narayana reddy

narayana reddy

1582182281

Machine Learning and Information Security: Impact and Trends

Machine learning is the latest to make waves in the field of Information Security, and for good reason. The support of complex algorithms that ‘learn’ and grow is invaluable to human analysts, allowing them to focus on larger tactical fights and strengthen security systems to be virtually bulletproof. In both routine and structural changes to Information Security, machine learning plays an increasingly important role and will continue to do so, leading into the coming years.

What is Information Security (InfoSec)?

InfoSec refers to the systems, tools and processes that are designed and then deployed to field sensitive and confidential data from being compromised or tampered with. Disruption, modification and destruction of data are some of the more common results of InfoSec breaches. The protection of digital and non-digital data falls under InfoSec. Security processes must account for the safety of data regardless of what format it is in.

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Why is it important?
Technology continues to make significant inroads outside of IT to find acceptance in industries that were not “traditionally” in the purview of technology. The flip side is that there is an alarming increase in big data which, in turn, warrants the increased use of security measures to protect a growing clientele from data breaches and security threats.

These burning issues plaguing an increasingly digital world are indicators of the need for InfoSec:

  • Sophisticated attacks: Hackers are using agile technologies and unseen malware to breach and compromise data– an issue that traditional security systems are hopelessly unequipped to deal with.

Spike in the cost of breaches: By 2021, cybercrime is expected to rack up $6 trillion in losses, up from $3 trillion in 2015.
Weak links in organisational systems: InfoSec was traditionally considered an IT problem– this couldn’t be further from the truth. Attacks could occur from any weak link in the company regardless of the hierarchy or department, so it is imperative that the entire enterprise is protected by seamless security programmes.

Variety of threat categories: The plethora of causes and sources of security threats makes ensuring thorough InfoSec programmes that much more crucial. Threats could be due to deliberate acts of espionage, technical hardware mishaps or even basic human errors– a fool-proof InfoSec system is required to account for and act on a variety of threats.

Most InfoSec programmes are built around a trifecta referred to as the CIA triad, which stands for Confidentiality, Integrity and Availability.
Confidentiality: sensitive data is disclosed only to authorised parties who have a right to access and view said data

Integrity: sensitive data is protected from being deleted or modified by an unauthorised party and, if such data is deleted as a cause of human error by an authorised party, then the damage can be reversed

Availability: sensitive data can be accessed by the right people, albeit through secure access channels safeguarded by authentication systems

How was InfoSec being treated over the past years?

Hacker attacks date back to the 1970s, even though network computing was still in its nascent stages and the internet was still in the works. The first-ever known method of hacking or targeted attacks was through the infiltration of phone lines that were connected to computers.

The story was no better in the 1980s– in fact, a group of teenagers in the US broke into more than 60 corporate and military systems to siphon off more than $70 million from banks. Since then, security systems constantly failed to thwart threats and keep data safe as hackers only grew more sophisticated, leveraging state-of-the-art technology for their crimes. By 2010, cybercrime was a serious enough offence to warrant decades in prison.

Perimeter protection (think antiviruses and firewalls) was once heavily relied on, but today’s security systems are multi-layered because no matter how high the wall, it is still penetrable. The focus turned to data itself, and how to keep it protected when (not if) a breach occurs. InfoSec transitioned from firewalls and antivirus software on individual computers to encryption of data at multiple levels. Data encryption also evolved to be employed at any stage, from digital file to data transmission.

Multi-factor authentication also began being used as roadblocks to hinder all but authorised personnel from accessing data, even in its encrypted form. This setup uses two or more authentication processes that go beyond passwords and PINs to prevent attackers by closing off immediate access.

Examples of data breaches

In 2016, nearly 3.2 million debit cards were targeted during what is now known as the 2016 Indian Banks Data Breach. HDFC Bank, State Bank of India, YES Bank and ICICI were the worst hit. As a result of this breach, the country’s biggest card replacement program was conducted– SBI alone reported the blocking and replacement of 6 lakh debit cards.

The personal data of nearly 50 million worldwide Facebook users was compromised in 2018 after a debilitating InfoSec breach. Facebook was reported to have lost $30 billion as a result and the firm was also put through a thorough investigation within the US and conducted by the European Union.

Once again in 2018, confidential data of nearly 9.4 million Cathay Pacific Airlines passengers were exposed. Although no misuse was reported by the Hong Kong-established carrier, the leak was a crippling one nonetheless.

