Difference Between Artificial Intelligence vs Machine Learning vs Deep Learning

Artificial Intelligence: Artificial Intelligence is basically the mechanism to incorporate human intelligence into machines through a set of rules(algorithm). AI is a combination of two words: “Artificial” meaning something made by humans or non-natural things and “Intelligence” meaning the ability to understand or think accordingly. Another definition could be that “AI is basically the study of training your machine(computers) to mimic a human brain and it’s thinking capabilities”. AI focuses on 3 major aspects(skills): learning, reasoning and self-correction to obtain maximum efficiency possible.

Machine Learning: Machine Learning is basically the study/process which provides the system(computer) to learn automatically on its own through experiences it had and improve accordingly without being explicitly programmed. ML is an application or subset of AI. ML focuses on the development of programs so that it can access data to use it for themselves. The entire process makes observations on data to identify the possible patterns being formed and make better future decisions as per the examples provided to them. The major aim of ML is to allow the systems to learn by themselves through the experience without any kind of human intervention or assistance.

Deep Learning: Deep Learning is basically a sub-part of the broader family of Machine Learning which makes use of Neural Networks(similar to the neurons working in our brain) to mimic human brain-like behavior. DL algorithms focus on information processing patterns mechanism to possibly identify the patterns just like our human brain does and classifies the information accordingly. DL works on larger sets of data when compared to ML and prediction mechanism is self-administered by machines.

Below is a table of differences between Artificial Intelligence, Machine Learning and Deep Learning:

Artificial Intelligence	Machine Learning	Deep Learning
AI stands for Artificial Intelligence, and is basically the study/process which enables machines to mimic human behaviour through particular algorithm.	ML stands for Machine Learning, and is the study that uses statistical methods enabling machines to improve with experience.	DL stands for Deep Learning, and is the study that makes use of Neural Networks(similar to neurons present in human brain) to imitate functionality just like a human brain.
AI is the broader family consisting of ML and DL as it’s components.	ML is the subset of AI.	DL is the subset of ML.
AI is a computer algorithm which exhibits intelligence through decision making.	ML is an AI algorithm which allows system to learn from data.	DL is a ML algorithm that uses deep(more than one layer) neural networks to analyze data and provide output accordingly.
Search Trees and much complex math is involved in AI.	If you have a clear idea about the logic(math) involved in behind and you can visualize the complex functionalities like K-Mean, Support Vector Machines, etc., then it defines the ML aspect.	If you are clear about the math involved in it but don’t have idea about the features, so you break the complex functionalities into linear/lower dimension features by adding more layers, then it defines the DL aspect.
The aim is to basically increase chances of success and not accuracy.	The aim is to increase accuracy not caring much about the success ratio.	It attains the highest rank in terms of accuracy when it is trained with large amount of data.
Three broad categories/types Of AI are: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI)	Three broad categories/types Of ML are: Supervised Learning, Unsupervised Learning and Reinforcement Learning	DL can be considered as neural networks with a large number of parameters layers lying in one of the four fundamental network architectures: Unsupervised Pre-trained Networks, Convolutional Neural Networks, Recurrent Neural Networks and Recursive Neural Networks
The efficiency Of AI is basically the efficiency provided by ML and DL respectively.	Less efficient than DL as it can’t work for longer dimensions or higher amount of data.	More powerful than ML as it can easily work for larger sets of data.
Examples of AI applications include: Google’s AI-Powered Predictions, Ridesharing Apps Like Uber and Lyft, Commercial Flights Use an AI Autopilot, etc.	Examples of ML applications include: Virtual Personal Assistants: Siri, Alexa, Google, etc., Email Spam and Malware Filtering.	Examples of DL applications include: Sentiment based news aggregation, Image analysis and caption generation, etc.

Source: geeksforgeeks.org

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Top 5 Applications of Machine Learning in Cyber Security

Cybersecurity is a critical part of any company. Not only companies but even governments need top-class cybersecurity to make sure that their data remains private and is not hacked or leaked for all the world to see! And with the increasing popularity of Artificial Intelligence and Machine Learning, these technologies are even becoming key players in the field of cybersecurity. Machine Learning has many applications in Cyber Security including identifying cyber threats, improving available antivirus software, fighting cyber-crime that also uses AI capabilities, and so on.

