The Role of Data science in Cyber security
Introduction:
The volume of data generated every day is adding at a surprising rate. Nearly 5 quintillion bytes of data are being created daily. With the rise in data, there has also been a swell in data breaches. Hacking and piercing a system using colorful tools has come a significant cause of concern for associations and individualities worldwide. Sophisticated data wisdom ways are now extensively used by bushwhackers to break into a system. The question is if data wisdom can be used to take charge of the system, can it be used to help it from playing? The answer is yeah; with the use of data wisdom in cyber security, it has come easy to prognosticate vulnerability in a system, which in turn prevents the implicit threat of breach by taking applicable measures.
What’s Data Science?
Data wisdom is a branch of AI which involves studying and assaying large volumes of data using colorful tools and ways. It’s used to find unseen patterns and draw meaningful perceptivity from the data. A data scientist’s part involves preparing data for disquisition, analysis, and visualization, along with developing models which will fruit issues for unborn inputs Data Science finds its operation in colorful aspects. Cybersecurity Data wisdom is a discipline primarily concentrated on guarding systems and data from internal or external pitfalls. The demand for Cybersecurity data scientists has increased extensively with the rise in challenges related to security. threat analysis should be one of the primary chops held by a data scientist cyber security expert.
What is Data Science in Cyber security?
Data Science for cyber security has been a game changer in defying fraudulent conditioning. Data Science uses Machine literacy tools on once data to prognosticate the liability of an intrusion or attack. It involves developing algorithms to conclude patterns from former attacks and beforehand advising about the trust ability of the system in use. For illustration, Detecting unauthorized access in an institution. The AI model would grant access to only pre-registered druggies grounded on their credentials and dissect the exertion of these druggies so that there’s no exertion beyond authorization. All these ways are used to help any kind of data breach or abuse of information.
Technology is enhancing day by day. therefore, the implicit threat of cybercrimes is also adding. If you’re still wondering about data wisdom or cybersecurity which is better? The best possible answer to this question is data wisdom for cyber security. The quantum of sensitive data within an association is adding day by day, it becomes decreasingly important for each one of them to include data wisdom in their threat analysis plans.
Data science plays a crucial role in cyber security. With the help of data analytics and machine learning tools, organizations can conduct thorough analysis of information to reveal trends, patterns, and actionable intel. For instance, they can use the extracted information to predict potential attacks that can take place in future. Modern data science can both enhance and simplify the use of intrusion detection systems. By feeding present and historical data into a machine learning algorithm, this system can precisely detect potential problems. Over time, as such system becomes more precise; it can foretell future attacks and spot various loopholes. Another concern of a data attack is the loss of extremely valuable data and information, which can be really damaging to your organization. With the use of security measures like highly complex signatures or encryption, you can stop anyone from probing into a dataset. By involving Data Science, you can start to build impenetrable protocols. For example, by analyzing the history of your cyber-attacks, you can develop algorithms to detect the most frequently targeted chunks of data.
Data Science in Cybersecurity to cover the Digital Footprint Today, everyone is under the trouble of an attack, and these attacks are not limited to just large associations or governments. Hackers are always looking for the smallest occasion to get sensitive information. These include particular information, bank account details, etc. There is no way one can wash down their digital exertion. With every round of surfing, we are leaving a huge amount of information that helps businesses to grow their trades by making user- acquainted choices. Data wisdom becomes fundamental in guarding our digital traces as they can be misused. For illustration, my particular information can be used for identity theft. A person can claim my identity and thus produce a lot of chaos by piercing private and confidential accounts, thus creating a lot of loss.
There are several data science tools that are used in cyber security. Some of the most common ones include:
1. Machine Learning: Machine learning algorithms are used to analyze large amounts of data to identify patterns and anomalies that could indicate a potential cyber-attack. These algorithms can be trained to recognize specific types of attacks and can be used to predict future attacks.
2. Data Mining: Data mining is the process of analyzing large amounts of data to identify patterns and trends. In cyber security, data mining can be used to identify potential vulnerabilities in a system or network.
