet's infrastructure. Cybercriminals are always refining their methods of attack and evading detection by using techniques that are out of date. Traditional detection systems are not suited to identify novel DDoS attacks since the volume of data created and stored has expanded exponentially in recent years. This research provides a comprehensive overview of the relevant literature, focusing on deep learning for DDoS and DRDoS detection. Due to the expanding number of loT gadgets, distributed DDoS and DRDoS attacks are becoming more likely and more damaging. Due to their lack of generalizability, current attack detection methods cannot be used for early detection of DDoS and DRDoS, resulting in significant load or service degradation when implemented at the endpoint. In this research, a brief review is performed on the models that are used for identification of DDoS and DRDoS attacks. The working of the existing models and the limitations of the models are briefly analyzed in this research. © 2023 IEEE.