AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities

With the expansion of the digital world, the number of Internet of things (IoT) devices is evolving dramatically. IoT devices have limited computational power and a small memory. Consequently, existing and complex security methods are not suitable to detect unknown malware attacks in IoT networks. This has become a major concern in the advent of increasingly unpredictable and innovative cyberattacks. In this context, artificial immune systems (AISs) have emerged as an effective malware detection mechanism with low requirements for computation and memory. In this research, we first validate the malware detection results of a recent AIS solution using multiple datasets with different types of malware attacks. Next, we examine the potential gains and limitations of promising AIS solutions under realistic implementation scenarios. We design a realistic IoT framework mimicking real-life IoT system architectures. The objective is to evaluate the AIS solutions’ performance with regard to the system constraints. We demonstrate that AIS solutions succeed in detecting unknown malware in the most challenging conditions. Furthermore, the systemic results with different system architectures reveal the AIS solutions’ ability to transfer learning between IoT devices. Transfer learning is a pivotal feature in the presence of highly constrained devices in the network. More importantly, this work highlights that previously published AIS performance results, which were obtained in a simulation environment, cannot be taken at face value. In reality, AIS’s malware detection accuracy for IoT systems is 91% in the most restricted designed system compared to the 99% accuracy rate reported in the simulation experiment

Domestic thermoelectric cogeneration system optimization analysis, energy consumption and CO 2 emissions reduction

In this paper, a domestic thermoelectric cogeneration system (DCS) is suggested. This system permits to use the lost heat of exhaust gases to simultaneously heat water and produce electricity via thermoelectric generators (TEG). To proceed, the concept of the system is drawn and the corresponding thermal modeling is developed. An optimization analysis, based on the position of the thermoelectric generators within the system, is carried out using the thermal modeling. The TEGs are places on the inner or outer walls of the tank or the pipe (cases 2–5), or on all of them (case 6). Results show that water can be heated to up to 97 °C, when TEGs are located on the inner wall of the tank. More the TEGs are nearer to the exhaust gases, higher is the total power produced by the TEGs and lower is the water temperature. The power produced by one TEG in direct contact with the exhaust gases is 0.35 W and the water temperature is 76 °C. Also, a DCS with TEG located at all layers can generate up to 52 W and 81 °C hot water, however this configuration has high initial cost. An economic and environmental concerns are considered. Results show that DCS with TEGs located on the inner wall of the pipe has a payback period of 1 year and 8 months when water is heated 60 times per month. In addition to that, it was shown that the location of TEGs do not affect the amount of CO2 gas reduced which is about 6 tons yearly. Finally, this study shows that the configuration where TEGs are placed at the inner wall of the pipe is the most cost-effective energy recovery configuration

New Hybrid heat recovery concept applied to exhaust gas

International audienc

Computational Discovery of Energy-Efficient Heat Treatment for Microstructure Design using Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is employed to develop autonomously optimized and custom-designed heat-treatment processes that are both, microstructure-sensitive and energy efficient. Different from conventional supervised machine learning, DRL does not rely on static neural network training from data alone, but a learning agent autonomously develops optimal solutions, based on reward and penalty elements, with reduced or no supervision. In our approach, a temperature-dependent Allen-Cahn model for phase transformation is used as the environment for the DRL agent, serving as the model world in which it gains experience and takes autonomous decisions. The agent of the DRL algorithm is controlling the temperature of the system, as a model furnace for heat-treatment of alloys. Microstructure goals are defined for the agent based on the desired microstructure of the phases. After training, the agent can generate temperature-time profiles for a variety of initial microstructure states to reach the final desired microstructure state. The agent's performance and the physical meaning of the heat-treatment profiles generated are investigated in detail. In particular, the agent is capable of controlling the temperature to reach the desired microstructure starting from a variety of initial conditions. This capability of the agent in handling a variety of conditions paves the way for using such an approach also for recycling-oriented heat treatment process design where the initial composition can vary from batch to batch, due to impurity intrusion, and also for the design of energy-efficient heat treatments. For testing this hypothesis, an agent without penalty on the total consumed energy is compared with one that considers energy costs. The energy cost penalty is imposed as an additional criterion on the agent for finding the optimal temperature-time profile