3. Artificial Intelligence: Artificial intelligence (AI) is used in cyber security to automate tasks such as threat detection and response. AI algorithms can be trained to recognize patterns in data that could indicate a potential attack.
4. Big Data Analytics: Big data analytics is the process of analyzing large amounts of data to identify patterns and trends. In cyber security, big data analytics can be used to identify potential threats and vulnerabilities in a system or network.
5. Natural Language Processing: Natural language processing (NLP) is used in cyber security to analyze text-based data such as emails, chat logs, and social media posts. NLP algorithms can be used to identify potential threats and vulnerabilities in these types of data.
How Applied Data Science and Machine Learning Work Together to Ameliorate Cybersecurity?
Technology is enhancing day by day. therefore, the implicit threat of cybercrimes is also adding. If you’re still wondering about data wisdom or cybersecurity which is better? The best possible answer to this question is data wisdom for cyber security. The quantum of sensitive data within an association is adding day by day, it becomes decreasingly important for each one of them to include data wisdom in their threat analysis plans.
There are colorful ways in which data wisdom help to palliate the pitfalls, below are some mentioned
substantiation
1. Protection of Data is extremely vital to any association and it’s extremely pivotal that it’s been defended at any cost, data wisdom helps to produce impermeable data channels for transferring the data using machine literacy algorithms.
2. Enhanced Intrusion Discovery with advancements in technology, hackers don’t use just one pathway to hack a system. Refined ways have increased challenges for companies to fete the paths for piercing the system. Machine literacy models developed on current and once attack information give a wholesome understanding to model different attacks. These models also prognosticate the type of attack and the probability of breaking the system.
3. Effective vatic nation does not just only mean detecting True cons. A data wisdom cyber security model should also induce veritably many False cons; this will help to combat the problem of spam calls. These ways help to produce real- world suppositions rather than old- academy hypotheticals related to pitfalls and Cyber threat.
4. Behavioral Analysis Just understanding the type of attack or knowing the probability of it affecting the system isn’t enough, one must understand a hacker’s behavioral pattern. This can serve great advantages as we will be in a position to prognosticate his/ her coming move or coming attack. This behavioral analysis is done by combining different datasets, studying the network logs, and chancing correlations between systems help to draw a hacker’s behavioral pattern and take preventative measures consequently. Considering the need of the hour, there has been a rise in data wisdom cyber security jobs.
What Do Data Science Cyber Security Professionals Do?
Data Science professionals dissect large quantities of data using statistical and programmable chops. They develop results to feed to an association’s requirements. It involves interpreting raw data and rooting precious information from it. This information is further used to interpret the beginning trend and decide a result using machine literacy algorithms. Data wisdom cyber security professionals are exposed to a large quantum of data handed by institutions that thrive on collecting further and further data to work data wisdom results. Data to be used must be managed. Handling large quantities of data without the help of data scientists is a veritably big challenge. Taking the prophetic way tightens not only the security of the sensitive data but also blocks any kind of penetration.
• Intrusion Detection & Network Traffic Analysis: By analyzing network traffic data, data scientists can identify and detect network intrusion and malicious activity such as malware infections, botnet command and control, and insider threats. This can include analyzing the source and destination of network traffic, as well as the types of protocols and ports being used.
• Vulnerability Assessment: Data science can be used to analyze data from vulnerability scanners to identify potential vulnerabilities in network systems and devices. This can help organizations to assess their vulnerabilities and prioritize which ones need to be addressed first. Eventually, this helps organizations detect and prevent these types of threats from spreading or causing damage. Security automation can also be handled by data science in networks.
• Security Configuration Management: Data science can be used to analyze data from security configuration management systems and automatically apply changes to systems and devices that are not in compliance with security policies. This can include identifying systems and devices that are most at risk, prioritizing which configurations to change first, and automating the process of configuring systems and devices.
- Security Information and Event Management (SIEM): Data science can be used to analyze data from security information and event management systems and automatically generate security alerts and incidents based on patterns of malicious activity. This can include analyzing network traffic data, system logs, and security events to identify patterns of behavior that may indicate a potential threat.
