Fusion Predictive Analysis and WAF Models for First Level Security

Introduction

Keeping your data secure is a multi-step process. To ensure that your data is secure, you must use predictive analytics models and a firewall.

Predictive analytics is changing every facet of the business world. While most of the emphasis has been on identifying market opportunities, predictive analytics is also essential for increasing safety. The new models of predictive analytics are merging with various security solutions to strengthen cyber security.

Predictive analytics has helped improve security in a number of ways. These solutions include:

• Better identify threat agents from different regions where cyberattacks are known to originate

• Evaluate the evolution of cyber attacks

• Help security experts identify defenses that will be most effective against new generations of cyber attacks

NIST Computer Security documented many of these findings in The Cyber Risk Predictive Analytics Project. The authors discussed an approach adopted to use predictive analytics in their security plans:

"We use the negative binomial panel regression technique.This is the appropriate multiple regression technique based on the distribution of a dependent variable with the following characteristics: a distorted distribution and a heavily weighted counting variable with zeros. (eg, bad access violations, nonconformities, etc.), as well as the lack of compliance with the rules and regulations, techniques, robbery violations and behavioral vulnerability violations) The independent variables used included the following: the respondent's response to each of the questions and a set of control variables (year, industry, and company). Of control are year, industry, enterprise. "

You may have heard about web application firewalls recently but you have no idea what exactly they are used to. Do companies really need to implement this system? Keep reading to find out if it is necessary for you and your electronic business.

What is a WAF?

A firewall is a security system that controls and controls incoming and outgoing data traffic that is focused on security policies. Basically, it creates a barrier between a trusted internal network and an untrusted external network. Meanwhile, a WAF (Web Application Firewall) is an application firewall designed for HTTP applications. You can block, filter, and monitor network traffic to and from any application.

We can say that the WAF ensures that you get high quality desired results with minor risks. This is accomplished by creating a shield between the Internet and the web application. By monitoring and inspecting network traffic, you get an additional layer of security for your network. It also helps prevent common Web attacks, which often result from security breaches.

Do you need a WAF?   

Let's look at the benefits of having a WAF in your networks. After reading all the advantages, you can decide whether or not you need one for yourself. The following are the key benefits of having a WAF in place:

1. Protection of private data

WAFs operate 24 hours a day to protect the system against unauthorized data exposure. For an online business, WAFs ensure that private user data is stored securely. A lack of cybersecurity makes customer information vulnerable to hackers and this can cost you the reputation and trust of the customer. A Web attack can damage your business and a WAF can prevent this from happening.

WAFs proactively protect your sites and applications from data theft and fraud, stopping any suspicious activity. It inspects all web requests, from SQL injection and routing to site scripts. This ensures that your customer's data remains secure and secure.

2. Automate the patches

In cases where you discover a vulnerability on your site, you should take immediate action to prevent escalation of these issues. Some companies may have the resources to fix the application or even solve the problem quickly, although not all have the same features. However, some WAFs come with a convenient solution as they have the ability to temporarily fix the site for immediate protection before seeking a long-term solution.

3. Prevents data leakage

Hackers and their strategies have evolved with technological developments and can collect crucial data more easily. This means that it can be difficult to capture them before they completely compromise your website. Obviously, any kind of filtering can become a disaster for you and your customers. A WAF checks each request in its Web applications and prevents any unusual information from leaving the network. This causes your application or website to hack the proof!

Predictive analytics and WAF is the key to better cybersecurity

Predictive analytics is changing the future of cybersecurity. He is helping cybersecurity experts conduct better threat assessments and have the right defences in place. However, it is only effective if combined with the correct defences. WAF technology is helping to minimize the risk of data breaches by 2018.

From the above benefits, it should be easy to gauge whether you and your company really need a WAF. If you ask us, web application firewalls should be in place if companies really value their customers and their business. Business owners will benefit greatly when they ensure the security of their sites. Plus, their implementation will give them peace of mind and more time to focus on running their business! Predictive analytics is changing the future of cybersecurity. He is helping cybersecurity experts conduct better threat assessments and have the right defences in place. However, it is only effective if combined with the correct defences. WAF technology is helping to minimize the risk of data breaches by 2018.

From the above benefits, it should be easy to gauge whether you and your company really need a WAF. If you ask us, web application firewalls should be in place if companies really value their customers and their business. Business owners will benefit greatly when they ensure the security of their sites. Plus, their implementation will give them peace of mind and more time to focus on running their business!

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Why organizations today want an Analytical Platform

Introduction

With the incipient stage of data revolution beyond us, organizations are entering a new level of expertise in data management by experts. The days when organizations could manipulate data and extract information from them, without the presence of an analysis platform, are gone.

The importance of business analytics platforms has grown over time, to the point that they are considered imperative to store and analyze data today.

As a member of the SAS Collaborator program, I had the luxury of receiving information from an expanded SAS survey that assesses the likelihood that organizations will opt for analytical platforms and the motivating factors behind this strategy. The investigation consisted of two parts, the first of which included interviews with more than 132 government and business organizations.

