WAF 2.0 by BitNinja: Ultimate Web Application Firewall for your Compute Security

April 1, 2021

With the deployment of a strong web application firewall, one can run secured and critical web applications wherever they reside such as in a public cloud, or on-premise data centre. A powerful WAF solution protects organizations against OWASP top ten threats, various application vulnerabilities, and zero-day attacks.

In today’s world, enterprises are exploring their businesses with the usage of more web-based Cloud-hosted applications, so a more powerful web application firewall (WAF) isn’t a luxury—it’s a requirement, a need of the hour for the cloud infrastructure. A powerful WAF also allows compliance with some key regulatory standards like PCI DSS.

Let’s take a look at why having a WAF is so important, how it works, and the options you have to protect your server, from open source solutions to the WAF designed at BitNinja.

Web application attacks are the biggest threat in today’s World

The main security challenge that you face as a sysadmin is the increasing number of web applications, plugins and other software running on your servers. Customers demand the latest platforms, CMS and server management tools, and you’ve got to provide these features to keep their business.

Attacks on servers have become more and more complex. Rather than targeting brute force or other “typical” methods of attack, hackers are now exploiting vulnerabilities in out-of-date and insecure plugins and web apps.

You can create a basic firewall with IPTables and monitor bandwidth for IPs, but you’ll never be completely secure until you’ve locked down your web apps. In fact, a recent study found that 73% of all security exploits are directed against web applications.

With more than two-thirds of attacks directed towards web apps, it’s clear they pose the biggest threat to server security. And you can’t simply block these apps. You’ve got to allow access to keep your customers happy, and you’ve got to keep the bad guys out. The solution is to implement a web application firewall which selectively blocks exploits, and you’ve got a few options when choosing a WAF.

Consideration for choosing a suitable Web Application Firewall

When you start looking for a WAF for your server, you’ll see a lot of open source options in the search results. This is a good place to start because open source projects provide a clear picture of what’s needed in a web application firewall, and how they work.

Perhaps the most well-known resource on WAF’s is the Open Web Application Security Project (OWASP), a worldwide non-profit organization dedicated to making software and server security “visible so that individuals and organizations are able to make informed decisions”.

On Wiki, you can read about the top 10 web application security problems. The “OWASP Top 10” highlights the primary security concerns when creating or implementing a WAF on your server. These are the main attacks that a WAF is designed to stop, and the list also tells you a bit about how a WAF works to secure your server.

The OWASP Top 10 Web Application Security Risks

Here’s the latest including a brief description of the types of exploits a WAF is designed to stop:

ModSecurity – Open Source WAF based on OWASP

When it comes to open source web application firewalls, ModSecurity is at the top of the list. In some ways, it’s the only open-source WAF, because other open source solutions are targeted for specific frameworks, for example, NAXSI which is just for NGINX, and Webknight which is for Microsoft servers.

ModSecurity, which is an OWASP project, covers Apache, NGINX and Microsoft web servers and is highly based upon the Top 10 list and providing a base level of protection for every server. The primary drawbacks are that ModSecurity is a command-line only tool and is “help yourself” when it comes to support.

For DIY solutions, ModSecurity is a great place to start. If you’re willing to roll up your sleeves and do some hand-coding, it can provide reasonable protection. Things to look out for along the way are keeping up-to-date with the latest versions and making sure ModSecurity doesn’t interfere with the applications when they are running, especially when you begin modifying the rules for your specific needs.

Why Choose BitNinja WAF 2.0 for your server?

1. Industry Standards and Compatibility

BitNinja WAF 2.0 built on the backbone of ModSecurity. It’s the industry standard and compatible across a wide range of platforms. Using ModSecurity as the base for our platform ensures that our WAF is always up-to-date with the latest best practices according to OWASP and the worldwide security community.

2. Less Configuration, More Protection

While ModSecurity provides adequate protection for web servers, BitNinja wanted to go a step further and create a WAF that would protect against vulnerabilities before they were discovered.

The command line interface also presents a big roadblock for using ModSecurity “out of the box”. With BitNinja WAF 2.0 our goal was to create a WAF that was easy to use and didn’t require constant configuration. We wanted to be able to make changes with a few clicks.

