Digital pirates are keeping up with the times! They use the same technology that OTT and streaming platforms use to distribute unlicensed content. They have built their own ecosystem with cutting-edge technology. Now, all that a pirate needs are a streaming box and a high-speed reliable internet connection.
The days of torrents, cumbersome search for content, painful downloads, and low-quality content have gone. The content is just out there easily accessible, and high quality too.
The only way content owners and anti piracy firms can fight this evolving form of digital piracy is by using computing and machine learning prowess. Effective anti piracy and content protection is near impossible with traditional anti piracy measures. This is where using machine learning to fight digital piracy becomes inevitable.
How can machine learning be used to fight digital piracy?
Fighting digital piracy is all about staying a step ahead of pirates. Using machine learning and data analytics in combination with natural language processing (NLP), keyword searches, etc., (to identify sites hosting pirated and unlicensed content) is a great way to fight digital piracy. Let us understand how!!
What is Machine Learning? How can it be used for fighting Digital Piracy?
Machine Learning is a subset of Artificial Intelligence. Machine learning is the ability of systems to automatically process big data and produce reliable and repeatable outcomes. It is based on the concept that machines can learn from data, identify patterns, and perform specific tasks with minimal human intervention. In this case, identifying URLs hosting pirated content. Machine learning can either be supervised or unsupervised.
With respect to fighting digital piracy, supervised machine learning is a better choice as it involves training the machine using data that categorizes URLs as legitimate or illegitimate. It can also be trained to identify piracy hotspots and hosting sites with the highest incidence of piracy and the most likelihood of hosting infringed content.
By crawling the web, the monitoring and detection team effectively compiles an exhaustive database of URLs. Applying various machine learning algorithms, customized to each content type, the URLs are categorized either as legitimate, or illegitimate URLs hosting pirated content. This process typically eliminates approximately 94% of the URLs, and it is usually the remaining 6% URLs that host pirated content.
Since this is a reiterative process, the system progressively becomes efficient and accurate with each reiteration. The anti piracy efforts get laser focussed on eliminating this narrow subset (6% URLs that host pirated content) thus greatly enhancing the effectiveness of anti piracy efforts in terms of speed, time, and eradication rate.
Checks and Balances & How it Improves Machine Learning Efficiency
Even as the machine learning tool identifies an URL as an illegitimate URL hosting infringed content, anti-piracy analysts manually cross-check the identified websites before authenticating whether the URL has been correctly identified.
This process, in turn, helps further finetune the machine learning systems. These human decisions are used to train the system/machine, which when confronted with similar situations in the future will be better able to deal with the query. A machine learning technique called reinforcement learning in which the machine learns from continuous and relentless trial-and-error is used.
Machine Learning Could Deliver a Decisive Blow on Digital Piracy
- Machine-learning technologies that are applied to investigate high-value and high-profile cyber attacks can be used to detect pirate websites, find their owners, and remove illegal content.
- Classifying traffic by using IP flow data from an IPFIX/NetFlow feed is an option that seems to have a lot of promise.
- Machine learning can exploit weaknesses in the piracy ecosystem where most providers are using a client/server software pair from specific providers, and these can be targeted.
- Use IP flow data to detect pirated linear streaming traffic on broadband networks.
Do you think that Machine-Learning Based Piracy Detection system could give digital piracy a final decisive blow, or do you believe that the pirates have a lot more “up their sleeve”?
Let us know in the comments down below.