A cognitive based Intrusion detection system

Intrusion detection is one of the primary mechanisms to provide computer networks with security. With an increase in attacks and growing dependence on various fields such as medicine, commercial, and engineering to give services over a network, securing networks have become a significant issue. The purpose of Intrusion Detection Systems (IDS) is to make models which can recognize regular communications from abnormal ones and take necessary actions. Among different methods in this field, Artificial Neural Networks (ANNs) have been widely used. However, ANN-based IDS, has two main disadvantages: 1- Low detection precision. 2- Weak detection stability. To overcome these issues, this paper proposes a new approach based on Deep Neural Network (DNN. The general mechanism of our model is as follows: first, some of the data in dataset is properly ranked, afterwards, dataset is normalized with Min-Max normalizer to fit in the limited domain. Then dimensionality reduction is applied to decrease the amount of both useless dimensions and computational cost. After the preprocessing part, Mean-Shift clustering algorithm is the used to create different subsets and reduce the complexity of dataset. Based on each subset, two models are trained by Support Vector Machine (SVM) and deep learning method. Between two models for each subset, the model with a higher accuracy is chosen. This idea is inspired from philosophy of divide and conquer. Hence, the DNN can learn each subset quickly and robustly. Finally, to reduce the error from the previous step, an ANN model is trained to gain and use the results in order to be able to predict the attacks. We can reach to 95.4 percent of accuracy. Possessing a simple structure and less number of tunable parameters, the proposed model still has a grand generalization with a high level of accuracy in compared to other methods such as SVM, Bayes network, and STL.Comment: 18 pages, 6 figure

Investigating the Effects of Greenhouse Gases Emission on Supply and Demand of Irrigation Water in Watersheds of Qazvin Province

Increasing the emissions of greenhouse gases is among the factors affected the speed of occurrence of climate change during recent decades. In present study, first using time series data of 2006-2012 andRCM-PRECIS simulation model, the impacts of greenhouse gases emission on climatic variables of temperature and precipitation was investigated under different scenarios in watersheds of QazvinProvince. Then, the ordinary least squares (OLS) method and regression analysis were used to assess theimpacts of climatic variables of temperature and precipitation on the selected products yield. Afterwards,considering the results of regression analysis in positive mathematical programing (PMP) model, theamount of the created variation in supply and demand of irrigation water and agricultural output inwatersheds of Qazvin Province was investigated. The results showed that emission of greenhouse gasesunder scenarios A, B, and C affects the climatic variables of temperature and precipitation about 0.43 to1.27 °C and -14.1 to 1.31 mm respectively. This case changes the selected products yield in the surface of each river basin of Qazvin Province. Change in yield affects acreage of agricultural crops by about -10.51to 3.17 percent, the amount of irrigation water supply by about -10.4 to 1.64 percent, and the amount ofirrigation water demand by about 1.60 to 7.35 percent. Moreover, the results showed that maximum andminimum decrease in the gap between supply and demand of irrigation water happens in Kharroud andShahroud watersheds by about 9.20 and 1.82 percent respectively. With estimating the gap betweendemand and supply of irrigation water, one can adopt the appropriate decisions for sustainable waterresources in watersheds of Qazvin Province

A multiple criteria decision making technique for supplier selection and inventory management strategy: A case of multi-product and multi-supplier problem

Selection of an appropriate supplier along with planning a good inventory system has become an area of open research for the past few years. In this paper, we present a multi objective decision making supplier and inventory management model where two objectives including the quality and offering price of supplier are minimized, simultaneously. The proposed model is formulated as mixed integer programming and it is converted into an ordinary single objective function using Lp-Norm. In order to find efficient solution, we use NSGA-II as meta-heuristic technique and the performance of the proposed model is examined using some instances. The preliminary results indicate that both Lp-Norm and NSGA-II methods can be used to handle problems in various sizes

Anomaly Detection in Intrusion Detection Systems

Intrusion detection systems (IDS) play a critical role in network security by monitoring systems and network traffic to detect anomalies and attacks. This study explores the different types of IDS, including host-based and network-based, along with their deployment scenarios. A key focus is on incorporating anomaly detection techniques within IDS to identify novel and unknown threats that evade signature-based methods. Statistical approaches like outlier detection and machine learning techniques like neural networks are discussed for building effective anomaly detection models. Data collection and preprocessing techniques, including feature engineering, are examined. Both unsupervised techniques like clustering and density estimation and supervised methods like classification are covered. Evaluation datasets and performance metrics for assessing anomaly detection models are highlighted. Challenges like curse of dimensionality and concept drift are outlined. Emerging trends include integrating deep learning and explainable AI into anomaly detection. Overall, this comprehensive study examines the role of anomaly detection within IDS, delves into various techniques and algorithms, surveys evaluation practices, discusses limitations and challenges, and provides insights into future research directions to advance network security through improved anomaly detection capabilities

