Infographic : How To Secure Your Browser

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Infographic : How To Prevent Email Spams

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Detailed article on how to prevent email spams : How To Prevent Email Spams

Infographic: How To Backup Data

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Symmetric Key Encryption vs Public Key Encryption

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If we want to safeguard our data from theft or protect our privacy, encryption is the most feasible option. It converts our sensitive data to something that can be read only by authorized people. 

Nowadays, there are many encryption solutions available and we get many options while encrypting our data. Some of them use symmetric key encryption and some use public key encryption. But, what are symmetric key encryption and public key encryption actually? How do they work and how are they different from each other? In this article we would discuss about that.

What is Encryption ?

Encryption is a process which takes as input a plaintext message and converts it into an encoded message called ciphertext, such that only authorized people can read it. And, decryption is the opposite process. It takes as input a ciphertext message and converts it back into the original plaintext message. These encryption and decryption processes take help of secret keys to perform these actions. The secret key used in encryption process is called an encryption key and the secret key used in the decryption process is called the decryption key.

What is Symmetric Key Encryption ?

As said above, encryption and decryption processes take help of encryption key and decryption key respectively to encrypt or decrypt data. symmetric key encryption is an encryption process in which the same secret key is used during both encryption and decryption. We call the secret key symmetric key. So, if we encrypt a file using a symmetric key encryption using a secret key, we would have to use the same secret key at the time of decryption also.

This symmetric key encryption can use either stream ciphers or block ciphers.

Stream Ciphers

In stream ciphers, each plaintext digits is taken one by one from the plaintext message and encrypted using a keystream. A keystream is basically a stream of pseudo random characters used as keys. At the time of encryption, each plaintext digit is taken one by one and is encrypted with corresponding digit of the keystream.

This stream cipher can be of two types:

• Synchronous Stream Cipher
• Asynchronous Stream Cipher

In synchronous stream cipher, the keystream does not depend on the plaintext or the ciphertext message. It is generated independently. 

In case of synchronous stream ciphers, the sender and the receiver of the encrypted message must be in the same step for the decryption to be successful. If a digit is added or removed at the time of transmission, the synchronization will be lost. In practical implementation though various methods are used to restore the synchronization, if it gets lost.

In asynchronous stream cipher, N number of previous ciphertext digits are used to compute the keystream. This N can vary with the implementation. In asynchronous stream cipher, the receiver of the ciphertext message can automatically synchronize with the keystream generator after receiving N ciphertext digits, which makes it easier to recover if digits are added or lost at the time of transmission.

Because of their speed and simplicity of implementation in hardware, stream ciphers are often used. RC4, A5/1, A5/2, FISH, Helix, ISAAC etc are a few stream ciphers that are commonly used in many software.

Block Ciphers

In block ciphers, the input plaintext message is divided into a number of blocks of some fixed length and each block is then encrypted with the help of symmetric key.

If a message produces the same ciphertext message each time it is encrypted with a symmetric key, then the encryption process is supposed to be weak. Because in that case, the attacker can observe the bit patterns in the ciphertext message and guess the plaintext message. So, an Initialization Vector is often used for that purpose. An Initialization Vector is basically a pseudorandom value which is used along with the symmetric key at the time of encryption. It can randomize the plaintext message, so that the same plaintext message produces different ciphertext messages each time it is encrypted even with the same symmetric key.

Block ciphers are widely used in many software. Data Encryption Standard or DES, RC5, Advanced Encryption Standard or AES, Blowfish are some examples of block ciphers.

What is Public Key Encryption ?

As discussed already, symmetric key encryption uses the same secret key at the time of encryption and decryption of data. But, this may be inconvenient at times. For example, if two users want to transfer some encrypted message between them over the internet using symmetric key encryption, they would need to share the secret key with each other. And, this may not be possible all the time. And, to address that public key encryption is used.

Public key encryption is an encryption process in which two different keys are used at the time of encryption and decryption. Typically, one key is used at the time of encryption and the other one is used at the time of decryption. These are called private key and public key.

Each user who wants to use public key encryption has to create a keypair consisting of a public key and a private key. The private key must be kept secret with the user and the public key can be distributed with others who want encrypted communication with the user.