Using Real-Time PCR to Investigate Some of Antibiotic Resistance Genes from Streptococcus agalactiae Isolates from ewe Mastitis cases in Nineveh province

هذه الدراسة ، من بين مجموعه 856 حالة من حالات التهاب الضرع في النعاج المرضعة ، أظهرت 34 عزلة فقط من المكورات العقدية اكالكشيا أنواعًا مختلفة من المقاومة لثلاثة أنواع من المضادات الحيوية (البنسلين والإيثروميسين والتتراسيكلين). تم التعرف على عزلات المكورات العقدية اكالكشيا وفقًا للطرق القياسية, بما في ذلك تقنية جديدة مقترحة باستخدام الاكار الكروموجيني المتخصص. حيث حددت المقاومة البكتيرية للمضادات الحيوية باستخدام مقايسة الصفيحة الميكروية (بطريقة التخفيف). أيضا، تم استخدام  تقنية تقنية الوقت الحقيقي لتفاعل سلسلة البوليمر, حيث تم تحديد أن هناك ثلاثة مقاومة لمورثات الجينات (pbp2b  وtetO و mefA). اظهرت النتائج نسبة التتراسيكلين (20.59%) مرتفعة من بين العزلات التي تحمل جين واحد، يليها نسبة البنسلين (17.65%), بينما أدنى نسبة كانت في الإريثروميسين (11.77%). مع ذلك,  كان هناك العديد من العزلات التي تحمل جينين  مقاومة للمضادات الحيوية يتمثل بـ البنسلين والاريثروميسين حيث تشكل  (38.22%), في حين كانت النسبة المئوية للبنسلين والتتراسيكلين( 11.77%). في المقابل ، لم يتم الكشف عن أي جين شائع مع اثنين من المضادات الحيوية (الاريثروميسين والتتراسيكلين). من ناحية أخرى ، لم يتم العثور على اي نتائج بالنسبة  لمشاركة مع ثلاثة أنواع من الجينات المقاومة للمضادات الحيوية معا ( pbp2bو tetO و mefA). كشفت هذه الدراسة أن عزلات المكورات العقدية اكالكشيا هي السبب الحقيقي وراء التهاب الضرع المتكرّر في النعاج العراقية المرضعة وخصوصاً في هذه الدراسة للبكتيريا التي تحمل جينات مقاومة للمضادات الحيوية المختلفة.In this study, from a total of 856 mastitis cases in lactating ewes, only 34 Streptococcus agalactiae isolates showed various types of resistance to three types of antibiotics (Penicillin, Erythromycin and Tetracycline). St. agalactiae isolates were identified according to the standard methods, including a new suggested technique called specific Chromogenic agar. It was found that antibiotic bacterial resistance was clearly identified by using MIC-microplate assay (dilution method). Also, by real-time PCR technique, it was determined that there were three antibiotics genes resistance ( pbp2b, tetO and mefA ). The high percentage of isolate carried of a single gene which was the Tetracycline (20.59%) followed by percentage Penicillin was (17.65%) and the lowest was in Erythromycin (11.77%). However, there were many isolates that carried two genes of antibiotics resistance represented by Penicillin and Erythromycin with collective present of 38.22%, and for the Penicillin and Tetracycline, the percentage was found to be 11.77%. In contrast, no common gene with two antibiotics (Erythromycin and  Tetracycline) was detected. On the other hand, it was found that no bacterial sharing with three kinds of antibiotic resistance genes ( pbp2b, tetO and mefA ). This study has revealed that the St. agalactiae isolates did induce recurrent mastitis in lactating Iraqi's ewes.&nbsp

Experimental study on heat recovery using multi tube tank: effect of changing the head shape

This work presents a heat recovery system utilized to heat water from exhaust gases of a chimney. A waste heat recovery system is suggested named as “multi tube tank”. The suggested design is illustrated and described. The system is constructed and tested. In order to enhance the system effect of changing the head shape is studied. Two head were constructed: cylindrical and conical. Results shows that conical head reflected better performance compared to cylindrical head. For a cylindrical head water temperature increase to maximum 60 °C in 275 min. while for conical 16 head water temperature increased to 70 °C in 275 min and the system was able to increase the water temperature more up to 80 °C in 400 min