3. Easy-to-Use Dashboard

Here’s a screenshot of the BitNinja WAF 2.0 dashboard in action. You can enable/disable the firewall or activate/deactivate a pattern or ruleset for all your servers, all in one place:

4. Pre-defined rulesets for low false positives

One of the primary challenges as you add layers of security to ModSecurity is preventing the WAF from blocking web apps. Often you’ll implement one rule to protect an app, only to find that it blocks access to another app.

BitNinja developed and continually refined a default rule set for all the websites hosted on your server. This ruleset is rigorously tested to ensure the lowest false positive rates and constantly updated with safety rules that protect your server while allowing access to all your web apps.

For those who want a greater level of control, it also gives you the option to change the rules one-by-one or manage them by categories.

5. Domain-based WAF controls to keep users happy

The primary goal for any server admin is to keep their customers and users happy. Every website has its own specific needs, and there are often individual requests from users and site owners.

To make day-to-day life easier, BitNinja created a built-in option in our WAF that allows you to add custom patterns and rulesets for each domain. You can also disable the WAF entirely for only a subdomain if necessary. This is a great way to keep your customers satisfied and still provide great protection.

6. Lock-down feature for emergency situations

When disaster strikes, it helps to be prepared. BitNinja WAF 2.0 includes a handy lock-down feature that immediately disables POST requests (registrations, logins, posting, etc) and converts the site to read-only mode. This restricted mode leaves the site available for visitors while preventing further hacking attempts as the situation is mitigated. It’s a win-win situation that allows you to calmly address sudden increases in attacks from botnets and other distributed types of attacks.

7. Log-only or Active Protection

To provide a way to monitor activity without blocking it. Sometimes, you need to troubleshoot the configuration of a web app, and you need to rule out the possibility of the WAF interfering.

In Log-only mode, you can see all the logged (but not blocked) incidents using the Dashboard. In this case, connections are not interrupted by the WAF. This allows you to monitor any incidents and manually block the IPs if you find positive hits, as well as implement web apps with complex configuration before turning the switch to the firewall “on”.

To keep your other sites and servers protected while you monitor traffic or install an app, you can choose between Log-only mode and Active Protection by the server and even by domain.

WAF 2.0 is completely integrated with the BitNinja Security Suite

At BitNinja, take a holistic approach to cybersecurity. Different types of attacks require different types of defence for a server. It is like security at a castle Vs an airport. With a castle, you put all your defences in one place, leaving you vulnerable to multiple attacks. However, in the case of an airport, you have multiple checkpoints for defence protecting you by closing all the security loopholes.

In addition to WAF 2.0, BitNinja’s Security Suite includes 8 other security modules: IP Reputation, Port Honeypot, Web Honeypot, DoS Detection, Log Analysis, Malware Detection, Outbound WAF and Protection for HTTPS. Each of these modules works together to provide multiple points of defence for your servers against a wide range of attacks, from hackers, botnets and whatever’s next on the horizon.

Try  WAF 2.0 and the BitNinja Server Security Suite with E2E Networks compute in one click during the launch of an instance or enable with your running instances without any obligation.

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This is a decorative image for Project Management for AI-ML-DL Projects
June 29, 2022

Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

Reference Links:

https://www.datacamp.com/blog/how-to-manage-ai-projects-effectively

https://appinventiv.com/blog/ai-project-management/#:~:text=There%20are%20six%20steps%20that,product%20on%20the%20right%20platform.

https://www.datascience-pm.com/manage-ai-projects/

https://community.pmi.org/blog-post/70065/how-can-i-manage-complex-ai-projects-#_=_

This is a decorative image for Top 7 AI & ML start-ups in Telecom Industry in India
June 29, 2022

Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.

 

The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.

 

In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.

 

Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:

Wiom

Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 

TechVantage

As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online

Manthan

As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 

NetraDyne

Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.

 

Helpshift

Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 

Facilio

An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.

 

In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.

 

 

References

https://www.inventiva.co.in/trends/telecom-startup-funding-inr-30-crore/

https://www.mygreatlearning.com/blog/top-ai-startups-in-india/

This is a decorative image for Top 7 AI Startups in Education Industry
June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

Reference Links:

https://belitsoft.com/custom-elearning-development/ai-in-education/ai-in-edtech

https://www.emergenresearch.com/blog/top-10-leading-companies-in-the-artificial-intelligence-in-education-sector-market

https://xenoss.io/blog/ai-edtech-startups

https://riiid.com/en/about

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