The last point is extremely relevant as many cybercriminals also use Artificial Intelligence and Machine Learning to improve and enhance their cyberattacks. According to a study conducted by Capgemini Research Institute, AI is necessary for cybersecurity because hackers are already using it for cyberattacks. 75% of the surveyed executives also believe that AI allows for a faster response to security breaches. Therefore, Machine Learning based cybersecurity software is fast becoming a necessity and not only a luxury.

So let’s see the top 5 Applications of Machine Learning in Cyber Security which companies can use so that they are safe and secure. Companies can easily do this by first implementing AI in their existing CyberSecurity protocols and then move on to specialty AI and ML Cybersecurity vendors. This can be done by using predictive analytics to detect threats and malicious activity, using natural language processing for security, enhancing biometric-based login techniques, etc.

1. Cyber Threat Identification

Cybersecurity is a very important component of all companies. After all, if a hacker manages to enter their systems, they are toast! The most difficult component of cybersecurity is finding out if the connection requests into the system are legitimate and any suspicious looking activities such as receiving and sending large amounts of data are the work of professionals in the company or some cyber threats. This is very difficult to identify for cybersecurity professionals, especially in large companies where requests range in the thousands all the time and human s are not always accurate. That’s where machine learning can provide a lot of help to professionals. A cyber threat identification system that is powered by AI and ML can be used to monitor all outgoing and incoming calls as well as all requests to the system to monitor suspicious activity. For example, Versive is an artificial intelligence vendor that provides cybersecurity software in conjugation with AI.

2. AI-based Antivirus Software

It is commonly recommended to install Antivirus before using any system. This is because antivirus protects your system by scanning any new files on the network to identify if they might match with a known virus or malware signature. However, this traditional antivirus requires constant upgrades to keep up with all the upgrades in the new viruses and malware being created. That’s where machine learning can be extremely helpful. Antivirus software that is integrated with machine learning tries to identify any virus or malware by its abnormal behavior rather than its signature. In this way, it can manage threats that are common and previously encountered and also new threats from viruses or malware that were recently created. For example, Cylance a software company has created a smart antivirus that learns how to detect viruses or malware from scratch and thus does not depend on identifying their signatures to detect them.

3. User Behavior Modeling

Some cyberthreats can attack a particular company by stealing the login credentials of any of their users and then illegally logging into the network. This is very difficult to detect by normal antivirus as the user credentials are authentic and the cyberattack may even happen without anyone knowing. Here, machine learning algorithms can provide help by using user behavior modeling. The machine learning algorithm can be trained to identify the behavior of each user such as their login and logout patterns. Then any time a user behaves out of their normal behavioral method, the machine learning algorithm can identify it and alert the cybersecurity team that something is out of the ordinary. Of course, some changes in user behavior patterns and entirely natural but this will still help in catching more cyberthreats than conventional methods. For example, there is a cybersecurity software provided by Darktrace that uses machine learning to identify the normal behavioral patterns of all the users in a system by analyzing the network traffic information.

4. Fighting AI Threats

Many hackers are now taking advantage of technology and using machine learning to find the holes in security and hack systems. Therefore, it is very important that companies fight fire with fire and use machine learning for cybersecurity as well. This might even become the standard protocol for defending against cyberattacks as they become more and more tech-savvy. Take into account the devastating NotPetya attack that utilized EternalBlue, a software hole in Microsoft’s Windows OS. These types of attacks can get even more devastating in the future with the help of artificial intelligence and machine learning unless cybersecurity software also uses the same technology. An example of this is Crowdstrike, a cybersecurity technology company that uses Falcon Platform which is a security software imbued with artificial intelligence to handle various cyberattacks.

5. Email Monitoring

It is very important to monitor the official Email accounts of employees in a company to prevent cybersecurity attacks such as phishing. Phishing attacks can be done by sending fraudulent Emails to employees and asking them for private information such as sensitive information related to their job, their banking and credit card details, company passwords, etc. Cybersecurity software along with machine learning can be used to avoid these phishing traps by monitoring the employees’ professional emails to check if any features indicate a cybersecurity threat. Natural language processing can also be used to scan the Emails and see if there is anything suspicious such as some patterns and phrases that may indicate that the Email is a phishing attempt. For example, Tessian is a famous software company that provides Email monitoring software that can be used to check if an email is a phishing attempt or a data breach. This is done using natural language processing and anomaly detection technologies to identify threats.