If a plaintext message is encrypted with the private key, it can be decrypted with the public key. And, if it is encrypted with the public key, it can be decrypted with the private key. And, this makes public key encryption much convenient to be used in encryption, decryption and in making digital signatures.

If Alice wants to send an encrypted message to Bob, she would need to encrypt the message using Bob’s public key. Bob can decrypt the message using his private key and read. As the private key is kept secret to Bob, only Bob would be able to decrypt the message and read.

But, at the same time, Bob may need to make sure the encrypted message is sent by Alice only and not by anyone else using Bob’s distributed public key. Digital Signatures are used for that purpose. Alice can make a digital signature of the message using her private key and send it to Bob along with the original encrypted message. Bob can verify the digital signature using Alice’s public key. As no one else knows Alice’s private key, Bob can be sure that Alice only has sent the encrypted message.

Thus, public key encryption can be used conveniently for encryption, decryption and digital signatures. DSA, RSA, PGP use public key encryption. PGP though can use both symmetric key encryption and public key encryption depending on the application.

What is 2 Factor Authentication ?

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We often use a combination of username and password to authenticate ourselves. But, this is not secure enough. We often get to hear about data breaches using weak passwords or password reuse. We are also aware of malware like keyloggers that can steal passwords of users. And, a feasible way to address that problem is to use 2 Factor Authentication.

What is 2 Factor Authentication ?

We often use several pieces of information to prove our identity at the time of authentication, such that no unauthorized person can know the information. These are called factors of authentication. For example, a password, a PIN, a security question etc are authentication factors.

There are mainly three types of factors that are commonly used for the purpose of authentication.

• Knowledge Factor
• Possession Factor
• Inherence Factor

Knowledge Factor

A knowledge factor refers to a piece of information that the user only knows. For example, a password or a PIN is considered to be a knowledge factor. A security question is also a knowledge factor, though it is considered to be a weak factor. An attacker can do enough research on the victim and find the information used.

Possession Factor

A possession factor refers to something that the user has. A hardware token used at the time of authentication can be considered to be a possession factor. Authentication using ATM card is also a good example of possession factor. As anyone without physically possessing the possession factor cannot authenticate, authentication using possession factor is considered to be quite secure. But, it may prove to be inconvenient at times as the user always has to keep the possession factor along with him in order to authenticate himself.

Inherence Factor

Inherence factor refers to something that is an essential characteristic of the user. Authentication using biometrics like fingerprints, iris or voice can be a good example of inherence factor. This method of authentication is supposed to be quite secure.

Any authentication process that uses only one of the above factors is called a single factor authentication. A multifactor authentication is an authentication process that uses more than one of the above factors. And, a 2 Factor Authentication or 2FA is authentication using two of the above three factors.

Authentication using ATM card and PIN is a good example of 2FA. Here, the ATM card is the possession factor and the PIN is the knowledge factor. Authentication using password and One Time Password (OTP) sent to the user’s mobile phone is also an example of 2FA. Here, the password is the knowledge factor and the user’s mobile is the possession factor.

How secure is 2 Factor Authentication using OTP sent to mobile phones ?

Many websites use 2FA using password and an OTP or One Time Password that is sent to the mobile phone of the user at the time of authentication. This can be considered as 2FA, though it does not provide very strong security. Attackers can infect the user’s mobile phone with malware or perpetrate a Man-In-The-Middle Attack to steal the OTP from the user’s mobile phone and authenticate to the system without physically possessing the mobile phone. 2FA using a hardware token instead is considered to be more secure.

Another option that users can use for 2FA is using Google Authenticator. In this method, the user has to install the Google Authenticator application in his mobile phone and do some setup beforehead. Later, when the user wants to authenticate to any website, he has to run the application. The application will show a 6 digit code and sends the same code to the website at the same time. The website then asks the user to enter the 6 digit code and verifies it with the sent code. As the website has to provide a shared secret key to the user to store it in the application at the time of setup, an attacker will need to get the shared secret key or physically possess the mobile phone to be able to authenticate to the account.

Thus, 2 Factor Authentication using mobile phones does not provide very strong security. But, surely it is more secure than using single factor authentication and more convenient than using a hardware token.

