Using Big Data to Defend Against Cyber Threats Essay Example

cyber-attacks. An important approach to securing big data is the implementation of access control policies in big data stores.

Keywords:

Network Protection, Analytics, and Analysis

Introduction

Big data is crucial for technological progress as it enables companies to enhance their services through analysis of user data and algorithm development. Instagram, a widely-used social media platform, demonstrates the utilization of big data. The app's explore page is tailored to each user by taking into account their likes, comments, and shared photos with followers. This personalized feature not only improves user satisfaction but also increases engagement and generates greater revenue.

The use of big data is expanding to various industries, including financial services, health services, weather, politics, sports, science and research, automobiles, real estate, and now cyber security. Setting up a big data analysis program is an effective way to monitor your network. Big data analysis involves examining large data sets to reveal hidden patterns, unknown correlations, market trends, customer preferences, and other valuable business information. In terms of preventing cyber-attacks, understanding the data flow on your network can be crucial. By tracking everything that enters the network, you can decide whether it poses a potential threat or not.

This study will demonstrate how easily unfamiliar data in your network can be identified and its path traced. Big data refers to high-volume, high-velocity, and high-variety information assets. It is collected from sources such as social networking, cell phones, and interactive web applications. Every minute, billions of bytes of data are gathered through various mediums. To gain better understanding and facilitate decision-making, efficient and inventive means of processing information are essential for big data.

Dealing with big data always

poses challenges in terms of storage and processing. Storing, managing, and analyzing large datasets can result in security and privacy issues. Privacy and security are compromised during the storage, management, and analysis of these extensive quantities of data.

To address these concerns, it is crucial to maintain a balanced approach towards regulations and analytics when handling big data. Data management techniques and analytical methods can be employed to solve security problems. (http://ceur-ws.org/Vol-1097/STIDS2013_P1_JanssenGrady.pdf)

The collection of vast quantities of data requires thorough analysis in order to protect against cyber-attacks. Security and privacy concerns surround the management and analytics of big data. Big Data plays a pivotal role in database management, with numerous communities focused on developing solutions for effectively handling and analyzing large datasets. To ensure its security, research and development on Big Data must be carried out in academic, industrial, and government research laboratories.

Cloud data management encompasses activities such as detecting malware, identifying insider threats, detecting intrusions, and filtering spam. Paying more attention to security and privacy considerations is essential for effectively managing Big Data, including systems, data, and networks. It is crucial for large organizations and government agencies, which are major collectors of big data, to collaborate in developing solutions for ensuring security and privacy in the realm of Big Data. Policies regarding big data privacy, integrity, and trust must be thoroughly examined within the framework of Big Data security.

Using data collection and analysis of user activities and travel, it is possible to identify the source of disease outbreaks in different areas. Furthermore, by gathering data from social media platforms like videos, photos, and status updates, criminal or terrorist activities can be detected. Multiple domains

and technologies are vital for improving security. The participants emphasized that Big Data analysis for cyber security should focus on dealing with adaptive and malicious adversaries who may carry out undetectable attacks.

The denial of information attacks is a significant threat to data privacy. Cyber security's big data analysis requires operating on high volumes of data from multiple intrusion detection systems, sensors, and in high noise environments. Normal system usage data is combined with stealth advanced persistent threat related data. Big data analytical tools are needed to integrate data from various sources such as host, network, social networks, bug reports, mobile devices, and internet of things sensors for attack detection. Securing biometric authentication is crucial.

The authentication process involves capturing biometric data from users and using it to compare with templates that users provide during authentication. Protecting the templates containing user biometric data is crucial as it contains sensitive information. To ensure security in various environments where users interact with multiple service providers, hashing security technique is applied. Unfortunately, existing security systems are unable to effectively detect increasing cyber threats.

Previously, the goal of attacks was to attack or destroy the system. However, modern hacking attacks now target large-scale systems such as critical infrastructure and state authority, rather than just leaking information or causing service disruptions. Current defense techniques rely on limited pattern matching methods to detect these attacks. To effectively defend against these sophisticated attacks, researchers can propose a new simulation based on big data analysis that can extract information for future attack detection.

