Computer security incident response teams: are they legally regulated? The Swiss example

Computer Security Incident Response Teams (CSIRTs) or Computer Emergency Response Teams (CERTs) are an integral part of incident handling capabilities and are increasingly demanded by organizations such as critical infrastructures. They can hold many different skills and are of great interest to organizations in terms of cyber security and, more concretely, cyber incident management. This contribution seeks to analyze the extent to which their activity is regulated under Swiss law, considering that private CSIRTs are not regulated in the same way as governmental and national CSIRTs such as the Computer Emergency Response Team of the Swiss government and official national CERT of Switzerland (GovCERT)

KONTRIBUSI USAHATANI KELAPA TERHADAP PENDAPATAN KELUARGA DI DESA KLABAT KECAMATAN DIMEMBE KABUPATEN MINAHASA UTARA

This study aims to find out: 1) The amount of average income of coconut farmers per quarter, 2) The amount of contribution of coconut farming to family income per quarter. Data collection in this study was conducted from October to November 2018 in Klabat Village, Dimembe District, North Minahasa Regency. The method used is the survey method, using primary data and secondary data. Primary data was obtained through direct interviews with 25 coconut farmers and one person from the Klabat Village based on a list of questions that had been prepared previously. Secondary data in this study were sourced from local bookstores, and the internet through Google Scholar to access articles from various scientificjournals and theses from Sam Ratulangi University and other universities related to the contribution of coconut farming to family income. The data obtained were analyzed using contribution analysis and using descriptive analysis presented in table form. The results showed that the amount of income received by coconut farmers was Rp. 1,837,320. While the contribution of coconut farming to household income is 27.45%. This means that coconut farming provides a moderate contribution and cannot be used as the main source of household income in Klabat Village.*eprm

ANALISIS PENDAPATAN USAHATANI JAGUNG DI DESA PAKUWERU KECAMATAN TENGA KABUPATEN MINAHASA SELATAN

This study aims to analyze the income of corn farming in Pakuweru Village, Tenga District, South Minahasa Regency. The data used in this research are primary data and secondary data. Primary data were obtained from direct interviews with farmers using a questionnaire. The number of respondents was 9 corn farmers out of a total of 91 farmers. Respondents were selected using the Purposive Sampling Method, with the sample criteria being those who planted corn during the pandemic and the harvest was in August 2020. The sample farmers were divided into two categories according to the planted area. The first category is those who have a planting area of 2 hectares and below, while the second category is those who have a planting area of more than 2 hectares. The results showed that the average income per farmer in the first category, which has a planting area of 2 hectares and below, is Rp. 356,450, or Rp. 254,607/ha, while for the second category, namely farming with a planting area of more than 2 hectares, the average income per farmer is Rp. 17,699,835, with an average income per hectare of Rp. 3,371,398. So the average income per hectare of corn farming with a planting area of more than 2 hectares is much higher than that of a farm with a planting area of 2 hectares and below

Loi fédérale sur la sécurité de l’information : version 2.0

La Loi fédérale sur la sécurité de l’information (LSI) servira de cadre légal pour assurer des traitements (informatisés ou non) sécurisés de l’information, principalement au sein de l’administration fédérale. Elle servira également de base légale pour l’activité du Centre national pour la cybersécurité (NCSC) et prévoira une obligation de signaler les cyberattaques visant les infrastructures critiques. Nous allons examiner en quoi consiste exactement cette loi et, plus particulièrement, les conséquences de l’obligation de signaler les cyberattaques pour les organisations qui y seront soumises

La cybersécurité: entre autonomie et soutien étatique

Présentation et analyse d’une enquête sur la position d’experts sur l’implication des différentes parties prenantes en cybersécurité sous le prisme de la transformation du Centre national pour la cybersécurité (NCSC) en office fédéral

On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks.

Anomaly detection in todays industrial environments is an ambitious challenge to detect possible faults/problems which may turn into severe waste during production, defects, or systems components damage, at an early stage. Data-driven anomaly detection in multi-sensor networks rely on models which are extracted from multi-sensor measurements and which characterize the anomaly-free reference situation. Therefore, significant deviations to these models indicate potential anomalies. In this paper, we propose a new approach which is based on causal relation networks (CRNs) that represent the inner causes and effects between sensor channels (or sensor nodes) in form of partial sub-relations, and evaluate its functionality and performance on two distinct production phases within a micro-fluidic chip manufacturing scenario. The partial relations are modeled by non-linear (fuzzy) regression models for characterizing the (local) degree of influences of the single causes on the effects. An advanced analysis of the multi-variate residual signals, obtained from the partial relations in the CRNs, is conducted. It employs independent component analysis (ICA) to characterize hidden structures in the fused residuals through independent components (latent variables) as obtained through the demixing matrix. A significant change in the energy content of latent variables, detected through automated control limits, indicates an anomaly. Suppression of possible noise content in residuals—to decrease the likelihood of false alarms—is achieved by performing the residual analysis solely on the dominant parts of the demixing matrix. Our approach could detect anomalies in the process which caused bad quality chips (with the occurrence of malfunctions) with negligible delay based on the process data recorded by multiple sensors in two production phases: injection molding and bonding, which are independently carried out with completely different process parameter settings and on different machines (hence, can be seen as two distinct use cases). Our approach furthermore i.) produced lower false alarm rates than several related and well-known state-of-the-art methods for (unsupervised) anomaly detection, and ii.) also caused much lower parametrization efforts (in fact, none at all). Both aspects are essential for the useability of an anomaly detection approach

Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models.