Nowadays, many website provide the option of using 2FA. Users should enable it wherever possible to secure the account in a better way.



Read More

How to create a strong password ?

How to prevent phishing ?

How to prevent ransomware ?

How to prevent Petya ransomware ?

What is firewall and how does it work ?

How to prevent email spams ?

What is Social Engineering ?

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We often hear the term “social engineering”. It is a technique commonly used by the attackers to spread malware or steal sensitive data from the victims. What is this social engineering actually? How do attackers use this for malicious purposes and how can we safeguard ourselves? In this article we would discuss about that.

What is Social Engineering ?

Sometimes we think in certain ways that deviates from being rational or showing good judgment. These are called cognitive biases. These cognitive biases are often maliciously exploited by the attackers in perpetrating cyber crimes. Social engineering is a technique based on these cognitive biases of common people.

Social engineering refers to the psychological manipulation of people with the purpose of deceiving them in performing malicious actions like installing a malware or divulging sensitive information, which otherwise the victims would not be doing.

Types of Social Engineering

There are several types of social engineering.

Pretexting

In pretexting, criminals create an imaginary scenario to convince a user to divulge sensitive information or perform other actions that solve the malicious purposes of the attackers. The attackers often do this by researching and exploiting the information to impersonate a legitimate authority and deceiving the user. A very good example can be impersonating a tax authority and deceiving a victim in divulging sensitive information. Another example may be, impersonating a coworker who has some urgent problem and requires access to additional network resources.

Baiting

Baiting is like a real world Trojan Horse. Attackers use some physical media to lure the victims and exploit the curiosity or greed of the victims to victimize them. A very good example can be to leave a malware-infected USB drive in public places and wait for victims. If a victim, out of curiosity takes the USB drive and inserts it into his computer, his computer will be infected with malware and give access of that to the attackers.

Quid Pro Quo

In this technique, attackers lure the victims in divulging sensitive information in return of something very cheap. A good example can be, offering icecreams or chocolates to young people to make them divulge their sensitive passwords.

Scareware

Scareware involves scaring the victim into thinking that his computer has some technical problem or the computer is infected with some malware, that needs immediate removal. This technique is often used by the attackers to trick users in installing rogue anti-malware, that itself installs malware in the computer.

Phishing

Phishing is a technique widely used by the attackers to deceive victims into divulging sensitive information or installing malware in their computers. The attackers typically sends an email purportedly from a legitimate authority and requests to verify some details by clicking on a link or by opening a malicious attachment. The attackers typically use threats and creates a sense of urgency to the users, so that users get worried and fall victims.

Vishing

In this technique, the attackers use a rogue Interactive Voice Response or IVR system to recreate a legitimate-sounding copy of a bank or other legitimate authority and use that for phishing. Attackers often send the victims some legitimate looking numbers to verify some details and when the victims make a call, they are deceived to divulge passwords, PINs or other sensitive information. In some cases, the attackers ask the victims to login using the IVR and reject the credentials continually, so that the victims type in the credentials multiple times or are are tricked to type in multiple passwords.

 Techniques used in Social Engineering

Attackers can use several methods in social engineering.

Email from a friend

Attackers can spoof email address of a friend or relative and send a phishing email to the user. As the email contains email address of a friend or relative, it becomes more difficult for the victims to detect such scams.

Containing a link

Attackers often send emails containing a link that points to some malicious website. The website may spread malware or it may be a clone of a legitimate website that is used by the attackers to trick users in divulging sensitive information.

Containing attachment

Attackers often send an email requesting the victim to verify some details by opening a malicious attachment and when the attachment is opened, the computer gets infected with malware.

Urgently asking for help

Attackers can send emails urgently asking for help. They may talk about an imaginary situation and ask the victim to send money to the sender.

Asking for donation

Attackers may send emails asking for donation for their charitable fundraiser and instruct the victim how to send money.

Asking to verify some information

Attackers may send some malicious attachment and trick the user in opening it by requesting to verify some information. The attackers often create a sense of urgency through the email to increase the probability that the email will be opened by the victim.