This author argues that Network Behavior Analysis (NBA) has emerged as a security management tool to enhance network security. NBA monitors both inbound and

outbound network traffic to prevent unauthorized access to host systems, software, and applications, thereby improving overall network security. According to Anand, approximately 25% of large enterprise systems will implement NBA by 2011. However, the current NBA architecture lacks proactive capabilities in preventing security incidents. While it can detect most security events in advance, it often fails to identify and address smaller threats before they become network problems. Enforcing access control policies in large data stores is crucial for securing data storage.

Recent Big Data systems enable users to submit any jobs using programming languages, which presents challenges in efficiently enforcing fine-grained access control for different users. To effectively address this challenge, there is a need for efficient policy enforcement in newly developed Big Data stores. Preventing cyber attacks is of vital importance in security and data management through Big Data analysis. By utilizing provenance information to track sensitive data, Big Data analysis systems have the potential to detect when sensitive information falls into the hands of hackers. Therefore, it is necessary to build Big Data analysis systems that are aware of provenance in order to prevent cyber attacks.

The potential of big data tool for cyber security lies in its ability to extract valuable information about attacker motivations, technical capabilities, and preventive measures against future attacks. The ongoing debate about using big data analysis for security is characterized by a conflict between two opposing perspectives. One side argues that privacy acts as a barrier to progress and restricts the opportunities presented by big data. On the other hand, others see big data as a privacy threat, infringing upon personal space in the name of technological advancement. Rather

than seeing this conflict as new, it can be seen as history repeating itself during periods of technological progress. Ultimately, it represents a clash between those who focus on disadvantages and those who recognize advantages.

The story, however, is much more intricate and as time passes, these requirements cannot be accommodated in traditional plans. In other words, big data analysis and security are here to stay. The purpose of this section is to outline how the ability to derive value from data for advanced society, and the control over it represented by security standards, can coexist successfully.

Understanding Infrastructure

As data security experts analyze more data, they gain a better understanding of the infrastructure of a complex network. Big data is used to analyze large-scale network attacks from recent history. This provides analysts with insights on how to enhance the design of network infrastructure and implement security tools to counter cyber-attacks. The more secure and resilient the foundation of a network is, the less likely data will be compromised.

Understanding Hackers

The use of big data is helping to identify hackers responsible for cyber-attacks. By analyzing attacks and recognizing patterns in their behavior, security specialists can react quickly and effectively if they are familiar with the hacker's routine. This could potentially lead to tracking their actions and finding out where they are located. Additionally, big data allows security experts to find hackers on different social media platforms such as Facebook, Instagram, YouTube, and other forums that hackers may frequent. In the past, hacking incidents typically involved personal information leaks or were driven by a desire for fame. However, recent targets of hacking include various organizations and government agencies.

The text discusses

Advanced Persistent Threat (APT), which is a specialized form of attack that combines social engineering, zero-Day vulnerability, and other methods to infiltrate systems and collect valuable data continuously. These attacks can cause significant harm to government agencies or businesses. APT is described as a strategy that uses various tactics to breach networks, stay undetected, and extract valuable information over an extended period.

The infographic provides information on the unique characteristics of APTs, including their attack phases, methods, and requirements (Siegel, J. E.). Security systems utilize patterns to defend against cyber attacks, employing tools such as firewalls, intrusion detection systems, intrusion prevention systems, antivirus software, database encryption, DRM solutions, and more. Additionally, integrated monitoring technologies are employed to supervise system logs. These security measures are developed using signature-based techniques. However, reports suggest that APT attacks cannot be effectively detected or prevented by these systems due to the lack of signatures.

Security experts are currently utilizing data mining technologies to tackle the issue of targeted attacks, proposing the use of big data analysis technology for preventing and detecting previously unidentified APT attacks. These attacks involve intrusion, reconnaissance, data collection, and actual attack steps (Figure one).

Progress in Security Tools

Cybercriminals have the ability to develop sophisticated tools that exploit vulnerabilities within networks.

Big data is utilized by cybersecurity experts to create tools that combat hackers and aid security professionals in various tasks, such as compliance reporting, monitoring, alerting, and managing complex systems. Lumify is a popular big data analytical tool that offers a visualization platform for analyzing large sets of data. It encompasses features like 2D and 3D graph visualizations, full-text search capability, complex histograms, interactive geographic maps, and collaborative workspaces.