Future of Machine Learning and Cybersecurity

Machine learning is still a comparatively new addition to the field of cybersecurity. However, the above given 5 applications of Machine Learning in Cybersecurity are a good start in this field. The only thing to keep in mind is that machine learning algorithms should minimize their false positives i.e. actions that they identify as malicious or part of a cyberattack but that are not. Companies need to ensure that they consult with their cybersecurity specialists who can provide the best solutions in identifying and handling new and different types of cyberattacks with even more precision using machine learning.

Source: geeksforgeeks.org

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Unsupervised Machine Learning – The Future of Cybersecurity

Cybersecurity is like Tom and Jerry! While Tom always tries new ways to catch Jerry, he manages to escape in some way or another. Most of the Cybersecurity teams find themselves in the unenviable position of Tom, where they can try whatever methods they like, Jerry always escapes and tries to get the cheese in even more creative ways next time! Today’s cyber-criminals have become even more dangerous because of the variety of tools available online like proxy servers, botnets, and automated scripts. They don’t have just one method of launching a cyber-attack, and they can hide their identities by mimicking real user activity, using spoofing devices, etc. In such a high stakes game where cybercrime costs companies around $2 Trillion each year, Cybersecurity definitely needs to up its performance with Unsupervised Machine Learning.

And that’s definitely happening these days with a surge in the popularity of Unsupervised Machine Learning. According to a study by O’Reilly, the usage of Unsupervised Machine Learning has gone up by 172% in 2019. This will definitely reflect in the domain of Cybersecurity as well with more and more companies adopting this technology.

Cybersecurity in any company mainly focuses on two different facets, namely:

◉ How to counter attacks that have already occurred on the system or those that are a familiar type of cyber-attacks. How to respond against them and implement preventative measures?

◉ How to counter attacks that are totally new and never seen before. How to identify such attacks and what are the solutions to dispel them.

While companies can tackle the first facet using traditional Cybersecurity methods, there are no solutions that can handle the second scenario. And the second scenario is becoming more and more important while cyber-attacks evolve and become more unpredictable. That’s where Unsupervised Machine Learning comes in.

So let’s understand Machine Learning and how different types like Supervised, Unsupervised, and Semi-Supervised are used in the context of cybersecurity.

Types of Machine Learning in the Context of Cybersecurity

1. Supervised Machine Learning

Supervised Machine Learning is the most common method in Machine Learning. To understand this type, imagine a student that needs to be taught everything explicitly by the teacher. This student would be excellent in repeating and using the information the teacher has already taught him but wouldn’t be able to learn anything on his own. Unfortunately, that student will only be good in certain situations (like an exam!) but in general, would be quite a poor student. That’s the same case with a Supervised Machine Learning Algorithm. Here, the algorithm learns from a training dataset where the data is labeled and makes predictions about new data based on that dataset.

Now, this method would generally be fine but that is not true for a dynamic and ever-changing field like cybersecurity where Supervised Machine Learning cannot keep up. After all, hackers don’t just stick to the topics that the algorithm has learned! What this means is that a Supervised Machine Learning Algorithm would be able to identify cyber-attacks that it was trained to identify. However, if there are any attacks that are new, then the algorithm will totally fail. It will not be able to cope if the exam is out of the syllabus! In that case, machine learning engineers will have to retrain the algorithm with the data labels based on the new attacks, and by the time it has learned those, there may be even more new attacks created. Clearly, the Supervised Machine Learning Algorithm would be outclassed in this respect. That’s where Unsupervised algorithms enter the fray.

2. Unsupervised Machine Learning

If a Supervised Machine Learning Algorithm is the student that is spoon-fed all the information by the teacher, then the Unsupervised Machine Learning Algorithm is the genius student that does not need much instruction and can learn information by himself. This student is not restricted by being taught only a specific thing, but he learns from whatever comes his way by exploring and understanding the information. So this student is good in many types of situations as he can tackle problems when they arise. This is also the situation with an Unsupervised Machine Learning Algorithm. Here, the algorithm is left unsupervised to find the underlying structure in the data in order to learn more and more about the new situation.

This algorithm is much more suited to Cybersecurity. It can handle many kinds of cyber-attacks no matter if it has seen them before or not because it does not try to identify a cyber-attack based on what it has already learned. Rather, it identifies the abnormalities in the system that occur with a cyber-attack. So this means that an Unsupervised Machine Learning Algorithm will create a baseline for your system where everything is working normally. Then if any suspicious behavior occurs in the system, such as a sudden increase of data transfer in the network or transfer of some file that does not usually occur, this type of behavior will be flagged as abnormal and a sign of a cyber-attack.  

