Privacy Preserving Threat Hunting in Smart Home Environments

The recent proliferation of smart home environments offers new and transformative circumstances for various domains with a commitment to enhancing the quality of life and experience. Most of these environments combine different gadgets offered by multiple stakeholders in a dynamic and decentralized manner, which in turn presents new challenges from the perspective of digital investigation. In addition, a plentiful amount of data records got generated because of the day to day interactions between these gadgets and homeowners, which poses difficulty in managing and analyzing such data. The analysts should endorse new digital investigation approaches to tackle the current limitations in traditional approaches when used in these environments. The digital evidence in such environments can be found inside the records of logfiles that store the historical events occurred inside the smart home. Threat hunting can leverage the collective nature of these gadgets to gain deeper insights into the best way for responding to new threats, which in turn can be valuable in reducing the impact of breaches. Nevertheless, this approach depends mainly on the readiness of smart homeowners to share their own personal usage logs that have been extracted from their smart home environments. However, they might disincline to employ such service due to the sensitive nature of the information logged by their personal gateways. In this paper, we presented an approach to enable smart homeowners to share their usage logs in a privacy preserving manner. A distributed threat hunting approach has been developed to permit the composition of diverse threat classes without revealing the logged records to other involved parties. Furthermore, a scenario was proposed to depict a proactive threat Intelligence sharing for the detection of potential threats in smart home environments with some experimental results.Comment: In Proc. the International Conference on Advances in Cyber Security, Penang, Malaysia, July 201

Excellent outcomes of laparoscopic esophagomyotomy for achalasia in patients older than 60 years of age

The effectiveness of an esophagomyotomy for dysphagia in elderly patients with achalasia has been questioned. This study was designed to provide an answer. A total of 162 consecutive patients with achalasia who had a laparoscopic myotomy and Dor fundoplication and who were available for follow-up interview were divided by age: <60 years (range, 14–59; 118 patients), and ≥60 years (range, 60–93; 44 patients). Primary outcome measures were severity of dysphagia, regurgitation, heartburn, and chest pain before and after the operation as assessed on a four-point Likert scale, and the need for postoperative dilatation or revisional surgery. Follow-up averaged 64 months. Older patients had less dysphagia (mean score 3.6 vs. 3.9; P < 0.01) and less chest pain (1.0 vs. 1.8; P < 0.01). Regurgitation (3.0 vs. 3.2; P = not significant (NS)) and heartburn (1.6 vs. 2.0, P = NS) were similar. Older patients were no different in degree of esophageal dilation, manometric findings, number of previous pneumatic dilatations, or previous botulinum toxin therapy. None of the older patients had previously had an esophagomyotomy, whereas 14% of younger patients had (P < 0.01). After laparoscopic myotomy, older patients had better relief of dysphagia (mean score 1.0 vs 1.6; P < 0.01), less heartburn (0.8 vs. 1.1; P = 0.03), and less chest pain (0.2 vs. 0.8, P < 0.01). Complication rates were similar. Older patients did not require more postoperative dilatations (22 patients vs. 10 patients; P = 0.7) or revisional surgery for recurrent or persistent symptoms (3 vs. 1 patients; P = 0.6). Satisfaction scores did not differ, and more than 90% of patients in both groups said in retrospect they would have undergone the procedure if they had known beforehand how it would turn out. This retrospective review with long follow-up supports laparoscopic esophagomyotomy as first-line therapy in older patients with achalasia. They appeared to benefit even more than younger patients

Long-term safety and outcome of a temporary self-expanding metallic stent for achalasia: a prospective study with a 13-year single-center experience

To prospectively evaluate the long-term clinical safety and efficacy of a newly designed self-expanding metallic stent (SEMS) in the treatment of patients with achalasia. Seventy-five patients with achalasia were treated with a temporary SEMS with a 30-mm diameter. The SEMSs were placed under fluoroscopic guidance and removed by gastroscopy 4–5 days after stent placement. Follow-up data focused on dysphagia score, technique and clinical success, clinical remissions and failures, and complications and was performed at 6 months, 1 year, and within 3 to 5 years, 5 to 8 years, 8 to 10 years, and >10 years postoperatively. Stent placement was technically successful in all patients. Complications included stent migration (n = 4, 5.33%), chest pain (n = 28, 38.7%), reflux (n = 15, 20%), and bleeding (n = 9, 12%). No perforation or 30-day mortality occurred. Clinical success was achieved in all patients 1 month after stent removal. The overall remission rates at 6 months, 1, 1–3, 3–5, 5–8, 8–10, and >10 year follow-up periods were 100%, 96%, 93.9%, 90.9%, 100%, 100%, and 83.3%, respectively. Stent treatment failed in six patients, and the overall remission rate in our series was 92%. The median and mean primary patencies were 2.8 ± 0.28 years (95% CI: 2.25–3.35) and 4.28 ± 0.40 years (95% CI: 3.51–5.05), respectively. The use of temporary SEMSs with 30-mm diameter proved to be a safe and effective approach for managing achalasia with a long-term satisfactory clinical remission rate

Using Machine Learning for NEETs and Sustainability Studies: Determining Best Machine Learning Algorithms

In this study, we apply and compare different algorithms from machine learning to describe and predict NEET rates in 31 European countries in the period from 2005 to 2020. With this aim, we considered eleven indicators describing the socio-economic national context and the level of innovation of the economies. Besides improving knowledge about the use of machine learning algorithms for the description of the NEET phenomenon, we discuss the connections between NEETs and other indicators that connect with other relevant sustainable development goals (SDGs), such as education, the reduction of inequalities, and decent work for everyone. The reduction of NEET rates is the only goal directly addressed to young people, The article underscores the need for evidence-based approaches to measure SDG achievement, especially concerning the heterogeneous NEET population. It emphasizes the importance of machine learning algorithms as a modern methodology for understanding and addressing the NEET phenomenon within the framework of SDGs, considering the complex interrelationships of socio-economic factors contributing to social and economic sustainability

Passion For Saving Lives: Turkey Between 2012 And 2017

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