Notifying you are a winner

Attackers may send an email claiming to be from a lottery, a dead relative or some other wealthy person who wants to transfer money to the victim’s bank account and thus trick the victim in clicking a link or attachment or divulging sensitive personal information.

Example of Social Engineering

Amazon Phishing Scam

This scam appeared in January, 2017. In this scam, a victim typically gets an SMS as mentioned below:

Order Confirmation (#101-2341765-1192723)

Order total: 70$

If you did not authorize this purchase, click http://bit.ly/amazon-refund to Cancel and Refund.

As usual the link points to some fraudulent website that looks quite identical to Amazon website and asks for sensitive credentials from the victim. The fake website even asks for entering credit card numbers to the victims. No doubt on providing such sensitive details the victims’s Amazon account as well as financial details get compromised.

However, if you look carefully, you can notice some pointers that indicate the SMS is not legitimate.

• It should have been written as $70 and not 70$. A legitimate communication should not have this mistake.
• It is unlikely that Amazon will send a link using such URL shortening service.

This is a good example of a scam using Social Engineering. However, if a user gets any such unexpected text, the best way to deal with it would be not to visit the provided link, but to login in legitimate Amazon website and verify the active orders. The user can also call the Amazon customer care and clarify.

Social Engineering Prevention

We can always take a couple of steps to protect ourselves in a better way:

• If an email gives a sense of urgency to click on a link, open an attachment or reveal any sensitive information, slow down and think twice to perform any action that the sender wants you to do.
• If an email looks suspicious, spend some time to research the facts. Sometimes some simple google searches help us a lot in preventing problems.
• Delete emails that request to divulge credentials or other sensitive information. They are surely scams.
• Reject requests coming from an unknown person that ask for help via emails.
• Do not click on any link in a suspicious email sent by an unknown sender.
• Do not open attachment of emails sent by unknown senders.
• Email spoofing is widely used by the attackers to trick victims. So, if you get an email containing email address of a friend or relative in the sender fiend but looks suspicious, do not click on any link in the email or open any attachment.
• If you receive an email offering a foreign lottery or sweepstakes, money from an unknown user or funds from foreign country in return of divulging personal information, delete the email immediately.
• If an email looks suspicious, confirm with the sender offline before responding to the email. It is better to be safe than sorry.
• If you think an email is a spam, mark it so in the spam filter. Spam filters often use machine learning in detecting spam emails. By marking an email as spam helps the spam filters to learn about spam emails in a better way and detect future spams better.
• Last but not the least, keep your operating system, browser and other commonly used software updated with recent security patches. Configure proper firewalls. Use anti-malware solutions from trusted sources and keep them updated regularly.

AI, Machine Learning, Deep Learning and Cyber Security

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Many of us might have heard the terms AI, machine learning and deep learning. Some of us also might have heard that they can have a big impact on cyber security. What are AI, machine learning and deep learning actually? And, how can they improve cyber security? In this article we would discuss about that.

What is Artificial Intelligence ?

Artificial Intelligence or AI is the science and engineering of making a machine intelligent, so that it can perform tasks similar to those that require human intelligence. It can give machines the ability to learn without being explicitly programmed. For example, a machine can know about the facts about a specific situation and based upon that it can decide upon its action to achieve a goal. It can look at the previous steps of a game of chess and decide on what can be the best possible next move. Or a machine can know about the general facts of the world, facts about a particular situation and a statement of a goal and it can plan a strategy or sequence of actions using AI to achieve its goal.

Artificial Intelligence is widely used in many areas, like:

• Playing games like chess
• Speech Recognition
• Understanding natural language
• Computer vision
• Building expert systems

What is Machine Learning ?

Machine learning is a sub-field of AI that gives machines the ability to learn from data and make predictions based on that. For example, a machine can use machine learning to learn from a set of inputs and its corresponding outputs and based on that it can predict the output of a new input data. Applications of machine learning includes spam filtering, Optical Character Recognition, search engines, computer vision and cyber security.

There can be three types of machine learning algorithms:

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

What is Supervised Learning ?

In this technique, the machine is provided with a set of inputs and its corresponding outputs. The machine uses supervised learning to obtain general rules that map the inputs with the outputs. The algorithm typically iteratively makes predictions on the training input data and adjusts itself from the feedback. It stops when an acceptable level of performance is achieved. This is called supervised learning because the training dataset supervises the learning process.

