Program Protection through Software-based Hardware Abstraction

The field of Artificial Intelligence (AI) known as machine learning (or ML for short) helps develop systems that can learn from examples, spot trends, and make rational decisions with little to no human intervention. Cybersecurity methods provide up-to-date security answers for detecting and countering dangers. Security solutions that were formerly adequate are now insufficient since criminals may circumvent traditional security procedures. Cybersecurity refers to the practice of keeping computer systems, servers, mobile devices, networks, and the data associated with them safe from malicious attacks. The two most critical parts of combining ML with cyber security are (1) ensuring that cyber security is taken into consideration whenever ML is used and (2) utilising ML to facilitate cyber security. We hope this union will help us in many ways; for example, by making cyber security procedures more efficient, bolstering the safety of machine learning models, and allowing us to detect zero-day vulnerabilities with little human intervention.

Section: Discussionmentioning

confidence: 99%

A Review and Analysis on Various Cyber Security Threats on the Basis of Machine Learning Algorithms

Srinivas A Vaddadi, Rohith Vallabhaneni, Sravanthi Dontu

2023

“…Then, at that point, assuming the locale is further from the sink than its area, it stores the information and holds it up till it arrives at its sending scheduled opening. Then, in communicating time, it picks the following district considering their need for sending, in the base of distance to sink and have sufficient energy [21][22][23][24][25][26][27][28][29][30]. Then, at that point, it will begin to send.…”

Section: Energy Harvesting Opportunistic Routing Protocolmentioning

confidence: 99%

Several Categories of Energy Harvested Routing Protocols, Challenges, and Characteristics in WSN: A Review

Bhanu Priyanka Valluri

2023

The routing protocol is a technique for determining the most efficient channel for data transmission. The route selection procedure, which relies on the kind of network, channel conditions, and measurement systems, presents several challenges. Routing is essential in Wireless Sensor Networks (WSNs) for environmental monitoring, traffic monitoring, and other applications. WSNs are small nodes that can sense, interpret data, and communicate wirelessly. Many routing, power control, and data dissemination techniques have been developed specifically for WSNs, where energy efficiency is a crucial design factor. On the other hand, the focus has been on energy harvesting and standard routing methods, which can vary depending on the design and network architecture. In a Wireless Sensor Network (WSN), the data collected by the sensor nodes is typically transferred to the base station, which connects the sensor network to other networks (such as the internet), where it is processed and necessary action is taken. WSN has recently been developed to allow various applications, including traffic enforcement building automation, smart warfare, environmental sensing, and many more.WSN integrates several sensors or nodes deployed around a specific node to perform computational processes.

“…The optimum conditions for feed rate and depth of cut is 0.2 mm/rev and 0.5 mm as higher the value higher will be the roughness. [26][27][28][29][30][31][32] The bright spots indicates the effect of surface roughness (Ra) in connection with input parameters, Hence all the interactions between the variables, especially the effect caused with respect to MQL flow rate to speed, feed and depth of cut are to be more systematic compared with other effects. It is observed from the counter plots to get better surface roughness higher value of MQL flow rate, speed and lower value of feed and depth of cut is to be used.…”

Section: Fig7 Residual Plots Of Regression Model Of Surface Roughnessmentioning

confidence: 99%

Parametric Analysis of EN 353 Alloy Using MQL Machining with Response Surface Method

A. Venkata Vishnu

2023

Heat generated in any conventional machining between tool and work- piece can be minimized by the use of cutting fluids since the latter plays a vital role in terms of improving surface quality and its characteristics because of its properties like continuous lubrication, cooling and chips flushing etc. As extensive use of cutting fluids increase the machining costs, the problem can be overcome by employing a sustainable manufacturing technique known as Minimum Quantity Lubrication (MQL) which reduces the flow of cutting fluid. On the other hand researches are focusing on Nano based fluids which are gaining more importance. As nano based cutting fluids are colloidal mixture of nano sized particles and a base fluid, which enhance the heat transfer characteristics suitable for machining applications. The present paper focuses on the application of Hybrid Nano based cutting fluids for machining of alloy steel under different MQL flow conditions. Using response surface methodology (RSM), an optimum solution is obtained for the selected input parameters to the output response-surface roughness. A regression relation is developed between the input and output responses with the help of Response surface methodology (RSM).