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An Introduction To Big Data Security
Do you know that nightmare of every network is security? No matter how much security they claim to be crooked internet minds will find one way or other to break into the network causing lots of damages ranging from a data breach to a complete server crash. As more companies started to adopt big data those crooked internet minds, or sometimes referred to as cybercriminals, get more opportunities.
But what is really wrong with big data? Let’s explore the answer to this question together.
What Is Big Data Security?
As we have seen in our previous articles, that the process of data analytics includes multiple steps ranging from data collection to reporting the insights made from data to respected authorities. Big Data Security refers to the process of adding security to the data we have. It includes various security measures taken, usage of security tools, and every other step that will protect the data from being stolen by before mentioned entities.
If you are a regular learner of Edu data online you might have read another beautiful article where we have discussed popular big data analytics tools. As we can see almost all the tools are free and are community-supported open-source projects. These tools were not designed with security in mind, which means Hadoop is expected to perform well only in a trusted environment. No one can be blamed since at the starting days privacy and security weren’t given any priority. But today if tight security is not given to the BIG data’s that these companies have accumulated over this time, it will lead to many huge problems which may sometimes be irreparable.
But before we understand the major challenges faced by Big Data companies it is mandatory to have a basic knowledge about how big data analysis works or to be more specific about how Hadoop works.
Basic Working Of Hadoop
Hadoop has 2 main software parts namely HDFS (Hadoop Distributed File System) and MapReduce, each of it individually handles two basic needs of the computation, that is Storage and Processing.
What is special about Hadoop was its distributed storage system, and it is done through HDFS technology. As we all know a large chunk of data is very hard to process all at once since it takes more time. To reduce the time required for computation what HDFS does is it will split the large pile of data into smaller blocks of data and distribute it between several systems (known as nodes) and these systems will be connected forming a cluster.
Now that we have solved one of the problems by using distributed storage system, it’s time to solve the next serious issue that is processing time. And each node will process the data individually thus reducing the workload of one single node and significantly reducing the time required for processing, this is done through parallel processing using MapReduce technology.
Suppose if you have 10 GigaBytes of unstructured data and you need to run some analysis on that data using Hadoop, Hadoop splits the 10 GB data into 10 parts with 1 GB each (This hugely depends on the number of nodes we have, by default each packet of data will be minimum of 128MB. But splitting data into such small size will result in more nodes being used. But Smaller the size faster will be the processing). Each 1GB of data is stored in one node and is processed in that node, thus the time taken for processing the whole 10GB data is equivalent to the processing time of 1GB of data by each node.
This will significantly reduce the time required for processing but will increase the cost of computation, but in today’s fast-developing world it’s worth noting that time matters more than money.
Importance Of Big Data Security
Security for data is undeniable, as we all know data is the most important asset for any organization leaving it unprotected will give rise to many problems like identity theft, financial loss, legal issue, etc. Some high-priority details like customer data medical data should be kept under high security without reaching the hand of hackers. Types of attacks range from DDoS, SQL injection, and ransomware, each of these attacks induces a different degree of damage to the systems.
Being attacked by cybercriminals will bring a bad reputation to the organization, in the past, we can see such real cases. As per a survey conducted by BI-Survey.com almost 90% of the participants said that Big Data Security plays a critical role in the organization. Many organizations believe that Big Data Security issues are a long-term issue and can be solved in the long run, but the reality is it can have short-term effects too. We might have read about data breaches happening in companies, all this happens because they are not following strict big data security measures.
Security often requires 100 percent participation from the user base, so if a few users follow lax security practices, the whole system could become at risk. Protecting data often entails protecting all aspects of your environment, including physical security. A common tactic is to fool authorized users into providing access. even if you think you have the right controls in place, there are always opportunities for human error, especially in complex environments.
Challenges And Solutions Of Big Data Security
Now let’s see some of the challenges that are faced by Big Data Security in today’s world, we will also briefly explain the solution for these challenges.