The effect of in-service training on the dimensions of empowerment and Professional development of primary teachers according to paragraph 3 of the general policies "transformation in the basic education system"

Teachers are known as one of the most important variables in need of change in order to improve educational systems. The goal of this research; The effect of in-service training on the dimensions of empowerment and development of primary teachers according to paragraph 3 of the general policies "transformation in the basic education system". The research was conducted in terms of purpose, application, and semi-experimental intervention method (pre-test-post-test design with control group). Statistical Society; All the teachers of primary schools in Qazvin city (200 people) in 2021-2022, 30 people were selected by available sampling method. They were randomly placed in the experimental and control groups. research tools; The professional development questionnaire was Nova (2008) and Spritz empowerment (1995). Descriptive statistics (frequency distribution tables, standard deviation) and inferential statistics (Kolomgorov Smirnov test, Levin test, t test and multivariate analysis of variance) were used for data analysis.The results showed that in-service training with a coefficient of 0.84 and 0.81 on the empowerment and professional development of teachers, as well as on the sense of autonomy (0.32), sense of significance (0.38), sense of competence (53 0.0), feeling effective (effect ratio 0.42), feeling confident (0.43), meaningfulness (0.38), planning skills (0.64), teacher evaluation (45 0.0), classroom management (0.48), teaching method (0.44) of primary school teachers in Qazvin city has an effect. According to the results, it is necessary for the authorities to pay special attention to the two factors of professional development and teacher empowerment and give a special place to in-service training

Beyond the Public Mempool: Catching DeFi Attacks Before They Happen with Real-Time Smart Contract Analysis

peer reviewedBeyond the Public Mempool: Catching DeFi Attacks Before They Happen with Real-Time Smart Contract Analysis The rise of decentralized finance has brought a vast range of opportunities to the blockchain space and many risks. This paper tackles the challenge of detecting malicious smart contracts on Ethereum designed to exploit vulnerabilities and cause financial losses. We present a novel approach for preemptively identifying malicious smart contracts during their deployment stage. For this purpose, we gathered a dataset comprising 161 malicious smart contracts and 5500 benign smart contracts. By introducing and extracting various features related to the deployer, transaction characteristics, and deployment bytecode and selecting the most impactful features, we developed multiple models using different machine learning (ML) classification algorithms, compared them using the set of most impactful features, and selected the most accurate one as our detection model. We compared the model's performance with a publicly available ML malicious smart contract detection tool to benchmark it. The results demonstrate that our model achieves a superior True Positive Rate while having a lower False Positive Rate. Our model achieved a 79.17% detection rate for malicious smart contracts while maintaining a False Positive rate of less than 1.8%. Our model provides swift detection capabilities by alerting users immediately after a contract's deployment, thus enabling timely response and risk mitigation.9. Industry, innovation and infrastructur

Timely Identification of Victim Addresses in DeFi Attacks

peer reviewedOver the past years, Decentralized Finance (DeFi) protocols have suffered from several attacks. As a result, multiple solutions have been proposed to prevent such attacks. Most solutions rely on identifying malicious transactions before they are included in blocks. However, with the emergence of private pools, attackers can now conceal their exploit transactions from attack detection. This poses a significant challenge for existing security tools, which primarily rely on monitoring transactions in public mempools. To effectively address this challenge, it is crucial to develop proactive methods that predict malicious behavior before the actual attack transactions occur. In this work, we introduce a novel methodology to infer potential victims by analyzing the deployment bytecode of malicious smart contracts. Our idea leverages the fact that attackers typically split their attacks into two stages, a deployment stage, and an attack stage. This provides a small window to analyze the attacker's deployment code and identify victims in a timely manner before the actual attack occurs. By analyzing a set of past DeFi attacks, this work demonstrates that the victim of an attack transaction can be identified with an accuracy of almost 70%.9. Industry, innovation and infrastructur

Structural characteristics and contractual terms of specialist palliative homecare in Germany

