Month: December 2021

Big Data Security Intelligence – Analytics Tools

Posted on by Bijaya Bhattacharjee

The rapid progress of technology is changing the course of the world and how we live in it. Today, we are generating and consuming data at enormous rates, creating a need for platforms of storage, tools for data analysis and retrieval, and data security. Companies (TechTarget) have migrated from traditional work processes and environments to cloud networking and online data storage as a result of digital transformation. Cyber security is one such critical requirement for facilitating efficient digital data processing, as any exposure to sensitive information could result in serious data security and vulnerability compromises. Advances in data analytics have resulted in the development of advanced tools that can evaluate and process data and information in order to accurately predict the occurrence of cyber-attacks and prevent them before any security lapse occurs.

 

Transition to Big Data Processing

The advent of smartphones and SaaS systems has led to the generation of information at an enormous rate that cannot be handled by traditional data processing tools and methods. Nearly 90% of all data generated has been in the past two years (Kommandotech). The use of digital tools to analyze huge sets of data and retrieve essential information and interpretations of the data, forms the foundation of Big Data Processing. Smartphones and other devices generate vast amounts of data containing highly sensitive information like bank details, transaction details, and personal details too that could be retrieved from data storage using big data analytics, bringing about the need to create fail-safes that will prevent abuse of these tools.

Data Security using Big Data Analytics

Big Data Analytics has a wide number of applications in Data Security as it helps facilitate information retrieval from various security sources like firewalls, security devices, web traffic etc. Its ability to integrate unstructured data from multiple sources under a single analytical network enables superior data analysis and interpretation for companies and enterprises. Experion’s big data security solutions have helped businesses detect anomalies up to 40% faster, significantly reducing the risk of data breaches and ensuring a safer digital environment. A few of the applications of data security using big data analytics are:

  • Network Flow Monitoring to Track Botnets – Analytical tools like MapReduce can identify and track infected hosts participating in a botnet by evaluating enormous amounts of NetFlow data within a short span of time, largely simplifying data processing as compared to traditional processing systems. It is the process of discovering patterns in large data sets using methods from artificial intelligence, machine learning, statistics, and database systems. Data mining is used to extract information from a data set and convert it to an analytical structure.
  • Enterprise Event Analytics – Multinational Companies and enterprises generate overwhelming amounts of data every day, creating a need for highly efficient analytical tools to generate valuable information by analyzing data. An effective enterprise analytics strategy can provide a comprehensive vision and end-to-end roadmap for data management and analysis. It can help with risk management, mapping out a company’s data management architecture, identifying and removing redundant data, establishing responsibility and accountability, and improving data quality, among other things.
  • Advanced Persistent Threats Detection – Advanced Persistent Threats are one of the most serious threats faced by organizations today. It is the strategized attack of specific, high-value assets in the digital architecture that operates in different modes like “Low profile” and “Slow” to avoid detection and prolonged execution respectively. Detection and tracking of such threats are cumbersome as huge loads of data must be evaluated to identify them, making big data analytics the ideal solution for tracking them. It is suitable for compliance needs and forensic investigations while also offering insights on user behavior that help track future threats efficiently.
  • Data Sharing and Provenance – The use of big data analytical systems allow companies and enterprises to research and review the results of cybersecurity experiments conducted across the world. The Worldwide Intelligence Network Environment (WINE) (Cloud Security Alliance) provides a platform for data sharing and analysis to research on the field data aggregated online by Symantec. These platforms allow companies to test out and validate novel ideas on real-world data and compare different algorithms and systems against reference data sets to evaluate efficiency. Data Provenance is information about the origin and process of data creation.  Such information helps in debugging data and transformations, auditing, evaluating data quality and trust, modelling authenticity, and implementing access control for derived data.

Conclusion

 

Big Data Analytics holds the potential to unlock high levels of efficiency and performance from companies and enterprises as it simplifies data analysis of massive amounts of data and provides access to actionable information easily. The element of versatility it holds in serving various applications in data analytics makes it a critical requirement for data processing companies. Big data analytics helps in making better-informed decisions, improving the supply chain, operations, and other strategic decision-making areas.

