One of the major and serious threats on the Internet today is malicious software, often referred to as a malware. The malwares being designed by attackers are polymorphic and metamorphic which have the ability to change their code as they propagate. Moreover, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses which typically use signature based techniques and are unable to detect the previously unknown malicious executables. The variants of malware families share typical behavioral patterns reflecting their origin and purpose. The behavioral patterns obtained either statically or dynamically can be exploited to detect and classify unknown malwares into their known families using machine learning techniques. This survey paper provides an overview of techniques for analyzing and classifying the malwares.

No abstract

Background There is scarcity of data on outcome of COVID-19 in patients with hematological malignancies. Primary objective of study was to analyse the 14-day and 28-day mortality. Secondary objectives were to correlate age, comorbidities and remission status with outcome. Methods Retrospective multicentre observational study conducted in 11 centres across India. Total 130 patients with hematological malignancies and COVID-19 were enrolled. Results Fever and cough were commonest presentation. Eleven % patients were incidentally detected. Median age of our cohort was 49.5 years. Most of our patients had a lymphoid malignancy ( n = 91). One-half patients (52%) had mild infection, while moderate and severe infections contributed to one-fourth each. Sixty seven patients (52%) needed oxygen For treatment of COVID-19 infection, half( n = 66) received antivirals. Median time to RT-PCR COVID-19 negativity was 17 days (7–49 days). Nearly three-fourth ( n = 95) of our patients were on anticancer treatment at time of infection, of which nearly two-third ( n = 59;64%) had a delay in chemotherapy. Overall, 20% ( n = 26) patients succumbed. 14-day survival and 28-day survival for whole cohort was 85.4% and 80%, respectively. One patient succumbed outside the study period on day 39. Importantly, death rate at 1 month was 50% and 60% in relapse/refractory and severe disease cohorts, respectively. Elderly patients(age ≥ 60)( p = 0.009), and severe COVID-19 infection ( p = 0.000) had a poor 14-day survival. The 28-day survival was significantly better for patients in remission ( p = 0.04), non-severe infection (p = 0.00), and age < 60 years ( p = 0.05). Conclusions Elderly patients with hematological malignancy and severe covid-19 have worst outcomes specially when disease is not in remission.

To detect the presence of Helicobacter pylori in nasal polyps. A case-control study was conducted enrolling 35 patients with nasal polyps (cases) and patients undergoing septoplasty (controls). Fresh tissue samples were used for urea broth test and imprint cytology, while formalin fixed tissue sections were used for morphology, special stains and immunohistochemistry for H. pylori. Fresh stool samples from both groups were tested to correlate the gastrointestinal status. H. pylori was detected in 40.0 % (14/35) of cases and 8.5 % of controls (3/35) (p = 0.004) by immunohistochemistry. Amongst cases, eight were positive with urea broth test, six with imprint cytology (Giemsa stain), three with H & E, and nine with modified McMullen's stain. Hyperplasia of the lining epithelium and lymphoid aggregates were significantly noticed in nasal polyps positive for H. pylori. Stool antigen test was positive in subjects who were positive for H. pylori in the nasal mucosa. There appears to be an association between H. pylori and nasal polyps. Immunohistochemistry is more sensitive and specific method to detect H. pylori.H. pylori induced inflammatory tissue reaction pattern indicates a possible causal association. Further studies are needed to prove the causal relationship between H. pylori and nasal polyps.