What is Unsupervised Learning ?

In unsupervised learning, the machine is provided with only the input data with no labels on them. The goal is to learn the underlying structure or distribution in the data and predict outcome of similar input data based on that. For example, it can extract features on the input dataset and divide them into similarity groups, so that when a new data comes, it can predict its output based on the information. A common application can be in an ecommerce website, where machine learning can be used to divide the customers into segments and draw inferences based on that to use it in a marketing campaign.

In many applications, semi-supervised learning algorithm is used, where the machine uses both supervised and unsupervised algorithms to learn from the training datasets.

What is Reinforcement Learning ?

In reinforcement learning, the machine interacts with the dynamic environment to perform a certain goal. A good example can be playing a game of chess, where the machine can use machine learning to learn from the previous steps and decide on its next move. And, based on the user’s next move, it can again decide on its next action.

What is Deep Learning ?

There are several approaches of machine learning algorithms. One such approach is to use artificial neural network. An artificial neural network is a machine learning algorithm that is inspired by the structure and functional aspects of biological neural networks. The neurons in the neural network are connected to each other, through which data can propagate. In a simple case, there can be two sets of neurons – ones that receive the input signals and ones that send the output signals. Deep Learning uses several layers between the input layer and the output layer.

In Deep Learning, when an input is given to the input layer, the input layer processes the input and passes on a modified version of the input to the next layer. Each neuron in the neural network assigns a weighting to its input and the final output is determined by the total of those weightings.

A simple example of using deep learning can be recognizing a stop sign from an image. Attributes of the stop sign image like its octagonal shape, red color, letters used, size of the traffic sign etc are examined by the neurons and based on that each neuron gives a weighting. Depending on the weightings, the deep learning algorithm can come up with a probability vector whether the image can be a stop sign.

So, to summarize, machine learning is evolved from a sub-field of artificial intelligence. And, a sub-field of machine learning is deep learning. Falling hardware prices and the development of GPUs have contributed to the development of Deep Learning.

AI, Machine Learning, Deep Learning and Cyber Security

Let’s try to understand, how AI, machine learning and deep learning can be used to improve cyber security.

Traditional Malware Detection Techniques

There are several ways malware are detected using traditional anti-malware programs. Some most common of them are:

Signature Based Detection – In this technique, an unidentified piece of code is compared with a database of signatures of known malware. If a match is found, the new piece of code is identified as a malware. But, the problem with this approach is, signature based detection cannot detect new malware the signatures of which are not updated with the database. Moreover, sometimes it takes months to release signatures of newly found malware. And so, this technique is extremely inefficient in detecting malware especially Zero Day Threats and APTs.

Heuristic Techniques – In this technique, the unidentified piece of code is made to run and the behavioral characteristics of the new code is observed. Malware behavior is typically observed at runtime, once the code starts execution. So, the prevention mechanism gets delayed which makes it ineffective at times.

Sandbox – In sandbox solutions, the unidentified code is executed in a virtual environment and its behavior is observed to determine whether it can be a malware. This process is time consuming and ineffective for real-time protection. Moreover, the malware can stall its execution once it detects a virtual environment, which makes its detection challenging at times.

Malware Detection using AI, Machine Learning and Deep Learning

Machine Learning can be used in more effective malware detection. In this technique, a file’s behavior is observed to detect whether it can contain a malware. This is done by training the machine learning algorithm with the help of some manually selected features, that can determine whether the file is malicious or legitimate.

This is no doubt a better approach, but it has its own disadvantages. This technique requires human intervention to teach the machine the parameters, variables or features based on which malware detection can be done. And, to address that an advanced technique is used that uses deep learning to detect malware.

In this technique, a dataset of huge number of malicious and legitimate files are fed into the machine. The machine uses deep learning to self-learn the features necessary for malware detection. When the learning completes, the machine can detect any malicious file type. Also, threats can be detected in real time and potential threats can be blocked. This technique can be quite effective in detecting even Zero Day threats and APTs.

AI, machine learning and Deep Learning technologies are evolving day by day. And, if used properly, they can improve cyber security up to a great extent.