 

Electric Vehicles Revolution with Quantum Computing

Posted on by Elia

We live in a fast-paced world where people are frantically juggling their professional and personal lives. With advances in science and technology, the transportation and communication sectors have advanced significantly, reducing the amount of time, resources, and effort expended in travel. Electric vehicles have evolved over time, with the assistance of artificial intelligence and quantum computing, to become highly efficient and optimized for people’s transportation.

Quantum computing has a variety of applications in revolutionizing the automobile industry such as improving battery performance, avoiding traffic congestions, preventing car accidents and mishaps by machine learning and analysis etc. that can greatly benefit the sector when they come to fruition. Machines in quantum computing work with physical properties of matter, such as superposition or entanglement, which means that calculations can be performed on multiple states of matter at the same time, drastically reducing computation time.

Advantage of Quantum Computing The brainchild of Nobel laureate Dr Richard Feynman, quantum computing has progressed to enormous levels of growth, finding a variety of applications in different fields and sectors. Quantum computing involves simulation of the physical nature of objects at subatomic sizes while allowing them to exist in more than one state. This allows rapid simulation and processing of data than conventional systems, making quantum computers much more powerful, efficient, and faster. It has been applied in fields like Cryptography, Medicine, and material sciences to accommodate multiple variables or molecules in simulations to reach the desired end product or solution. Various automobile companies like BMW (CNET) and Hyundai (Eetasia) have started working with quantum computing systems to solve various issues like cost optimization, development of new batteries, optimization of components to improve cost-effectiveness etc.

Quantum Computing in Battery Technology

Quantum computing has been applied to develop effective solutions in improving the battery technology in cars and automobile systems as it can simulate multiple molecules of compounds simultaneously in different states, conditions, and environments to help identify the ideal combination of variables. Hyundai Motor Co. has partnered up with quantum computing experts to develop a robust battery that can function with improved capabilities and durability when used in electric vehicles. They aim at reducing the cost of battery development and production to reduce the overall cost of the vehicles, improve affordability and progress towards sustainability. A quantum computer of sufficient complexity—for example, enough quantum bits or “qubits”—could theoretically achieve a quantum advantage, allowing it to solve problems that no classical computer could ever solve. In theory, a quantum computer with 300 qubits fully dedicated to computation could perform more calculations in an instant than the visible universe’s atoms Quantum computing has also been applied in the development of novel technologies that can improvise the functioning of EV batteries by incorporating advanced technologies to cool them. It is applied by compartmentalizing big issues into individual parameters that are simulated using quantum computing to be later integrated into the conventional systems as a hybrid model or to fashion a completely new model by combining the solutions offered by quantum computing(EENewsEurope).

Quantum Computing in Autonomous Driving Quantum computing can facilitate the design and development of powerful operating systems to produce self-driving cars, simplifying transportation and reducing the chances of human errors in road traffic accidents. Artificial intelligence and machine learning require the real-time analysis of vast amounts of data to produce optimal responses to changing environmental conditions and quantum computing with its excellent computational features

can lend a hand in facilitating the requirements. Volkswagen(Prescouter) has experimented in the design and development of computational systems to optimize traffic control and regulation in the city of Beijing and has found great success in this venture. It also has applications in improving vehicle to vehicle and vehicle to cloud communications in next-generation cars that are expected to have the ability to communicate with cloud computing systems to regulate driving data. This will help in traffic and fuel optimization in cloud-connected cars while providing a safe environment for decentralized communication between them.

Conclusion

Quantum computing has unlimited potential and practical applications across different fields and sectors and can make a path for enormous progress in the automobile sector. Companies and enterprises in the automobile sectors would greatly benefit by working with quantum computing as it is a leap towards greater sales and a greener environment. Recently, quantum computing has gained a lot of traction in both general society and the private sector. Companies have been pouring huge sums of money into quantum computing research, with the last few years being the busiest for this innovation.