How Big Data Helps Avoid Cybersecurity Threats

Cybercrime instances seem to be breeding like rabbits. According to security software maker Malwarebytes, its users reported 1 billion malware-based incidents from June to November 2016.

That was two years ago. Just picture this figure in 2018. Malware attacks have become more sophisticated and more difficult to detect and fight. Keeping precious business data protected against malware and hacking is one of the biggest challenges facing modern businesses. These never-ending cybersecurity threats make it extremely difficult to sustain business performance and growth.

Big Data: a savior?

Some say Big Data is a threat; others declare it a savior. Big Data can store large amounts of data and help analysts examine, observe, and detect irregularities within a network. That makes Big Data analytics an appealing idea to help escape cybercrimes.

The security-related information available from Big Data reduces the time required to detect and resolve an issue, allowing cyber analysts to predict and avoid the possibilities of intrusion and invasion.

According to a CSO Online report, 84% of business use Big Data to help block these attacks. They also reported a decent decline in security breaches after introducing Big Data analytics into their operations.

Insights from Big Data analytics tools can be used to detect cybersecurity threats, including malware/ransomware attacks, compromised and weak devices, and malicious insider programs. This is where Big Data analytics looks most promising in improving cybersecurity.

However, is it really possible to stay protected on an everyday basis?

The respondents in the CSO Online survey accept that they can’t use the power of Big Data analytics to its full potential for several reasons, such as the overwhelming volume of data; lack of the right tools, systems, and experts; and obsolete data. Big Data doesn’t provide rock-solid security due to poor mining and the absence of experts who know how to use analytics trends to fix gaps.

Intelligent risk management

To improve your cybersecurity efforts, your tools must be backed by intelligent risk-management insights that Big Data experts can easily interpret. The key purpose of using these automation tools should be to make the data available to analysts more easily and quickly. This approach will allow your experts to source, categorize, and handle security threats without delay.

Threat visualization

Big Data analytics programs can help you foresee the class and intensity of cybersecurity threats. You can weigh the complexity of a possible attack by evaluating data sources and patterns. These tools also allow you to use current and historical data to get statistical understandings of which trends are acceptable and which are not.

Predictive models

Intelligent Big Data analytics enables experts to build a predictive model that can issue an alert as soon as it sees an entry point for a cybersecurity attack. Machine learning and artificial intelligence can play a major role in developing such a mechanism. Analytics-based solutions enable you to predict and gear up for possible events in your process.

Stay secure and ahead of hackers with penetration testing

Infrastructure penetration testing will give you insight for your business database and process and help keep hackers at bay. Penetration testing is a simulated malware attack against your computer systems and network to check for exploitable vulnerabilities. It is like a mock-drill exercise to check the capabilities of your process and existing analytics solutions. Penetration testing has become an essential step to protect IT infrastructure and business data.

Penetration testing involves five stages:

1. Planning and reconnaissance
2. Scanning
3. Gaining access
4. Maintaining access
5. Analysis and Web application firewall (WAF) configuration

The results shown by a penetration test exercise can be used to enhance the fortification of a process by improving WAF security policies.

Once you have configured your policies and strengthened your process, you can do a new penetration test to gauge the effectiveness of your preventive measures.

Sometimes vulnerabilities in an infrastructure are right in front of the analysts and property owners and still manage to go unnoticed. Operating systems, services and application flaws, improper configurations, and risky end-user behavior are some of the most common places where cybersecurity vulnerabilities exist.

Bottom line

Big Data analytics solutions, backed by machine learning and artificial intelligence, give hope that businesses and processes can be kept secure in the face of a cybersecurity breach and hacking. Employing the power of Big Data, you can improve your data-management techniques and cyberthreat-detection mechanisms.

Monitoring and improving your approach can bulletproof your business. Periodic penetration tests can help ensure that your analytics program is working perfectly and efficiently.

_{https://www.digitalistmag.com/cio-knowledge/2018/08/27/how-big-data-helps-avoid-cybersecurity-threats-06184059/}

CHALLENGES OF BIG DATA IN CYBERSECURITY

With some of the biggest data breaches in history having taken place in 2019 alone, it’s clear that cyber-attacks aren’t going to disappear any time soon. From the Capital One banking data breach affecting over 100 million customers to the major breach of Flipboard potentially affecting over 1 billion users, the rise in cyber-attacks is hugely concerning for both businesses and individuals alike.

Alongside this, the amount of personal information processed by companies across the globe has increased considerably over time. Because of this, cybersecurity practices have had to become more advanced than ever before, and new methods of processing such large volumes of data needed to be introduced.

This is where the use of big data has the potential to be incredibly useful, as it can not only help to block any potential cyber-attacks but also helps to analyze huge amounts of data far easier than was previously possible. Despite this, there is still a long way to go before big data and cybersecurity can coexist in harmony. To find out why, continue reading.

What is big data?