Upcoming challenges in Cyber Security Data Science
There are several research issues and challenges in the area of cybersecurity data science to extract insights from relevant data towards data-driven intelligent decision making for cybersecurity solutions. In the following, the summarized challenges are ranging from data collection to decision making.
•Cybersecurity datasets:
Source datasets are the primary component to work in the area of cybersecurity data science. Most of the existing datasets are old and might insufficient in terms of understanding the recent behavioral patterns of various cyber-attacks. Although the data can be transformed into a meaningful understanding level after performing several processing tasks, there is still a lack of understanding of the characteristics of recent attacks and their patterns of happening. Thus, further processing or machine learning algorithms may provide allow accuracy rate for making the target decisions. Therefore, establishing a large number of recent datasets for a particular problem domain like cyber risk prediction or intrusion detection is needed, which could be one of the major challenges in cybersecurity data science.
• Handling quality problems in cybersecurity datasets:
The cyber datasets might be noisy, incomplete, insignificant, imbalanced, or may contain inconsistency instances related to a particular security incident. Such problems in a data set may affect the quality of the learning process and degrade the performance of the machine learning based models. To make a data-driven intelligent decision for cybersecurity solutions, such problems in data is needed to deal effectively before building the cyber models. Therefore, understanding such problems in cyber data and effectively handling such problems using existing algorithms or newly proposed algorithm for a particular problem domain like malware analysis or intrusion detection and prevention is needed, which could be another research issue in cybersecurity data science.
• Security policy rule generation:
Security policy rules reference security zones and enable a user to allow, restrict, and track traffic on the network based on the corresponding user or user group, and service, or the application. The policy rules including the general and more specific rules are compared against the incoming traffic in sequence during the execution, and the rule that matches the traffic is applied. The policy rules used in most of the cybersecurity systems are static and generated by human expertise or ontology-based. Although, association rule learn-in techniques produce rules from data, however, there is a problem of redundancy generation that makes the policy rule-set complex. Therefore, understanding such problems in policy rule generation and effectively handling such problems using existing algorithms or newly proposed algorithm for a particular problem domain like access control is needed, which could be another research issue in cybersecurity data science.
•Context-awareness in cybersecurity:
Existing cybersecurity work mainly originates from the relevant cyber data containing several low-level features. When data mining and machine learning techniques are applied to such datasets, a related pattern can be identified that describes it properly. However, abrader contextual information like temporal, spatial, relationship among events or connections, dependency can be used to decide whether there exists auspicious activity or not. For instance, some approaches may consider individual connections as DoS attacks, while security experts might not treat them as malicious by themselves. Thus, a significant limitation of existing cybersecurity work is the lack of using the contextual information for predicting risks or attacks. Therefore, context-aware adaptive cyber-security solutions could be another research issue in cybersecurity data science.
•Feature engineering in cybersecurity:
The efficiency and effectiveness of a machine learning-based security model has always been a major challenge due to the high volume of network data with a large number of traffic features. The large dimensionality of data has been addressed using several techniques such as principal component analysis (PCA), singular value decomposition (SVD)etc. In addition to low-level features in the datasets, the contextual relationships between suspicious activities might be relevant. Such contextual data can be stored in an ontology or taxonomy for further processing. Thus how to effectively select the optimal features or extract the significant features considering both the low-level features as well as the contextual features, for effective cybersecurity solutions could be another research issue in cybersecurity data science.
•Remarkable security alert generation and prioritizing:
In many cases, the cybersecurity system may not be well defined and may cause substantial number of false alarms that are unexpected in an intelligent system. For instance, an IDS deployed in a real-world network generates around nine million alerts per day. A network-based intrusion detection system typically looks at the incoming traffic for matching the associated patterns to detect risks, threats or vulnerabilities and generate security alerts. However, to respond to each such alert might not be effective as it consumes relatively huge amounts of time and resources, and consequently may result in self-inflicted DoS. To overcome this problem, a high-level management is required that correlate the security alerts considering the current context and their logical relationship including their prioritization before reporting them to users, which could be another research issue in cybersecurity data science.