Background Multi-professional specialist palliative homecare (SPHC) teams care for palliative patients with complex symptoms. In Germany, the SPHC directive regulates care provision, but model contracts for each federal state are heterogeneous regarding staff requirements, cooperation with other healthcare providers, and financial reimbursement. The structural characteristics of SPHC teams also vary. Aim We provide a structured overview of the existing model contracts, as well as a nationwide assessment of SPHC teams and their structural characteristics. Furthermore, we explore whether these characteristics serve to find specifc patterns of SPHC team models, based on empirical data. Methods This study is part of the multi-methods research project “SAVOIR”, funded by the German Innovations Fund. Most model contracts are publicly available. Structural characteristics (e.g. number, professions, and affiliations of team members, and external cooperation) were assessed via an online database (“Wegweiser Hospiz- und Palliativversorgung”) based on voluntary information obtained from SPHC teams. All the data were updated by phone during the assessment process. Data were descriptively analysed regarding staff, cooperation requirements, and reimbursement schemes, while latent class analysis (LCA) was used to identify structural team models. Results Model contracts have heterogeneous contract partners and terms related to staff requirements (number and qualifications) and cooperation with other services. Fourteen reimbursement schemes were available, all combining different payment models. Of the 283 SPHC teams, 196 provided structural characteristics. Teams reported between one and 298 members (mean: 30.3, median: 18), mainly nurses and physicians, while 37.8% had a psychosocial professional as a team member. Most teams were composed of nurses and physicians employed in different settings; for example, staff was employed by the team, in private practices/nursing services, or in hospitals. Latent class analysis identified four structural team models, based on the team size, team members’ affiliation, and care organisation. Conclusion Both the contractual terms and teams’ structural characteristics vary substantially, and this must be considered when analysing patient data from SPHC. The identified patterns of team models can form a starting point from which to analyse different forms of care provision and their impact on care quality

Survey of the Income Distribution and its Impact on the Social Welfare of Rural Households (Case Study Alamout Region)

هدف اصلی این تحقیق بررسی وضعیت توزیع درآمد و رفاه اجتماعی خانوارهای روستایی منطقه الموت استان قزوین است. این تحقیق از نوع کاربردی با رویکرد توصیفی- تحلیلی بوده و جامعه آماری در آن شامل کلیه خانوارهای روستایی منطقه الموت است. داده‌های موردنیاز مربوط به سال 92-1391 بوده که با تکمیل پرسشنامه‌ از 328 خانوار نمونه جمع‌آوری شد. خانوارهای نمونه با استفاده از روش نمونه‌گیری تصادفی طبقه‌بندی‌شده و فرمول کوکران انتخاب شدند. برای تعیین روایی و اعتبار پرسشنامه‌های تنظیمی از روش آلفای کرونباخ، برای اندازه‌گیری نابرابری توزیع درآمد از ضریب جینی، برای محاسبه رفاه اجتماعی از شاخص سن و برای تعیین معنی‌داری نابرابری توزیع درآمد از آزمون کروسکال- والیس استفاده شد. تحلیل‌ آماری در محیط نرم‌افزاری SPSS صورت گرفت. مقدار ضریب جینی برای مناطق رودبارالموت شرقی، رجایی‌دشت و رودبارالموت غربی به ترتیب 52/0، 44/0 و 39/0 برآورد شد. نتایج نشان داد می‌دهد که توزیع درآمد خانوارهای روستایی در منطقه رودبار الموت شرقی نسبت به دیگر مناطق ناپایدارتر است. نتایج آزمون کروسکال-والیس نیز نابرابری توزیع درآمد را در منطقه الموت نشان می‌دهد. افزون بر این، نتایج حاکی از آن است که سهم درآمد حاصل از فعالیت‌های باغداری مهم‌ترین عامل نابرابری توزیع درآمد در منطقه الموت است. نتایج حاصل از شاخص سن نیز نشان داد که خانوارهای روستایی منطقه رودبار الموت غربی از بیشترین سطح رفاه اجتماعی برخوردار هستند. با توجه به نتایج به‌دست آمده، برای توزیع پایدار درآمد و انتقال آن از بخش باغداری به سایر بخش‌ها، سرمایه‌گذاری کافی در امر آموزش، هماهنگی فعالیت‌های بازتوزیعی دستگاه‌های اجرایی، فراهم نمودن زمینه برای جذب گردشگر، ارائه تسهیلات به نسل جوان برای جذب در بخش زراعی و ایجاد شغل دوم در صنعت و خدمات برای کشاورزان منطقه الموت پیشنهاد می‌شود

Simulation of Farmers’ Response to Irrigation Water Pricing and Rationing Policies (Case Study: Zabol City)

Considering that agricultural sector is the largest consumer of water, presenting integrated management for water resources and formulating effective policies to increase water productivity in this sector is essential. Therefore, using economic modeling , this study simulated the farmers’ responses to irrigation water pricing and rationing policies in Zabol city. To achieve the study purpose, the State Wide Agricultural Production Model and Positive Mathematical Programming were applied. The required data for the years 2010-2011 was collected by completing questionnaires and collecting data sets from the relevant agencies of Zabol city in personal attendance. The results showed that imposing irrigation water pricing and rationing policies in Zabol city leads to a reduction in the total cultivated area by 9/54 and 5/14 percent and a reduction in the water consumption by 6/23 and 7/01 percent, compared to the base year. Ultimately, irrigation water rationing policy, considering frugality of 18/9 million m3 of water, as the appropriate solution for the sustainability of water resources of Zabol city was proposed