A Semi-Supervised Machine Learning Approach Using K-Means Algorithm to Prevent Burst Header Packet Flooding Attack in Optical Burst Switching Network

شبكة تبديل الاندفاع البصري (OBS) هي تقنية اتصال بصري من الجيل الجديد. في شبكة OBS ، ترسل عقدة الحافة أولاً حزمة تحكم ، تسمى حزمة رأس الاندفاع (BHP) التي تحتفظ بالموارد اللازمة لدفعة البيانات القادمة (DB). بمجرد اكتمال الحجز ، تبدأ قاعدة البيانات بالتحرك إلى وجهتها من خلال المسار المحجوز. هناك هجوم بارز على شبكة OBS هو هجوم فيضان BHP حيث ترسل عقدة الحافة BHPs لحجز الموارد ، ولكن في الواقع لا ترسل قاعدة البيانات المرتبطة بها. نتيجة لذلك ، يتم إهدار الموارد المحجوزة وعندما يحدث ذلك على نطاق واسع بما فيه الكفاية ، فقد يحدث رفض الخدمة (DoS). في هذه البحث ، نقترح طريقة شبه آلية للتعلم باستخدام خوارزمية الوسائل k ، لاكتشاف العقد الخبيثة في شبكة OBS. تم تدريب النموذج شبه المراقب المقترح والتحقق من صحته باستخدام بيانات كمية صغيرة من مجموعة بيانات مختارة. تُظهر التجارب أن النموذج يمكن أن يصنف العقد إلى فصول تتصرف أو لا تتصرف بدقة 90٪ عند التدريب باستخدام 20٪ فقط من البيانات. عندما يتم تصنيف العقد إلى فصول تتصرف ، لا تتصرف، وربما لا تتصرف ، فإن النموذج يظهر دقة 65.15 ٪ و 71.84 ٪ إذا تم تدريبه بنسبة 20 ٪ و 30 ٪ من البيانات على التوالي. مقارنة مع بعض الأعمال البارزة كشفت أن النموذج المقترح يتفوق عليها في كثير من النواحي.Optical burst switching (OBS) network is a new generation optical communication technology. In an OBS network, an edge node first sends a control packet, called burst header packet (BHP) which reserves the necessary resources for the upcoming data burst (DB). Once the reservation is complete, the DB starts travelling to its destination through the reserved path. A notable attack on OBS network is BHP flooding attack where an edge node sends BHPs to reserve resources, but never actually sends the associated DB. As a result the reserved resources are wasted and when this happen in sufficiently large scale, a denial of service (DoS) may take place. In this study, we propose a semi-supervised machine learning approach using k-means algorithm, to detect malicious nodes in an OBS network. The proposed semi-supervised model was trained and validated with small amount data from a selected dataset. Experiments show that the model can classify the nodes into either behaving or not-behaving classes with 90% accuracy when trained with just 20% of data. When the nodes are classified into behaving, not-behaving and potentially not-behaving classes, the model shows 65.15% and 71.84% accuracy if trained with 20% and 30% of data respectively. Comparison with some notable works revealed that the proposed model outperforms them in many respects

Semi-supervised learning approach using modified self-training algorithm to counter burst header packet flooding attack in optical burst switching network

Burst header packet flooding is an attack on optical burst switching (OBS) network which may cause denial of service. Application of machine learning technique to detect malicious nodes in OBS network is relatively new. As finding sufficient amount of labeled data to perform supervised learning is difficult, semi-supervised method of learning (SSML) can be leveraged. In this paper, we studied the classical self-training algorithm (ST) which uses SSML paradigm. Generally, in ST, the available true-labeled data (L) is used to train a base classifier. Then it predicts the labels of unlabeled data (U). A portion from the newly labeled data is removed from U based on prediction confidence and combined with L. The resulting data is then used to re-train the classifier. This process is repeated until convergence. This paper proposes a modified self-training method (MST). We trained multiple classifiers on L in two stages and leveraged agreement among those classifiers to determine labels. The performance of MST was compared with ST on several datasets and significant improvement was found. We applied the MST on a simulated OBS network dataset and found very high accuracy with a small number of labeled data. Finally we compared this work with some related works

Origin and Accumulation Mechanism of Gas Condensate in Kailashtila Gas Field, Sylhet Basin, Bangladesh

The Kailashtila gas field (KGF) is situated in the northeastern part of Sylhet basin, Bangladesh. This paperpresents chemical characteristics of extractable natural gas in drilled well KTL-2, in order to examine their potentialsource and maturity of organic matter, and hydrocarbon accumulation mechanism in the basin. The gas condensate inthe KTL-2 composed primarily of methane (85.81 wt.%), ethane (6.68 wt.%), propane (2.13 wt.%), and traces of higherhydrocarbons (i-butane, 0.69 wt.%; n-butane, 0.73 wt.%; i-pentane, 0.50 wt.%; n-pentane, 0.44 wt.%; hexane, 1.27wt.%; heptane, 0.99 wt.%; octane, 0.24 wt.%). Nitrogen and CO2 contents in the gas condensate are low (0.46 wt.%and 0.05 wt.%, respectively). Average dry coefficient (C1/C1–5) value in the gas condensate is 0.93 (0.91–0.95), whichreflects relatively mature hydrocarbon migrating from nearby deeply buried source rocks. The δ13C1 (–39 to –40‰) andC1/C(2+3) (19.77) variation diagram show that gas condensate in the KGF is mainly controlled by type III kerogen, andthe organic matter was thermally mature in nature. However, the relationships between stable isotope value of methane(δ13C1), ethane (δ13C2) and propane (δ13C3) indicate mainly thermogenic origin of the studied gas condensate, andminor input from mixed thermogenic and bacteriogenic processes

