No abstract

Computer viruses, malicious, and other hostile attacks can affect a computer network. Intrusion detection is a key component of network security as an active defence technology. Traditional intrusion detection systems struggle with issues like poor accuracy, ineffective detection, a high percentage of false positives, and an inability to handle new types of intrusions. To address these issues, we propose a deep learning-based novel method to detect cybersecurity vulnerabilities and breaches in cyber-physical systems. The proposed framework contrasts the unsupervised and deep learning-based discriminative approaches. This paper presents a generative adversarial network to detect cyber threats in IoT-driven IICs networks. The results demonstrate a performance increase of approximately 95% to 97% in terms of accuracy, reliability, and efficiency in detecting all types of attacks with a dropout value of 0.2 and an epoch value of 25. The output of well-known state-of-the-art DL classifiers achieved the highest true rate (TNR) and highest detection rate (HDR) when detecting the following attacks: (BruteForceXXS, BruteForceWEB, DoS_Hulk_Attack, and DOS_LOIC_HTTP_Attack) on the NSL-KDD, KDDCup99, and UNSW-NB15 datasets. It also maintained the confidentiality and integrity of users' and systems' sensitive information during the training and testing phases.

No abstract

Objectives: A conflict of evidence exists regarding the gender-based differences in outcomes after primary percutaneous coronary intervention (PCI), therefore, aim of this study was to compare the clinical characteristics, angiographic findings, and outcome of primary PCI for men and women. Methodology: Data for this study was extracted from a prospectively managed primary PCI database of the National Institute of Cardiovascular Diseases (NICVD), Karachi, Pakistan. We included consecutive patients of either gender with STEMI undergone primary PCI. Data on clinical characteristics, angiographic finding, and post procedure outcomes for female were compared with male group and also with a propensity matched male cohort. Results: A total of 2400 patients were included with 421(17.5%) women. The mean age for the men and women were 54.44±11.16 and 57.17±11.01 years respectively; p<0.001. Women had significantly high prevalence of hypertension (61.0% vs. 39.1%; p<0.001), diabetes (37.1% vs. 23.9%; p<0.001), and obesity (18.5% vs. 13.5%; p=0.008). The median symptom onset to hospital arrival time was 216 [366-124] minutes vs. 180 [310-112] minutes; p=0.001 for women and men. In-hospital mortality rate was 3.8% vs. 2.5%; p=0.147 for female and unmatched male cohort, while it was 3.6% vs. 3.8%; p=0.855 for female and propensity matched male cohort. Conclusion: Gender-based differences persist in clinical profile of the patients with STEMI. Women are likely to be older in age with more diabetes, hypertension, and obesity. Gender-based difference in outcome of primary PCI is appears to be driven by differences in clinical profile as adjusted outcome is not different for men and women.

No abstract