In modern manufacturing facilities, there are basically two essential phases for assuring high production quality with low (or even zero) defects and waste in order to save costs for companies. The first phase concerns the early recognition of potentially arising problems in product quality, the second phase concerns proper reactions upon the recognition of such problems. In this paper, we address a holistic approach for handling both issues consecutively within a predictive maintenance framework at an on-line production system. Thereby, we address multi-stage functionality based on (i) data-driven forecast models for (measure-able) product quality criteria (QCs) at a latter stage, which are established and executed through process values (and their time series trends) recorded at an early stage of production (describing its progress), and (ii) process optimization cycles whose outputs are suggestions for proper reactions at an earlier stage in the case of forecasted downtrends or exceeds of allowed boundaries in product quality. The data-driven forecast models are established through a high-dimensional batch time-series modeling problem. In this, we employ a non-linear version of PLSR (partial least squares regression) by coupling PLS with generalized Takagi–Sugeno fuzzy systems (termed as PLS-fuzzy). The models are able to self-adapt over time based on recursive parameters adaptation and rule evolution functionalities. Two concepts for increased flexibility during model updates are proposed, (i) a dynamic outweighing strategy of older samples with an adaptive update of the forgetting factor (steering forgetting intensity) and (ii) an incremental update of the latent variable space spanned by the directions (loading vectors) achieved through PLS; the whole model update approach is termed as SAFM-IF (self-adaptive forecast models with increased flexibility). Process optimization is achieved through multi-objective optimization using evolutionary techniques, where the (trained and updated) forecast models serve as surrogate models to guide the optimization process to Pareto fronts (containing solution candidates) with high quality. A new influence analysis between process values and QCs is suggested based on the PLS-fuzzy forecast models in order to reduce the dimensionality of the optimization space and thus to guarantee high(er) quality of solutions within a reasonable amount of time (→ better usage in on-line mode). The methodologies have been comprehensively evaluated on real on-line process data from a (micro-fluidic) chip production system, where the early stage comprises the injection molding process and the latter stage the bonding process. The results show remarkable performance in terms of low prediction errors of the PLS-fuzzy forecast models (showing mostly lower errors than achieved by other model architectures) as well as in terms of Pareto fronts with individuals (solutions) whose fitness was close to the optimal values of three most important target QCs (being used for supervision): flatness, void events and RMSEs of the chips. Suggestions could thus be provided to experts/operators how to best change process values and associated machining parameters at the injection molding process in order to achieve significantly higher product quality for the final chips at the end of the bonding process

Macrophage Death as a Pharmacological Target in Atherosclerosis

Atherosclerosis is a chronic inflammatory disorder characterized by the gradual build-up of plaques within the vessel wall of middle-sized and large arteries. Over the past decades, treatment of atherosclerosis mainly focused on lowering lipid levels, which can be accomplished by the use of statins. However, some patients do not respond sufficiently to statin therapy and therefore still have a residual cardiovascular risk. This issue highlights the need for novel therapeutic strategies. As macrophages are implicated in all stages of atherosclerotic lesion development, they represent an important alternative drug target. A variety of anti-inflammatory strategies have recently emerged to treat or prevent atherosclerosis. Here, we review the canonical mechanisms of macrophage death and their impact on atherogenesis and plaque stability. Macrophage death is a prominent feature of advanced plaques and is a major contributor to necrotic core formation and plaque destabilization. Mechanisms of macrophage death in atherosclerosis include apoptosis, passive or accidental necrosis as well as secondary necrosis, a type of death that typically occurs when apoptotic cells are insufficiently cleared by neighboring cells via a phagocytic process termed efferocytosis. In addition, less-well characterized types of regulated necrosis in macrophages such as necroptosis, pyroptosis, ferroptosis, and parthanatos may occur in advanced plaques and are also discussed. Autophagy in plaque macrophages is an important survival pathway that protects against cell death, yet massive stimulation of autophagy promotes another type of death, usually referred to as autosis. Multiple lines of evidence indicate that a better insight into the different mechanisms of macrophage death, and how they mutually interact, will provide novel pharmacological strategies to resolve atherosclerosis and stabilize vulnerable, rupture-prone plaques