For example, Unsupervised Machine Learning is the best option for identifying IoT based zero-day cyber-attacks. There are many IoT devices connected to the cloud these days which can be used for myriad purposes including zero-day cyber-attacks. These attacks exploit any vulnerability that exists in the system, and so they don’t have any set pattern or context. That’s why Supervised Machine Learning algorithms fail to identify these attacks and Unsupervised Machine Learning can prove to be invaluable.

3. Semi-Supervised Machine Learning

As is obvious from the name itself, Semi-Supervised Machine Learning Algorithm is the student that learns both from his teacher and by himself. This type of Machine Learning represents the best of both worlds where it is a combination of Supervised and Unsupervised Machine Learning. This algorithm uses a little amount of labeled data like Supervised Machine Learning and a larger amount of unlabeled data like Unsupervised Machine Learning to train the algorithms. The labeled data can be used to partially train the Machine Learning Algorithm, and this partially trained algorithm also finds insights organically.

A Semi-Supervised Machine Learning Algorithm may well be the perfect combination for Cybersecurity. This algorithm could use Unsupervised Learning to identify any abnormalities in the system that occur with a specific cyber-attack and then label that cyber-attack as a threat that it can identify using Supervised Machine Learning if it occurs again in the future. In this way, a Semi-Supervised Machine Learning Algorithm embodies the advantages of both types in that it can constantly be on the lookout for any disturbances and deviations from the norm in the system and simultaneously have a provision for quickly identifying cyber-attacks that have already occurred before and eliminating them.

Adoption of Unsupervised Machine Learning in Cybersecurity

There is still some hesitation in the adoption of Unsupervised Machine Learning in the Cybersecurity industry and with valid reasons. This type of Machine Learning is totally based on reactionary performance. Since the data is not labeled beforehand, the Unsupervised Machine Learning Algorithm can only react when the attack occurs and cannot implement any proactive methods. Also, it is impossible to measure its effectiveness against an attack which understandably makes industries hesitant to invest their money in this technology.

However, there is still a lot of hype about Unsupervised Machine Learning in Cybersecurity because this technology is a step in the right direction. Investment in developing this will undoubtedly yield results because Unsupervised Machine Learning is indeed the future of Cybersecurity. While cyber-attacks are becoming more and more creative with different tools and technologies at their disposal, the cyber defense also has to up its game. And in this, Unsupervised Machine Learning can prove to be invaluable as it can identify abnormalities in the system to signal multiple types of cyber-attacks no matter how advanced they become.

Source: geeksforgeeks.org

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Machine Learning – Types of Artificial Intelligence

The word Artificial Intelligence comprises two words “Artificial” and “Intelligence”. Artificial refers to something which is made by human or non-natural thing and Intelligence means the ability to understand or think. AI is not a system but it is implemented in the system.

There can be so many definitions of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present human can do better.” Therefore It is an intelligence where we want to add all the capabilities to machines that humans contain. Artificial Intelligence can be classified into two types:

1. Based on the Capabilities of AI. 

◉ Artificial narrow Intelligence.

◉ Artificial General Intelligence.

◉ Artificial Super Intelligence.

2. Based on Functionality of AI.  

◉ Reactive machines.

◉ Limited memory.

◉ Theory of mind.

◉ Self-awareness.

Let’s discuss all of them one by one. 

Based on the Capabilities of AI

1. Artificial Narrow Intelligence: ANI also called “Weak” AI is that the AI that exists in our world today. Narrow AI is AI that programmed to perform one task whether it’s checking the weather, having the ability to play chess, or analyzing data to write down the journalistic report. It can attend a task in real-time, but they pull information from a selected perform outside of the only task that they’re designed to perform.ANI system can attend to a task in the period however they pull info from a specific data set. These systems don’t perform outside of the sole task that they’re designed to perform. 

2. Artificial General Intelligence: AGN also called strong AI it refers to machines that exhibit human intelligence. we will say that AGI can successfully perform any intellectual; a task that a person’s being can. this is often the type of AI that we see in movies like “Her” or other sci-fi movies during which humans interact with machines and OS that are conscious, sentiment, and driven by emotional and self-awareness. It is expected to be ready to reason, solve problems, make judgments under uncertainty in decision-making and artistic, imaginative.but for machines to realize true human-like intelligence. 