How can Machine Learning help Secure Data?
As hacks, threats and breaches grow increasingly sophisticated, the focus has turned to fighting fire with fire and staying one step ahead. Here is how machine learning is crucial to securing data in the face of large-scale breaches:

Finding Network Threats
By continuously monitoring data frameworks for anomalies or breaches, machine learning algorithms can effectively detect and deter threats. The ability of machine learning to process data in real-time is highly useful as it allows the detection of threats, insider breaches and malware as it occurs, preventing huge losses.

Protecting Cloud Data
Organisations are increasingly shifting their databases to the cloud to reduce the load on external servers and the hassle of maintenance. Machine learning can help secure data stored on the cloud by identifying and analysing suspicious cloud logins and carrying out the analysis of IP addresses and their reputation.

Encrypting Data
Homomorphic encryption is the process by which machine learning algorithms perform computations on existing encrypted data without having to decrypt it. The added perk of this process is that the results generated are also in ciphertext but, when decrypted, show the same results as they would have if the operation was performed on decrypted data.

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Evading Hacker Attacks
By using methodologies like behaviour analytics and pattern recognition, machine learning can help prevent data breaches well in advance– a change from scrambling to recover losses after a breach. It helps organisations be one step ahead of hackers to offset potential attacks and strengthen protection beforehand.

Facilitating Endpoint Security
Machine learning can be used to train endpoint security setups in identifying anomalies and malicious activities based on what it has already experienced and flagged. Since machine learning thrives on volumes and larger datasets, endpoint security can be continuously strengthened against newer threats based on past data and repositories.

Examples of Machine Learning being used in InfoSec

A UK-based startup, Darktrace, uses machine learning as a base for its Enterprise Immune System which enables third-party organisations to detect malicious intentions faster and stall attacks before they even occur. The firm said they had mitigated threats to one NHS agency network during the Wannacry ransomware crisis in 2017. To put the threats into perspective, the ransomware had successfully breached security systems of around 2 lakh victims in more than 150 countries.

MIT’s CSAIL (Computer Science and Artificial Intelligence Lab) created a system. AI2, which reviews crores of logins each day to filter out anomalies and pass it on to a human analyst for review. The experiment that CSAIL carried out in partnership with a startup showed attack detection rates rising to 85% with a decrease in false positives. This development is also an example of machine learning being used to further automation to free the hands of analysts for deeper research.

Homomorphic encryption and the use of machine learning makes for a great case study in data ethics. There is also the concept of differential privacy, the mathematical framework of which is used to understand the extent machine learning algorithms can ‘remember’ information it shouldn’t, and make necessary changes to increase privacy guarantees.

Machine learning is all set to drive most Information Security efforts in the coming decade. These algorithms aren’t just providing protection against breaches, they’re also unearthing vital information and patterns that are invaluable to strengthening proactive security systems.

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Machine Learning and Information Security: Impact and Trends
sophia tondon

sophia tondon

1620898103

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Nora Joy

1604154094

Hire Machine Learning Developers in India

Hire machine learning developers in India ,DxMinds Technologies is the best product engineering company in India making innovative solutions using Machine learning and deep learning. We are among the best to hire machine learning experts in India work in different industry domains like Healthcare retail, banking and finance ,oil and gas, ecommerce, telecommunication ,FMCG, fashion etc.
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Hire machine learning developers in India ,DxMinds Technologies is the best product engineering company in India making innovative solutions using Machine learning and deep learning. We are among the best to hire machine learning experts in India work in different industry domains like Healthcare retail, banking and finance ,oil and gas, ecommerce, telecommunication ,FMCG, fashion etc.