The second part consisted of a global online survey that attracted 477 respondents, all from a qualified fund. The results were very interesting and validated the importance of the analysis in the current organizations. Over 80% of all subjects said that the topic of analysis hit the boardroom within their organization.

added value

To begin, we asked respondents about the value that the reviews had added to their business model and efficiency. Seventy-two percent of all respondents said their use of analytics helped them add value. This value was added through a visible improvement in key business operations. Some organizations also argued that the analysis helped them launch complete business models.

To make a better assessment of the value obtained through the analysis, we asked the respondents exactly what they used the data and analysis in their organization. A convincing 98% of all respondents believed that analysis played a role in their organization. Its deployment, however, varied from case to case. When asked about the role of analysis in their system, 39% of respondents believed that the analyzes were used to make tactical and strategic decisions throughout the organization. 

They closely followed the 35 percent who believed that the analyzes were used throughout the organization, but only for tactical problems, not for strategic purposes. On the other hand, only 7% mentioned that the analysis was used only to make strategic decisions, while only 2% confessed that they never used the analysis in the organization.

Challenges

We have all talked and read/written articles about the challenges that persist in implementing and analyzing widespread data across organizations. Not all companies have the talent or the government to use the data. Data management remains a struggle for most organizations, so some make the final decision to avoid scaling on a larger scale.

We asked many of our online interviewees and physical respondents to indicate their confidence in four data-related issues. The process of reflection was to determine the number of people they trusted to assess challenges and recognize the dangers they posed. The issues were trust, accessibility, exchange and integration.

Interestingly, respondents had less confidence in the integration provided by the analysis. Respondents believed that data is generally saved and should be better integrated at all levels. The level of trust was relatively higher in relation to trust systems since confidence levels reached a score of 3.68 in 5. Respondents also showed scepticism in assessing accessibility and exchange, since both issues were rated at 3.22 and 3.02 respectively.

Benefits of the Platform.

Having discussed the implications and the challenges, we could see the benefits of using an analytical platform to get actionable information, as per our respondents' perceptions. We provide a list of possible benefits for all respondents and ask them to rate the five preferred benefits they expected or had experienced on an analysis platform in their organization. Through all the benefits, the preferred response was to reduce the time spent cleaning and preparing the data. More than 46% of respondents believed that an analysis platform helped them reduce time spent on data cleansing.

In addition, many of the respondents believed that an analysis platform improved decision making and also reduced the time spent doing so. The analysis platforms have helped data scientists, in particular, in all organizations, and have increased access to various data sources and hardware. They also improve integration by implementing models that can be accessed from any environment, through an API.

In addition, many of the respondents also believed that the analysis platforms have reduced the effort that was taken to generate ideas outside the system. Ideas are now generated faster than before. "The results from the analytical platform are also used at the board level. The main benefit for them is that they now receive the requested information much faster; the information is produced in a day now instead of 30 days before, "said a senior professional in the in-depth interview.

Enabling teamwork and general confidence

The team that deals with the analysis platforms in an organization is usually made up of data scientists, IT decision makers and business sponsors. We asked some of these team leaders to evaluate some issues related to your company's ability to obtain analysis value and rate your confidence on a scale of 0 to 10.

These leaders of the strategic teams showed a considerable degree of confidence here. The greatest confidence was in the success in the field of analytical / data science and statistics, as it was rated 7.2 by experts. The total confidence rate was 7.0; which is pretty decent, considering the limited time that the analysis platforms have had.

"We still have some problems, the deployment of the model is the biggest, but also, data accessibility or IT security remain a challenge," said the head of analysis of a public sector firm.

Knowing that the workload of analytics is bound to increase in the future, we asked our respondents how prepared their organizations were for the future. The rating system was the same as the previous one, and respondents requested ratings from 0 to 10, with 0 being least prepared and 10 exceptionally well prepared. The total average for this case was 6.3, which means that there is still room to improve what they can do and how they will work in the future.

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How Fraud Analytics Can Protect the Banking Industry

Introduction

Business transaction data is an important part of any bank's operation and is managed and stored by IT systems. Business processes are compatible with these IT systems. With these IT systems in pace, the level of human interaction has been drastically reduced. This, in turn, has become a major reason for the occurrence of fraud. Automated controls are used to keep fraud under control.

Identifying an expected fraud or actual fraud in an organization is what fraud detection is. Solid systems and processes must be implemented to prevent fraud or detect them at an early stage. Proactive or reactive methods, as well as manual or automated methods, are used to detect fraud.

Why is fraud detection important to a bank?

With the aim of discovering new types of fraud as well as traditional frauds, all banks have begun to realize the importance of fraud detection. A specialist swindler will avoid even the most sophisticated fraud detection techniques. Therefore, robust fraud detection techniques should always be in place.