3. Artificial Super Intelligence: ASI will be human intelligence in all aspects from creativity, to general wisdom, to problem-solving. Machines are going to be capable of exhibiting intelligence that we have a tendency to haven’t seen within the brightest amongst. This is the kind of AI that a lot of individuals square measure upset concerning, and also the form of AI that individuals like Elon musk assume can cause the extinction of the human race.

Based on Functionality of AI

1. Reactive Machines: Reactive machines created by IBM in the mid-1980s.These machines are the foremost basic sort of AI system. this suggests that they can’t form memories or use past experiences to influence present -made a choice, they will only react to currently existing situations hence “Reactive”. An existing sort of a reactive machine is deep blue, chess playing by the supercomputer. 

2. Limited Memory: It is comprised of machine learning models that device derives knowledge from previously-learned information, stored data, or events. Unlike reactive machines, limited memory learns from the past by observing actions or data fed to them to create experiential knowledge. 

3. Theory of Mind: In this sort of AI decision-making ability adequate to the extent of the human mind, but by machines. while some machines currently exhibit humanlike capabilities like voice assistants, as an example, none are fully capable of holding conversations relative to human standards. One component of human conversation has the emotional capacity or sounding and behaving sort of a person would in standard conversations of conversation. 

4. Self-Awareness: This AI involves machines that have human-level consciousness. this type of AI isn’t currently alive but would be considered the foremost advanced sort of AI known to man.

Source: geeksforgeeks.org

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How Artificial Intelligence (AI) and Machine Learning(ML) Transforming Endpoint Security?

Endpoint security refers to a methodology of protecting devices like laptops, mobiles and other wireless devices that are used as endpoint devices for accessing the corporate network. Although such devices create potential entry points for security threats still endpoints are becoming a more common way to compute and communicate than local or fixed machines. Such attacks tend to occur because a lot of data is outside the corporate firewall that exposes it to security threats. Some such threats to which our system is exposed constantly are phishing, spoofing, vishing, etc.

Below you will find in detail description about the security attacks and the solutions provided by both Machine Learning and Artificial Intelligence.

1. Social Engineering

In such types of attacks, a person pretends to be someone else in order to trick users into disclosing confidential data, information or both. In order to prevent any kind of unauthorized access gain to confidential information, a cloud-based stack can protect against highly targeted script-based attacks including malware. ML and AI enhance the capabilities of this cloud network by supporting real-time blocking of new and unknown threats.

2. Phishing

It is one of the most common types of attacks aimed at stealing the victim’s personal information like banking account details. Attackers usually use spoofed emails that contain links directing the user to a malware-infected site. Such sites replicate genuine sites and trick the user into entering confidential details like passwords. AI and ML co-ordinate very well with each other in order to identify potential anomalies in emails. By analyzing the metadata, content, context of emails the system makes suitable decisions on how to tackle the malicious email. Using words like urgent and promotion in an email are picked by the AI systems as suspicious but the final decision is made after analyzing the email as a whole based on the following parameters. Whether there was a previous conversation, a connection between the subject and the content of the email, along with misspelled domains if any. ML-based protection continuously learns from such scenarios along with feedback data given to it by the user making the protection more accurate day by day.

3. Spear Phishing

It is a type of phishing but done in a more planned way by the attacker. The attacker first tends to do a background check on the user and then according to the users’ most common interests, most common visited websites and social media feeds the user is analyzed and is sent so-called credible mails which ultimately lead the target to open up little by little. Ultimately the user ends up downloading the malicious file. However, ML and AI make consistent efforts to tackle such kind of attacks. AI is used to understand the communication patterns which take place and if the system identifies an attack the ML-powered AI system block it before they cause any damage.

4. Watering Hole

Such attacks are based on the principle that a hunter uses for the prey to fall into the trap. In such attacks, the attacker tends to exploit the vulnerabilities of a website that is visited again and again by the user. ML and AI her us the path traversal algorithms for detecting any kind of malicious data. These traversal algorithms analyze if a user is directed to any kind of malicious website. For plotting such kind if attacks a lot of data from email traffic, proxy and pocket are required which is thoroughly scanned by the ml systems.