Put simply, big data can be defined as a huge amount of structured and unstructured information that because of its large size, cannot be processed using traditional database and software techniques. It has become increasingly utilized by companies as a way of discovering patterns and trends in behaviour, while it also allows for advanced threat detection and machine learning.

According to Forbes, the big data analytics market was worth an estimated $203 billion back in 2017. As companies look to adequately protect themselves against the growing threat of cybercrime and handle ever-growing volumes of data, the value of the market will undoubtedly increase considerably as the years go by.

However, the rise in big data usage hasn’t gone unnoticed by online criminals, as many hackers have now made companies using big data their prime target. The growing number of data breaches to occur in recent years is a clear marker of the vulnerabilities of big data. But, just what exactly are the challenges of big data in cybersecurity? In the next section, we’ll delve deeper into exactly that.

What challenges do cybersecurity experts face?

Sustaining the growth and performance of business while simultaneously protecting sensitive information has become increasingly difficult thanks to the continual rise of cybersecurity threats. Therefore, it’s clear that preventing data breaches is one of the biggest challenges of big data in cybersecurity.

On a daily basis, countless sensitive records are processed by businesses across the globe. If this information was to end up in the wrong hands, the consequences could potentially be disastrous, as has been evident in previous data breaches including the ones mentioned earlier.

Traditional preventative security tools and technologies used for data mining purposes and for the prevention of cyber-attacks simply aren’t sufficient enough for many businesses – particularly ones which handle such large volumes of data. This is why big data analytics have become increasingly utilized by cybersecurity personnel.

However, another challenge faced by businesses is that the staff in charge of data analysis often don’t have the required knowledge to respond efficiently to any potential threats which may arise. But, as artificial intelligence (AI) and machine learning continue to be implemented and knowledge and awareness of big data increases, this will hopefully become less of an issue for businesses in future years.

Big data – is it a threat or a blessing?

Optimizing cybersecurity should be high up on the priority list of any business in the digital age, as not having the correct tools could possibly spell a catastrophe. This is why big data technology is becoming used by more and more businesses around the world, as it helps them to fend off any potential attacks from hackers prior to them actually happening.

However, unless the focus is placed on achieving optimal cybersecurity in big data, hackers could potentially gain easy unauthorized access to information processed using big data technologies. Therefore, it’s clear that big data has its advantages and disadvantages.

Conclusion

Overall, it would be appropriate to suggest that big data analysis can be hugely beneficial for companies through the detection of growth and performance insights which can help to drive a business forward. Big data is also arguably the best way forward when it comes to cybersecurity, as detecting threats at the earliest possible opportunity is now easier than ever before.

Big data undoubtedly has its plus points for any business needing to process large volumes of data on a regular basis. But, in spite of this, the increasingly sophisticated techniques utilized by cybercriminals are becoming ever-more difficult to combat. Taking this into account, it’s safe to say that optimal cybersecurity practices need to be put in place by businesses. Otherwise, instances of large data breaches will only continue to take their toll.

Article by Olivia Scott, Marketing Manager @ VPNpro.com

How To Fix Can Not Connect To MySQL Server With Root User Error.

To manage the XAMPP MySQL server, you can use MySQL workbench. If you use root user with empty password ( the root user’s password is empty by default ), it will popup error message Failed to Connect to MySQL at 127.0.0.1:3306 with user root. This error is because the MySQL server’s Hostname is wrong, the MySQL server Hostname should be the running XAMPP server’s ip address, which is 192.168.64.2 in this example.

But when you use XAMPP linux server’s ip 192.168.64.2 as the Hostname and use root user with empty password, it will popup another error dialog which said Failed to Connect to MySQL at 192.168.64.2:3306 with user root, Access denied for user ‘root’@’192.168.64.1’ (using password: NO). You can read article How To Resolve Access Denied For User ‘root’@’localhost’ (using Password: Yes) When Connect MySQL Database to fix this, but we had also list the short steps about how to fix it as below.

To fix this error, you should click the Open Terminal button in XAMPP manager window, then follow below steps to resolve it.

1. Connect to mysql server in command line with root user, the default root user’s password is empty.root@debian:/# mysql -u root -pEnter password: Welcome to the MariaDB monitor. Commands end with ; or \g.Your MariaDB connection id is 9Server version: 10.3.16-MariaDB Source distributionCopyright (c) 2000, 2018, Oracle, MariaDB Corporation Ab and others.Type ‘help;’ or ‘\h’ for help. Type ‘\c’ to clear the current input statement.MariaDB [(none)]>
2. Select all host and user name from mysql.user table use select sql command like below. We can see that the root user can only connect to the embedded mysql server on localhost, and the localhost is the XAMPP linux server, but MySQL workbench is running on the Mac OS. So you should grant root user access permission to the MySQL server from any machine with ‘%’ as the host value.MariaDB [(none)]> select host, user from mysql.user;+———–+——-+| host | user |+———–+——-+| 127.0.0.1 | root || ::1 | root || localhost | || localhost | pma || localhost | root |+———–+——-+6 rows in set (0.001 sec)
3. But generally, allow root user access MySQL server from any machine is not safety, so we had better create another MySQL user account and make it to connect to MySQL server from any machine remotely.
4. Create a new MySQL server user account with provided username and password use MySQL CREATE USER command. Please note @’%’ means jerry can connect to MySQL server remotely from any machine.CREATE USER ‘jerry’@’%’ IDENTIFIED BY ‘jerry’;
5. Grant all privileges to user account ‘jerry’@’%’.GRANT ALL PRIVILEGES ON *.* TO ‘jerry’@’%’ WITH GRANT OPTION;
6. Now use above newly created user account to connect to MySQL server. Please note the Hostname should be the MySQL server ip address ( displayed in XAMPP manager window top area ).
7. When you connect to MySQL server successfully, you will get below successful message dialog.
   

