How to transform cybersecurity from reactive to proactive: The role of deep learning

Deep learning (DL) is an advanced subset of machine learning (ML) and is behind some of the most innovative and complex technologies today. We can witness the rapid development of artificial intelligence, machine learning and deep learning in almost every industry and experience benefits that were thought to be impossible just a few short years ago.

Rewritten content: Deep learning has made huge advances in the complexity of machine learning. Unlike machine learning, which may require human intervention to adjust the output layer if the results are wrong or unsatisfactory, deep learning can continuously learn and improve accuracy without human intervention. Multi-layered deep learning models can achieve surprising levels of accuracy and performance

The rise of deep learning models

For years, researchers have been developing complex artificial intelligence algorithms to achieve More advanced features. Through research work that closely mimics biological brains, more complex mathematical calculation methods have been developed, resulting in artificial neural networks (ANN). Simply put, ANN is composed of many nodes (or neurons), just like the human brain, that can pass and process information to each other in the network. In other words, it has the ability to learn and adapt

The development of this technology has been slow due to its requirements. Achieving this achievement requires three elements: large amounts of data, more advanced algorithms, and vastly increased processing power. This processing power comes in the form of a graphics processing unit (GPU). A GPU is a computer chip that can significantly accelerate the deep learning computing process and is a core component of artificial intelligence infrastructure. It can perform multiple computing tasks simultaneously, speed up the learning process of machine learning, and handle large amounts of data with ease. Powerful GPUs combined with cloud computing can effectively reduce the time required to train deep models from weeks to hours

Disadvantages of GPU performance

GPUs for such high-performance computing Power consumption is staggering and expensive. Training a single final version of some GPU models may require more power than 80 homes use in a year.

In addition, large-scale data storage centers around the world have a serious impact on the environment due to energy and water consumption and greenhouse gas emissions. Part of solving this problem is improving data quality through deep learning, rather than relying solely on large amounts of data. As artificial intelligence continues to develop, sustainability plans must become a globally shared platform

The more layers, the deeper we dive

For humans, the deeper we delve The more research data and empirical examples you have on a topic, the more you can create a practical and comprehensive knowledge base. Artificial neural networks are composed of three types of layers. The first input layer provides the network with an initial pool of data. The last layer is the output layer, which generates all results for a given data input. In between these two is the most important hidden layer. These middle layers are where all the computational processes are performed

At least three layers qualify as deep learning, but the more layers there are, the deeper the learning becomes to inform the output layer. Deep learning layers have different functions that act on the data as it flows through each layer in a specific order. With each additional layer, more details and features can be extracted progressively from the data set. This ultimately results in the network output predicting or stating potential outcomes, predictions and conclusions.

The Importance of Deep Learning for Accuracy and Prevention

AI automation and deep learning models are key elements in the fight against cybercrime, while also providing important protection against ransomware upgrades. Deep learning models are able to identify and predict suspicious behavior and understand the characteristics of potential attacks to prevent the execution of any payload or encryption of data

Intrusion detection and prevention systems generated by artificial neural networks compared to machine learning Smarter, more accurate, and significantly lower false alarm rates. Rather than relying on attack signatures or memorizing lists of known common attack sequences, artificial neural networks continuously learn and update to identify any system activity that indicates malicious behavior or the presence of malware.

Cybersecurity teams have always viewed external attacks as a primary concern, but malicious internal activity is on the rise. According to Ponemon's 2022 Cost of Insider Threats: Global Report, insider threat incidents have increased by 44% over the past two years, and the cost per incident has increased by more than a third to $15.38 million

Security teams are increasingly leveraging User and Entity Behavior Analytics (UEBA) to thwart insider threats. Deep learning models can analyze and learn normal employee behavior patterns over time and detect when anomalies arise. For example, it can detect any out-of-hours system access or data breach and send alerts Huge difference. A reactive approach protects against threats after they enter the network to exploit systems and steal data. Through deep learning, vulnerabilities and malicious activities can be identified and eliminated before they are exploited, thereby achieving the goal of proactively preventing and eliminating threats

While automated and multi-layered deep learning cybersecurity solutions have greatly improved security defenses, this technology can also be exploited by both sides of cybercrime

Escalating AI innovations require protective legislation

In the field of cybersecurity, the development of artificial intelligence solutions like deep learning to combat sophisticated cyber enemies has outpaced the ability of regulatory agencies to limit and control. At the same time, enterprise defense measures may also be exploited and manipulated by malicious attackers

The consequences of uncontrolled artificial intelligence technology in the future may be devastating on a global scale. If our technology gets out of hand, without legislation to maintain order, human rights and international security, this could become an escalating battleground between good and evil.

The ultimate goal of cybersecurity is to move beyond passive detection and response to proactive protection and threat elimination. Automation and multi-level deep learning are key steps in this direction. Our challenge is to maintain reasonable control and stay one step ahead of our cyber enemies

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