Lumify's Search

Results

Users can either upload their personal data or use the built-in search engine to initiate a search. The search engine has the ability to identify different artifacts such as documents, images, and videos. Additionally, it can locate entities which are individual terms extracted from the artifacts during the data extraction process by an analyst.

The data can be customized for a specific search using Lumify's built-in filters, reducing unusable data. Once the search results are found, users can utilize the graphic visualization tool.

Graphic Visualization

Analyzing big data is complex as terabytes of data need to be sifted through to identify common interests. Lumify simplifies this process through graphic visualization. After completing the search (as shown above), users can place the results on a graph by dragging and dropping them. Users have the option to click on the results and load related items or specific types, establishing relationships between entities.

The relationship between the entities can be established either by creating it or by finding a common interest.

Map Integration

When data or an entity is loaded in Lumify, it can be associated with a geographical location. Any entity or data that has a location will have a location symbol displayed next to it (as shown in the picture below). By clicking on the location menu in the top left-hand corner of the screen (also shown in the picture below), the user can access a global map that displays the locations associated with the previously loaded entities. The user can then load data within a certain radius of the entity location and view results that are related to the location of the entity.

The map and visual are fully compatible

with each other, meaning whatever you do in one area of the database, the other tool is updated with the latest data. Lumify can be the leading BIG DATA analytical tool on the market because of its ability to display BIG DATA visually.

Conclusion

The chances of cyber-attacks increase daily because of a dependency on the internet to achieve daily task, per Susan O’Brien of Datameer, 99 percent of computers are vulnerable to attacks, nearly 1 million malware threats are released daily, and the estimated cost of cyber-crimes average a BILLION dollars!! Big data has already produced positive results in its efforts to dwarf cyber threats, per security website HELPNETSECURITY “federal agencies that effectively use big data analytics see improvements in cybersecurity. In fact, 84 percent of big data users say their agency has successfully used big data analytics to thwart a cybersecurity attack and 90 percent have seen a decline in security breaches – malware (50 percent), insider threats (47 percent), and social engineering (46 percent) – because of using big data analytics.” Evolving cyber threats demand a new generation of cyber tactics, big data is leading the way in making the internet and the world a safer place. We now live in the era of Big Data.

Regardless of our preferences, organizations constantly gather vast amounts of information about us during our daily activities. They monitor our online presence, track our movements in stores, and observe our purchasing habits. While certain data may be unremarkable, it could also consist of highly sensitive details such as our financial and health-related information. Safeguarding this personal data remains a constant challenge for both large corporations and

government agencies.

"Big data" refers to information assets that are high in volume, velocity, and variety, and require innovative and cost-effective methods of processing for better insight and decision making. The velocity of the data flow can vary, leading to the need for additional processors in cloud systems to handle the increasing load. Before the data is curated and stored, analysis and changes can be made to the log data that comes from devices. The volume of the data set is a defining characteristic of big data.

The cyber realm also faces the challenge of incorporating a wider range of attributes for its resources, thereby introducing various other contextual datasets into the analysis. Variety is a crucial attribute of Big Data that allows for more advanced cyber analytics. To accomplish this, a mechanism is necessary to efficiently handle multiple and highly diverse datasets in a scalable manner.

The security of data can be ensured through controlled metadata. In the present age of Big Data, companies collect vast amounts of information about individuals, including details about their finances and health. Ontologies for metadata help identify existing information and include resources and attack event ontologies. The ontology for metadata allows browsing and querying of metadata. The ontology representations convey complete confidence in the described data. While manual browsing of this metadata is possible, its true value lies in its actionable nature. This means that selections made over the metadata ontology can automatically generate queries for the best technique to protect big data using machine learning.

Government must also take serious measures to protect personal information when handling big data, requiring any big data collector or government agency to take further steps

in safeguarding citizen privacy.

References

1. O’Brien, S. (2016, May 05). Challenges to Cyber Security & How Big Data Analytics Can Help.
2. Retrieved October 27, 2016, from
3. Big Data to Fight Crime. (2015, June 10). Retrieved October 27, 2016, from
4. (2016, August 30). Retrieved October 27, 2016, from
5. Lumify Documentation. (n.d.).

Retrieved November 22, 2016, from