5. Network Sniffing

It is the process of capturing and analyzing the data packets that travel across the network. The network sniffer monitors all the data with the use of clear and readable messages being transmitted over a network. The best countermeasure to prevent sniffing is the use of encrypted communication between the hosts. VPNs are particularly used for encrypting the data. ML and AI-powered VPNs have however taken the protection to another level. ML-powered VPNs are equipped with a sophisticated learning algorithm that creates a private tunnel in the open networks like WiFi encapsulating and encrypting all the data sent on the network. This is done to prevent an attacker from deciphering the contents even if the data packets have been intercepted

6. DDOS Attack(Distributed Denial of Service Attack)

The principle of this attack although remains too straightforward but still, is effective today. It aims at causing interruption or suspension of a specific host or server by flooding it with large quantities of useless traffic(data) so that the server is not able to respond. Such flooding is done by multiple botnets(infected systems) simultaneously. DDOS is very effective because they are of lower bandwidth and hence they tend to bypass the detection quite easily and are often mixed with other attacks that also prevent them from the detection. However, AI-powered ML systems can instantaneously distinguish good traffic from bad traffic. This detection takes place within a few seconds that is the reason that such systems are preferred because they are quick, accurate and can analyze huge chunks of data in a very short interval of time.

Although Machine learning and Artificial intelligence have revolutionized the security systems there is no denying the fact that they have drawbacks in certain areas. One of the drawbacks is that dealing with AI AND ML systems requires a lot of financial resources which a medium scale industry cannot bear to spend. Sometimes hackers may exploit artificial intelligence and use it against the user if a hacker is successfully able to foil the system tricking it into misidentifying or misclassifying certain objects due to modified inputs by an attacker. In simple terms, the attacker may trick the system into thinking about the absence of a particular security check and manage to open a device without a face id or a password. Certain ML-powered software can also mimic a person’s voice after listening to the voice for just some time. Such software is used for vishing. Vishing is a technique in which phishing is combined with voice. This attack involves caller ID spoofing that masks the real phone number with that similar to the target, making them believe in the genuineness of the caller and thus successfully carrying out the attack. Thus we can say AI AND ML act as double-edged swords while transforming the endpoint security.

Source: geeksforgeeks.org

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How will AI and Machine Learning Affect Cyber Security?

The internet is increasingly becoming a part of our lives, growing every second. A new change takes place every day, rendering the prevailing system obsolete. Adjusting to this change is not always easy. The risks associated with the internet are many and affect the security of the users to a great extent. With the advent of Artificial Intelligence and Machine Learning, every process is being automated. Artificial Intelligence and Machine Learning are making things convenient for internet users but also for hackers who use AI to orchestrate multiple cyber-attacks. 

What is Cybersecurity?

Cybersecurity refers to the protection of computers or other similar devices from the theft of information, damage of software or hardware and other intellectual properties. Cybersecurity is important and holds relevance as all the sections of the society such as Governments, Corporates, the military, various financial institutions, etc. are driven by data. They store huge amounts of data on computers and various other devices that use the internet to stay connected. Also, this data is pieced together from sensitive information that is not available for public viewing and usage. These data sources tend to exchange information over the network on a very frequent basis which exposes the data to several cyber risks. This data can be easily misused by any third party. Cybersecurity has become the topmost concern of every internet user as all of us keep feeding a certain amount of data to our smartphones and personal computers.

Cyber-attacks may lead to the following:

1. Identity theft, extortion of information which might result in blackmailing 

2. Malware induction into the systems, affecting multiple systems by injecting viruses

3. Spoofing, Phishing, and Spamming

4. Denial of various services which may further lead to multiple attacks

5. Password theft

6. Sabotaging vital information

7. Vandalism through various websites

8. The exploitation of privacy through web browsers

9. Account hacks and money scams

10. Ransomware

11. Theft of Intellectual Property

12. Unauthorised access to computer systems and laptops

Cybersecurity aims at preventing theft of information, various data breaches and some malware and ransomware attacks. It acts as the only measure to prevent online frauds and helps in risk management. Cybersecurity could be managed by a company either on its own or with the help of a third party which specialises in the area. With proper measures to prevent cyber-attacks, cybersecurity protects various businesses against malware, phishing, ransomware, and social engineering. 

What is Artificial Intelligence and Machine Learning?

Machine learning and artificial intelligence are data-driven approaches to make decisions with no explicit programming involved.  With the help of artificial intelligence, processes are automated, thus making the business activity free from any human intervention and bias. 