Source : https://www.dev2qa.com/how-to-connect-to-mysql-server-after-install-xampp-on-mac-os/

Sentry vs Ranger

Comparison between Apache Sentry and Apache Ranger based on features offered by them:

Feature	Apache Sentry	Apache Ranger
Role-Based Access Control [RBAC]	Yes	Yes
Deny Support	No	Yes
Admin Web User Interface	No	Yes
REST API Support	No	Yes
CLI Support	Yes	No
Audits Support	No	Yes
Plugins Supported	Impala, Hive, HDFS, Solr, Kafka	Impala, Hive, HDFS, Solr, Kafka, HBase, Knox, Yarn, Storm, etc
Tag-based policy	No	Yes
Row Level Filtering	No	Yes
Column Masking	No	Yes
HDFS ACL Sync	Yes	No [Will be supported in upcoming CDP releases]

As we can see Apache Ranger supports more features like tag-based policy, row-level filtering, column masking, audits, admin web interface, more services, or plugins in CDP stack, and that’s why its the default choice for the authorization service in CDP.

For more detailed comparison see this article by @EricL 

https://www.ericlin.me/2020/01/introduction-to-apache-ranger-part-i/

Penjurian Bonsai

Kriteria penjurian bersumber dari buku Panduan Penjurian dalam Nominasi Bonsai , berikut ini dijelaskan unsur-unsur yang termasuk dalam kriteria penilaian antara lain:

A. PENAMPILAN

1. Keseimbangan Optik

Keseimbangan optik lebih mengutamakan atau menitikberatkan pada pengolahan rasa dan hal-hal yang tersirat.

2. Realitas Alam

Materi yang berkaitan dengan proses alam,  diolah dengan cermat dan tidak meninggalkan bekas rekayasa campur tangan manusia

3. Penjiwaan (Pesan dan Kesan)

Menampilkan sebuah karya yang berkarakter dengan kekuatan garis tersendiri atau memiliki ciri khas.

B. GERAK DASAR AKAR DAN BATANG

1. Gaya

Menilai bonsai menurut gaya yang sesuai dengan kriterianya.

2. Karakter

Setiap jenis tanaman memiliki karakter yang berbeda satu sama lainnya, hal ini terlihat dari ciri fisik anatominya.

3. Alur Gerak

Alur gerak yang terdapat di seluruh anatomi, mulai dari akar hingga mahkota, juga keharmonisan bagi tanaman yang berbatang lebih dari satu.

C. KESERASIAN

1. Kesehatan

Tanaman tampak sehat menurut ilmu pertanian secara ilmiah.

2. Peletakan Wadah/ Pot

a. Perspektif 

yaitu fenomena jarak pandang terhadap suatu objek.

b. Proporsi

Yaitu tata ukuran besar, bentuk, dan peletakan dalam pot

c. Harmoni

Yaitu tata keindahan hasil penggabungan dari beberapa komponen menjadi satu kesatuan.

3. Kesan Tua

Penampilan karakter  dari tekstur kulit, atau kayu disertai struktur anatomi, sesuai rentang perjalanan hidupnya dengan warna alami.

D. KEMATANGAN CABANG, RANTING DAN DAUN

1. Tahapan

Tahapan perjalanan hidup bonsai antara lain:

a. Tahapan Bayi (akar dan batang)

b. Tahapan Anak (akar, batang, dan cabang)

c. Tahapan Remaja (akar, batang, cabang, dan ranting)

d. Tahapan Dewasa (akar, batang, cabang, ranting, dan anak ranting)

e. Tahapan Tua (akar, batang, cabang, ranting, anak ranting, cucu ranting, dst)

2. Keseimbangan Anatomi

Semakin tua bonsainya, maka ukuran diameter dan keberadaan anatominya semakin seimbang dan bertambah lengkap

3. Dimensi

Yaitu ukuran ruang bonsai berupa karya tiga dimensi yang menempati ruang dalam tiga orientasi.

4. Komposisi

Tata letak yang membentuk kesatuan yang harmonis, termasuk ukuran besarnya.

Demikian ulasan yang dikutip dari Buku Panduan Penjurian dalam nominasi Bonsai tentang unsur-unsur yang menjadi kriteria penilaian, semoga berman