1) Security Issues With Distributed Enviornment
As we have seen Hadoop has a distributed environment instead of one centralized server. This distributed system raises security concerns about the nodes, even if one single node is vulnerable the whole network might get affected.
Trust has to be established between the nodes. Hadoop works better in an ecosystem where trust is already established.
2) End User Input Validation
Data collection for big data happens through both homogenous sources, which give structured data, and heterogeneous sources, which give unstructured data. But there is no system to verify the source of data, if the source is found to be vicious we can avoid further inputs from the source thus reducing damage.
Establishing authentication methods in the network, to ensure privacy de-identification has to be implemented.
3) NOSQL Database
NoSQL databases Collected from heterogeneous sources demand additional privacy and security policies. Validating all the data and filtering it is a very tedious process.
Big Data is growing at a high velocity, to ensure scalability and availability of data we require automation of data management. But these kinds of systems raise some kind of storage issues.
4) Misuse Of Data
Companies use Big Data To do marketing giving less consideration to user privacy, this intrusive marketing decreases domestic freedom and increases government and corporate control.
Establish better transparency, better security, and protection. Legal ways are best suited in this case, make personal data protection policies stronger.
5) Cryptographically Enforced Access Control And Secure Communication
Most sensitive private data is meant to be encrypted and should only be accessed by authorized personals.
This has to be made more efficient and scalable to implement fairness between distributed entities, cryptographically secured communication framework has to be implemented.
6) Granular Access Control
Access control security measures prevent access to data by people who are not supposed to access the data. Access controlled security measures are of many types like Grounded Access Control, Role-based/Rule-based Access Control. But all these systems have their own limitations and incompatibilities with Big Data needs.
A Granular Access Control is required to meet the needs. It is a practice of granting different levels of access to a particular resource to particular users.
7) Granular Audit
An attack may happen in different phases from reconnaissance scan to vulnerability attack, at real-time security monitoring we try to be notified with updates about the attack right when it’s happening. But in reality, this may or may not be the case.
A periodic Granular audit will help us solve this issue. This is not only relevant because we want to understand what happened and what went wrong, but also because of compliance, regulation, and forensics reasons.
These are some of the major challenges faced by Big Data Security in the present-day scenario. But Big Data is here to stay, it’s practically impossible to survive in the coming days without consuming data and producing new forms of data. So the before mentioned problems along with many others will be solved in the future by implementing better technologies and infrastructure.
Big Data Security Tools & Scope For Future Markets
To ensure Big Data Security many tools are widely being used, some of the frequently used Big Data Security tools are listed below.
- Apache Sentry
- HDFS encrypt
- Apache Falcon
As we have seen that the insecurity in the network can be caused by anything at any step, each tool is designed and is used for different purposes. Kerberos and LDAP are used to secure access to the cluster, Apache Sentry, Impala, Knox are used to define authorization for access to data, HDFS Encrypt, Vormetric, Gazzang are tools used for encryption and finally, apache falcon is used widely to audit and lineage. There are too many tools to be discussed at this point since the privacy concerns about big data security and its possibilities are least explored.
Acceptance of big data and the availability of cheap computing has given rise to many opportunities in this market. Shortly, the Big Data Security market is going to skyrocket and reach a billion-dollar market cap as no business or organization could survive without adopting this outstanding technology.
Big Data V/S Cybersecurity
The underlying ideologies of these two concepts are contradicting to each other. Data science’s key objective is to extract valuable insight by processing big data into specialized and more structured data sets. While cybersecurity protects and secures big data pools and networks from unauthorized access. There is a contradiction and many analysts argue on the topic that Big Data and Cybersecurity could not co-exist. Let’s see some further insights into this case.
Cybersecurity is the practice of protecting electronic data systems from criminal or unauthorized behavior. Working in cybersecurity is well suited to curious individuals, have a strong desire to learn, and enjoy creative problem-solving.
On the other hand data scientist have a more abstract role as their work isn’t purely focused on analytics or engineering, rather it is a multidisciplinary position that comprises a mix of collecting, extracting, and analyzing large amounts of big data from multiple sources. The area requires an understanding of artificial intelligence and machine learning techniques such as support vector machines, regression, cluster analysis, and neural networks.