Artificial intelligence is shaping the way companies make decisions. This enables machines to do their work on their own which was earlier done by employing a workforce to operate various machines. With the application of AI, the data and the algorithm are given as the input which teaches the machine to perform a specific task with utmost precision. With the help of AI, processes are being optimised and the tasks are becoming speedy and error-free. Also, with the help of artificial intelligence and machine learning, data is mined and various patterns based on past trends are drawn out. These trends help in making decisions concerning the present and the future.   

Effect of AI and ML on Cyber Security

With the advancement in the field of artificial intelligence and growth in the number of applications of machine learning, new methodologies are being developed to make the cybersecurity space more automated and risk-free. With the application of these elements, the cybersecurity personnel can easily organise and manage log data. Cybersecurity involves a lot of data points that can make use of artificial intelligence, as AI is all about data clustering, classification, processing, filtering, and management.

AI, though a very strong concept, cannot set-up and run on its own. It needs to have specific data chunks based on which the decisions must be made. Machine Learning analyses data from the past and then comes out with the optimum solutions for both the present and the future. Therefore, the past data will have to be made available to make the combination of machine learning, artificial intelligence, and cybersecurity work.

The Algorithms must be fed in so that the data from the past can be organised effectively. The system then has to provide instructions on various elements and patterns based on which it will scan threats and other malware. The algorithms have to be designed in such a way that the machine can easily differentiate between a normal situation and a situation where the security of the party involved is compromised. With the help of this pre-defined pattern, the machine learning system recognises the party trying to break into the system and disrupt the essence of it.

Machine learning and artificial intelligence should be quick to secure data as hackers can get into any system and hamper the intellectual property before the organisation realises a breach has happened. With the help of artificial intelligence, the attack can be recognised at a very early stage and then neutralised so that it doesn’t affect the system further. 

With multiple applications, machine learning and artificial intelligence are a great investment for a company whose focus is on strengthening cybersecurity and minimising the loss of sensitive information. With these tools, cybersecurity is becoming stronger with every passing day.

Artificial intelligence provides strong tools that increase the efficiency of cybersecurity algorithms. The software is highly effective when it comes to tracing negative elements. Data tracking mechanisms are becoming more powerful and effective, thus reducing the risks associated with it and improving its operational efficiencies. This will benefit the cybersecurity space in a multifold way.

Machine learning and artificial intelligence can help cybersecurity experts in analysing high volume data sources and streams in many ways.

Methods of Machine Learning (ML) and Artificial Intelligence (AI) to Analyse High Volume Data

1. Correlating various data sets by organising them in a specific pattern, scanning various possible threats, making a predictive analysis and forecasting the next attack.

2. Using data cleansing techniques, continuous auditing of data protection techniques can be done to safeguard the users and other relevant parties, checking if the restrictions placed are working effectively. 

3. Developing mechanisms to secure data without being a burden on the resources. With the help of machine learning and artificial intelligence, cybersecurity professionals can optimise costs and avoid wasteful expenditures.

4. With the help of artificial intelligence and machine learning, various malware and infections can be easily detected by setting up a security platform that has a built-in mechanism of scanning huge amounts of data, data networks and recognising any possible threats. 

The cloud computing element plays an active role in defining and deciding the online working patterns of any company. From various hosting servers and equipment, companies have now shifted to various cloud platforms like Microsoft Azure which protects data more effectively. This comes as both, a boon and a bane. On one hand, this is eliminating the usage of hardware reducing the complexity of various operations. On the flip side, a user’s complete trust is handed over to a third party where there is no first-hand information available about the companies operating the cloud, but all the sensitive information is now relayed to and fro. This makes the company vulnerable but due to lack of other options, cloud computing is the best option among its alternatives. With the help of artificial intelligence, software logarithms can be easily deployed across various cloud infrastructures. 

Ending Notes

Cybersecurity has been a primary concern for all kinds of internet users, be it individuals or giant corporations. Every day, every second, data is being transmitted with the help of various networks exposing it to several threats, risking information which cannot even be measured in monetary terms. With machine learning and artificial intelligence coming into the picture, cybersecurity is becoming more effective and powerful but there is another side of the coin too. With the implementation of machine learning and artificial intelligence, processes are becoming much easier, which exposes the systems to multiple risks. Breaking into computer systems has become a child’s play using AI and ML. Therefore, the scenario will not be favourable if machine learning and artificial intelligence are given all control. Manual intervention is important to keep things in check. With human intervention, these attacks could be prevented in a more structured way which strengthens the system.

Source: mygreatlearning.com

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