Google Analytics Fraud Monitoring

Bank transactional data is now easily accessible from internal and external sources. This requires the use of advanced analysis in fraud detection programs. Analyzing fraud data allows you to detect fraud before it occurs.

Fraud Analysis - The Significance

The use of advanced fraud analysis and analysis techniques combined with human interaction to help detect heterodox transactions is called fraud analysis.

Fraud Analysis - The Importance

Many banks are now using multiple rule-based methods and different anomaly detection methods. However, these have their own limitations and are not as powerful. The fraud detection features are enhanced with the influx of analysis and a new dimension of fraud detection techniques can be seen. Along with this, you can measure performance that helps you standardize and maintain control for constant improvement with fraud analysis.

Fraud Analysis - Advantages

Recognition of hidden patterns

Fraud analysis helps identify scenarios, new trends and patterns under which fraud occurs. Traditional methods ignore these aspects.

Data integration

Fraud analysis combines data and combines data from various sources, including public records, and integrates them into a model.

Improve existing efforts

It is not a substitute for methods based on existing rules, but, in fact, it is an accumulation of traditional methods.

Use of unstructured data

Achieving value from unstructured data is an analysis of scams and unexplored gold mines that helps you achieve it. In most organizations, structured data is stored in data warehouses. Unstructured data is the area where there is a high probability of fraudulent activity. This is where text analysis plays a very important role in reviewing such data and preventing fraud.

Data analysis - the process

Steps to Create Your Fraud Program:

• A profile should be created that contains all areas where fraud can occur and the different types of fraud that may occur.

• Use the ad hoc test method to find fraud indicators in different areas of the organization.

• Use a risk assessment matrix and decide which areas need more attention.

• All activity must be monitored and communicated to everyone in the organization so that knowledge about the events in the organization is maintained.

• If fraud is detected, inform management and resolve the problem immediately. Find out why the problem occurred and check for prevention mechanisms.

• Broken controls should be repaired.

• Clearly separate tasks

• Repetition of the process and expansion on a larger scale.

Bank cheats and corrective measures

The banking sector is the most susceptible to fraud. Fraudulent activities can be internal or external. Due to being a highly regulated industry, banks have to adhere to many external compliance requirements, and along with that, they must have their fraud detection measures on guard all the time.

Schemes of fraud related to banks

These are some examples of fraud that occur in the banking sector:

• Corruption

• Cash fraud

• Billing Fraud

• Check fraud against counterfeiting

• Skimming

• Furto

• Financial Statement Fraud

Most common bank cheats on the Internet:

Cloning sites

A situation in which a cluster is cloned or only the pages from which the requests are placed.

False merchant sites

These are sites where customers receive a service at a very low price. A site like this requests the details of a customer's credit card to access the content of the site.

Credit card generators

These are valid credit card numbers generated by computer programs. A list of credit card account numbers is generated from the same account.

Fraud Analysis - Methods and Best Practices

sampling

It is a key element and is mandatory for certain processes in the detection of fraud. When the data population is high, sampling is the most effective. All control of fraud detection may not be possible because it only takes data from some population sources. However, sampling still has its own advantages. Fraudulent transactions are not random, and banks must test all transactions to effectively detect fraud

Ad hoc

Ad-Hoc is discovering fraudulent transactions using a hypothesis. Transactions can be tested and fraud opportunities can be exploited.

Repetitive analysis

This method depends on the configuration of scripts that are executed in connection with large data values in order to identify frauds as they occur in a given period. These scripts must be run every day to help pass through all transactions and receive notifications about fraud. The fraud detection process will be extremely efficient and consistent with this method.

Statistical analysis techniques

This method will help you go beyond finding common frauds and help detect abnormal ones:

• Statistical perimeters must be calculated to discover the values that show more than the averages of the standard deviation.

• The anomalies should be detected based on the high and low values in the perimeter. These anomalies are the indicators of fraud.

• The classification of the data is very important since transactional anomalies can be detected based on geography, on the timeline, etc.

Benford's Law

Benford's law is a law of anomalous numbers. It is often referred to as the first digit law. It is commonly used as an indicator of fraudulent data. The distribution used here is not uniform. This means that smaller digits are more likely than larger digits.

The patterns will appear and the transactional numbers will be tested. When the numbers that should not appear so frequently begin to appear, these are the main suspects.

Here are some other data mining tools that can be used to detect fraud

• Data Correspondence - If any data exactly match other data, this method will be performed for you. Duplicate transactions can be avoided with the use of data correspondence.

• It seems that - Valid name variations of company employees always exist and this method helps to combat this problem.

• Lagoons - This method allows you to discover missing sequential data. Purchase orders in a sequence are a great example of this. The missing data can be found when the POs are not in sequence.

conclusion

Fraud in the banking sector is always increasing. Technological advances open new paths for scammers. Advanced statistical analysis, machine learning and predictive analysis are some of the ways in which banks detect fraud and keep it to a minimum.

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