Mechanical and thermo-chemical degradation of concrete exposed to simulated airfield condition

 Surface degradation at parking aprons of military airfields concerns jet aircraft safety. It is caused by the disintegration of coarse aggregates in concrete. This study aims to understand the effects of repeated exposure to various aviation oils and high-temperature on concrete constituent materials. An airfield exposure condition was created to expose samples made with different water to cement ratios (w/c). Samples were tested for residual mechanical properties, thermal conductivity, specific heat, thermogravimetric and microstructural analysis. Results show that the w/c ratio of concrete significantly influences the residual strength of the exposed samples. Moreover, aviation oils react with ordinary concrete at higher temperatures and produce harmful salts. Besides, thermal incompatibility between the aggregates and cement paste triggers microcracks in cement paste and thermal cracks in the coarse aggregate. Due to the simultaneous thermal and chemical attack, concrete suffers the disintegration of aggregates and flake-like concrete pieces on the top surface

Sinteza i antimikrobno djelovanje novih derivata tienopirimidina

Reaction of heteroaromatic o-aminonitrile with ethyl N-/bis(methylthio)methyleneamino acetate resulted in annelation of a thieno/3,2-e/imidazo/1,2-c/pyrimidine moiety in a one step process. /1,2,4/Triazolo/4,3-c/thieno/3,2-e/pyrimidine derivatives were prepared by initial treatment of o-aminonitrile with carbon disulfide, followed by methylation with methyl iodide and subsequent reaction with benzhydrazide and thiosemicarbazide, respectively. Hydrazinothieno/2,3-d/pyrimidine was prepared by cyclization of heteroaromatic o-aminoester with formamide, followed by chlorination and subsequent displacement with hydrazine. Treatment of the hydrazine derivative with acetylacetone, benzaldehyde and acetic anhydride afforded pyrazolylpyrimidine, benzylidenehydrazonopyrimidine and trizolopyrimidine derivatives, respectively. Some of these derivatives exhibited pronounced antimicrobial activity.Reakcijom heteroaromatskih o-aminonitrila s etil N-/bis(metiltio)metilenamino acetatom u jednom sintetskom koraku došlo je do anelacije u tieno/3,2-e/imidazo/1,2-c/pirimidin. Derivati /1,2,4/triazolo/4,3-c/tieno/3,2-e/pirimidina pripravljeni su reakcijom o-aminonitrila s ugljikovim disulfidom, te metilacijom s metil-jodidom i naknadnom reakcijom s benzhidrazidom, odnosno tiosemikarbazidom. Hidrazinotieno/2,3-d/pirimidin je pripravljen ciklizacijom heteroaromatskog o-aminoestera s formamidom, te kloriranjem i supstitucijom s hidrazinom. Reakcijom hidrazinskog derivata s acetilacetonom, benzaldehidom ili anhidridom octane kiseline nastali su derivati pirazolilpirimidina, benzilidenehidrazonopirimidina, odnosno trizolopirimidina. Neki od tih derivata djeluju antimikrobno

Efficiency in the worst production situation using data envelopment analysis

Data envelopment analysis (DEA) measures relative efficiency among the decision making units (DMU) without considering noise in data.The least efficient DMU indicates that it is in the worst situation.In this paper, we measure efficiency of individual DMU whenever it losses the maximum output, and the efficiency of other DMUs is measured in the observed situation.This efficiency is the minimum efficiency of a DMU.The concept of stochastic data envelopment analysis (SDEA) is a DEA method which considers the noise in data which is proposed in this study.Using bounded Pareto distribution, we estimate the DEA efficiency from efficiency interval. Small value of shape parameter can estimate the efficiency more accurately using the Pareto distribution.Rank correlations were estimated between observed efficiencies and minimum efficiency as well as between observed and estimated efficiency.The correlations are indicating the effectiveness of this SDEA model

Stochastic frontier approach and data envelopment analysis to total factor productivity and efficiency measurement of Bangladeshi rice

The objective of this paper is to apply the Translog Stochastic Frontier production model (SFA) and Data Envelopment Analysis (DEA) to estimate efficiencies over time and the Total Factor Productivity (TFP) growth rate for Bangladeshi rice crops (Aus, Aman and Boro) throughout the most recent data available comprising the period 1989–2008. Results indicate that technical efficiency was observed as higher for Boro among the three types of rice, but the overall technical efficiency of rice production was found around 50%. Although positive changes exist in TFP for the sample analyzed, the average growth rate of TFP for rice production was estimated at almost the same levels for both Translog SFA with half normal distribution and DEA. Estimated TFP from SFA is forecasted with ARIMA (2, 0, 0) model. ARIMA (1, 0, 0) model is used to forecast TFP of Aman from DEA estimation