Malware: One of the Most Common Types of Cyberattacks

February 3, 2022

Cybercrime is increasing every year as attackers are getting better at attacking. Cybercriminals try to exploit the weaknesses in an organization’s cybersecurity solutions, policies, or procedures.

Attackers can infiltrate an IT system in many ways, but most cyberattacks rely on pretty similar techniques. The most common type of cyberattack is malware.

What Is Malware?

Malware is a type of application used to obtain unauthorized access to your data. Malware can do things such as create persistent network access, access information such as your personal account or banking details, or cause disruptions on the machine (e.g., locking up your screen, forcing you to reboot).

Malicious software can fall into many categories. Trojans are designed to steal bank information, and malware contains spyware that is installed on your PC. One of the most notable examples of malware designed to extort the victim is ransomware - a kind of malicious software that infects your computer or mobile device then encrypts some files, making them unreadable until you pay the ransom! 

Objectives of the Malware

Malware is created with a specific objective. The objective of malware is limited only by the imagination of the one behind the said malicious software. 

The following are some of the most common objectives observed in malware:

  • Disrupt operations

There are several ways in which criminals can sabotage a company from the inside out. They can infect systems with malware, corrupt critical OS files, and disrupt or even completely shut down computer programs that provide specific functionality. Another typical attack called a distributed denial-of-service (DDOS) involves taking control of multiple computers via malware, which is used to overwhelm one machine at a time by flooding the system with more traffic than it can handle all at once.

  • Exfiltrate information

It is common for cybercriminals to try and steal personal information, credit card numbers, payment details, and credentials. Malware focused on this type of theft can be greatly expensive to a company, person, or government target that falls victim. 

  • Demand payment

Some viruses are focused on directly extorting money from the target. Many different kinds of malware pop up every day, thinking they can trick you into giving them your hard-earned cash. One common type of unethical software is scareware. Scareware takes advantage of innocent users by essentially lying to them or demonstrating some type of intimidating behavior that scares the target into paying for something they don’t actually need. 

Best Practices to Avoid Malware

The following are several best practices or precautions to take when preventing or mitigating your company’s chances of falling victim to a malware attack.

  1. Creating regular backups

Having a reliable backup process in place to protect against data loss is a great way to ensure your company can recover from a cyberattack. Regularly run a backup of the most critical data to a network location off-site, a cloud file storage service, or a tape drive stored in a vault.

  1. Ensuring network security

Controlling access to your organization’s network is an excellent idea for many different reasons. Using proven technology and methodologies will help minimize the attack “surface” your organization exposes, such as using a firewall, an intrusion prevention system, or an intrusion detection system. In addition to using firewalls and other security measures, some companies also use extreme methods such as physical system isolation.

  1. Perform regular website audits

Regularly scanning your organization’s websites can keep your organization secure, protect your users, and also help to protect customers who are browsing your sites.

  1. Use antivirus software

Antivirus solutions are designed to prevent security breaches in the form of malware, Trojans, and other harmful software programs. Installing this solution will remove infections or even stop them in their tracks before they have a chance to cause significant problems to the current configuration of the user’s PC. It’s essential to keep it updated with the latest definitions from the vendor.

  1. Continuous user education

Keeping your users and employees aware of the best ways to avoid malware attacks can do wonders for keeping your systems safe. Train them in things like not running random unknown software, or identifying phishing emails, etc. Realistic, unannounced phishing tests are among the best ways to determine how well your business handles these situations.

To prevent malware attacks (or any other hacking or unauthorized activity), make sure you are always running the latest, most recent version of your operating system, antivirus software, and all web browsers. If there is a zero-day vulnerability for a particular application, make sure to disable or uninstall it immediately before hackers take advantage of it. Additionally, if you know your company's acceptable use policy regarding technology, make sure everyone follows those policies.

Malware can come in various forms and attack tactics, but there are ways to help harden your position against this threat. For example, by making thoughtful preparations and implementing process improvements alongside a continued effort to educate users, your organization can stay on top of the ravages of malware over time.

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A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

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Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

  • Highly calibrated cameras that take a photograph of the image from various angles.
  • Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

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A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

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