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Nora Joy

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Applications of machine learning in different industry domains

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
  • Drug Discovery and Manufacturing
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Machine Learning-based Behavioral Modification
  • Smart Health Records
  • Clinical Trial and Research
  • Better Radiotherapy
  • Crowdsourced Data Collection
  • Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
  • Risk management
  • Investment predictions
  • Customer service
  • Digital assistants
  • Marketing
  • Network security
  • Loan underwriting
  • Algorithmic trading
  • Process automation
  • Document interpretation
  • Content creation
  • Trade settlements
  • Money-laundering prevention
  • Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning

Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Nora Joy

1607006620

Hire Machine Learning Developer | Hire ML Experts in India

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
  • Drug Discovery and Manufacturing
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Machine Learning-based Behavioral Modification
  • Smart Health Records
  • Clinical Trial and Research
  • Better Radiotherapy
  • Crowdsourced Data Collection
  • Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
  • Risk management
  • Investment predictions
  • Customer service
  • Digital assistants
  • Marketing
  • Network security
  • Loan underwriting
  • Algorithmic trading
  • Process automation
  • Document interpretation
  • Content creation
  • Trade settlements
  • Money-laundering prevention
  • Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning

Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Ananya Gupta

1595485129

Pros and Cons of Machine Learning Language

Amid all the promotion around Big Data, we continue hearing the expression “AI”. In addition to the fact that it offers a profitable vocation, it vows to tackle issues and advantage organizations by making expectations and helping them settle on better choices. In this blog, we will gain proficiency with the Advantages and Disadvantages of Machine Learning. As we will attempt to comprehend where to utilize it and where not to utilize Machine learning.

In this article, we discuss the Pros and Cons of Machine Learning.
Each coin has two faces, each face has its property and highlights. It’s an ideal opportunity to reveal the essence of ML. An extremely integral asset that holds the possibility to reform how things work.

Pros of Machine learning

  1. **Effectively recognizes patterns and examples **

AI can survey enormous volumes of information and find explicit patterns and examples that would not be evident to people. For example, for an online business site like Amazon, it serves to comprehend the perusing practices and buy chronicles of its clients to help oblige the correct items, arrangements, and updates pertinent to them. It utilizes the outcomes to uncover important promotions to them.

**Do you know the Applications of Machine Learning? **

  1. No human mediation required (mechanization)

With ML, you don’t have to keep an eye on the venture at all times. Since it implies enabling machines to learn, it lets them make forecasts and improve the calculations all alone. A typical case of this is hostile to infection programming projects; they figure out how to channel new dangers as they are perceived. ML is additionally acceptable at perceiving spam.

  1. **Constant Improvement **

As ML calculations gain understanding, they continue improving in precision and productivity. This lets them settle on better choices. Let’s assume you have to make a climate figure model. As the measure of information you have continues developing, your calculations figure out how to make increasingly exact expectations quicker.

  1. **Taking care of multi-dimensional and multi-assortment information **

AI calculations are acceptable at taking care of information that is multi-dimensional and multi-assortment, and they can do this in unique or unsure conditions. Key Difference Between Machine Learning and Artificial Intelligence

  1. **Wide Applications **

You could be an e-posterior or a social insurance supplier and make ML work for you. Where it applies, it holds the ability to help convey a considerably more close to home understanding to clients while additionally focusing on the correct clients.

**Cons of Machine Learning **

With every one of those points of interest to its effectiveness and ubiquity, Machine Learning isn’t great. The accompanying components serve to confine it:

1.** Information Acquisition**

AI requires monstrous informational indexes to prepare on, and these ought to be comprehensive/fair-minded, and of good quality. There can likewise be times where they should trust that new information will be created.

  1. **Time and Resources **

ML needs sufficient opportunity to allow the calculations to learn and grow enough to satisfy their motivation with a lot of precision and pertinence. It additionally needs monstrous assets to work. This can mean extra necessities of PC power for you.
**
Likewise, see the eventual fate of Machine Learning **

  1. **Understanding of Results **

Another significant test is the capacity to precisely decipher results produced by the calculations. You should likewise cautiously pick the calculations for your motivation.

  1. High mistake weakness

AI is self-governing yet exceptionally powerless to mistakes. Assume you train a calculation with informational indexes sufficiently little to not be comprehensive. You end up with one-sided expectations originating from a one-sided preparing set. This prompts unessential promotions being shown to clients. On account of ML, such botches can set off a chain of mistakes that can go undetected for extensive periods. What’s more, when they do get saw, it takes very some effort to perceive the wellspring of the issue, and significantly longer to address it.

**Conclusion: **

Subsequently, we have considered the Pros and Cons of Machine Learning. Likewise, this blog causes a person to comprehend why one needs to pick AI. While Machine Learning can be unimaginably ground-breaking when utilized in the correct manners and in the correct spots (where gigantic preparing informational indexes are accessible), it unquestionably isn’t for everybody. You may likewise prefer to peruse Deep Learning Vs Machine Learning.

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