At some times Big Data and data science are made use of in cybersecurity to find possibilities of future threats. Together data science and cybersecurity will enhance user security and privacy in the future.
How You Can Implement Big Data Security In Your Organization?
There 5 simple steps you can do right now to ensure Big Data Security in your firm, they are
Secure Distributed Computing Frameworks Implement better authentication methods to establish trust between different decentralized nodes. de-identification must be implanted to ensure privacy constraints are met. Then, organizations must validate access to files and ensure that sensitive data is not leaked by any means.
Secure Data Storage Data must be stored in a secure way to enhance Big Data Security. To monitor unauthorized alterations from third-party agents SUNDR( Secure Untrusted Data Repository) must be employed.
Protect Your Data To secure your data, organizations must use firewall security, intrusion detection, and prevention tools, scanning tools, and demand validation for all access to data.
No Audits Should Be Skipped organizations must conduct a complete audit to check whether operations are working fine. Tools like Apache Oozie can be made use to find potential Big Data Security Threats.
Hardware And Software Configurations Must Be Secured Data loss in an organization can happen in many ways. It could be due to the failure of servers, data breaches, or software failure. The most common of them are hardware and software fails so it’s very important to have a backup of every data so that no data is lost.
In Hadoop, as we have seen all the nodes are used to store data, you may think the failure of one node may result in loss of data But Hadoop uses a replication system to store the same data in a minimum of 3 nodes thus if anyone node gets faulty the data can be retrieved from other nodes which store a replica of the lost data.
If you follow these 5 steps strictly Big Data Security in your organization will stand compromised with market demand.
Here we have discussed what is Big Data Security, its major challenges, tools used to solve Big Data Security issues, and finally, the difference between Big Data Security and Cybersecurity and at last we have seen 5 steps to practically implement Big Data Security in your organization. Hope this article was informative, if it was kindly share this knowledge with your friends.
Why big data is not secure?
Big Data Security is a huge concern because data can be used for both good and bad things, it largely depends on who is using the data. The primary problem is with Hadoop, as it is not designed with privacy and security in mind. big data is not secure because due to limitations in present-day technology it’s very hard and complex to handle such a variety of data with high volume and velocity.
Which is better big data or cybersecurity?
Big data and cybersecurity are entirely different domains. One deals with the collection of data and processing of the same while the other prevents third persons from collecting data and thus ensuring privacy and security. Sometimes big data is used in cybersecurity to enhance privacy and security.
How does custom security software prevent big data breaches?
Hadoop by default has many security flaws, to solve these issues custom software and told are made use in Big Data Security. Some Of them are Kerberos, LDAP, Apache Sentry, Impala. Using these tools will add an extra layer of security to the data being stored. Some of the tools may use cryptographic methods to ensure data security.
What jobs can I get with cybersecurity in the big data system?
Sometimes big data is made use in cybersecurity to predict attacks, which opens a wide window for security engineers into this field. Some of the most common job titles in the Big Data Security and cybersecurity space are cybersecurity engineer, information security engineer, cybersecurity architect, security consultant. etc
What industries and organizations are working to boost their big data security analytics?
Big data security analytics and audits must be done by all industries and organizations. CSA( Cloud Security Alliance) is the worlds leading nonprofit organization that is doing research and surveys on cloud computing and big data space to make it better in the future.
Is data mining of any importance in cybersecurity?
Data mining has proved to be a useful tool in cybersecurity solutions for discovering vulnerabilities and gathering indicators for baselining. There are mainly 4 methods used for the detection of malware, they are scanning, activity monitoring, and integrity checking, and data mining. Data mining is used to improve the speed and quality of malware detection.
How can data-centric security protect data lakes and safeguard innovation?
Current models of security use middleware or APIs to secure data and control access. Data-centric security means that without any intermediaries to prove the authenticity of the data, the data itself will prove its authenticity to the end-user. A much familiar example is blockchain. Data-centric security will eliminate the need for any middle person to validate the data.