How Neural Networks can revolutionize Cybersecurity

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The texts in this article were partly generated by artificial intelligence and corrected and revised by us.

Introduction

The digital world is a complex and constantly evolving landscape, and the threat of cyberattacks is growing with it. While traditional security measures remain effective to some extent, they are no longer sufficient to keep pace with the increasing sophistication and speed of modern threats. This is where we can apply neural networks to enhance cybersecurity in a number of different areas, thus increasing overall IT security.

Given that neural networks are already being utilised in cybersecurity, it is probable that they will play a pivotal role in the following areas: threat detection and defence, malware analysis, security automation, predictive analytics and user behaviour analysis.

This introduction will examine the specific ways in which neural networks can be used to improve cybersecurity, explore their potential and limitations, and finally examine their potential impact on the future of digital security.

In the following article, we will use the term “attackers” in a negative context. While this term is often associated with malicious intentions, we would like to take this opportunity to also refer to white-hat hackers, who have no malicious intent. However, the focus here is on securing IT systems against any unauthorised access.

(Modern) Cybersecurity as a challenge

Cybersecurity as a field of application for modern IT security involves a whole range of challenges. Here is a list of some of the challenges: ¹ ² ³ ⁴

The rapid development of new technologies in the IT sector, particularly in web development, is leading to the emergence of new and previously undiscovered security vulnerabilities on a regular basis.
The further development of attack vectors: In order to identify and exploit new attack surfaces, attackers are constantly trying to develop new approaches or methods. Examples of this include ransomware, pishing, social engineering and (zero-day) exploits.
The development of more advanced tools and organisational structures is also a factor. As well as further development of tools to defend against attacks, the attackers’ tools are also evolving to enable them to carry out ever more effective attacks. In addition, hacker groups have repeatedly emerged in recent years, which share expertise and together can tie up more of the victims’ resources.
The potential for worldwide attacks is a significant concern in the current digital landscape. The ability to launch attacks from any location at any time makes it challenging to identify and respond to the perpetrators.

In addition to the aforementioned factors, which predominantly contribute to the challenges in cybersecurity, there are several other reasons for difficulties. Traditionally, these reasons are not primarily the result of external malicious actions, but are frequently triggered from within an organisational structure and its prioritisation. The following examples illustrate this: ⁵ ⁶

The high level of interconnectivity of modern IT infrastructure within organisations makes it challenging to monitor and isolate individual devices in the event of an incident. The issue is the creation of strong dependencies within the IT infrastructure, which could be exploited by attackers.
In addition to the technical aspects of cyber-attacks, there is also a human element. Attackers may use phishing emails, social engineering campaigns or accidental data leaks to cause significant incidents in an organisation.
The absence of established security standards in the IT sector results in a lack of a common foundation for IT security.

In the meantime, preliminary steps are being taken to establish general principles. One example of this is OWASP, which we presented in this article.

The lack of personnel, experience or software/hardware can make it challenging to implement security measures within the necessary framework.
The regulatory framework regarding IT security is subject to constant evolution, which can present challenges in the implementation of IT security measures.

Neural networks in cybersecurity

In addition to the issues already outlined, there is a new challenge: the use of neural networks for malicious purposes. This has opened up a range of possibilities for attackers, including the circulation of malicious content, the enhancement of existing tools, and even the development of new methods. The automated generation of phishing emails has attracted significant attention, as have the use of deepfakes and generated “social engineering” campaigns. ³ ⁴

A case study demonstrating the potential risks associated with the misuse of neural networks can be observed in the context of the US election.

However, neural networks can also be part of the solution. For example, a discriminator (usually the defender with the ability to recognise deep fakes) can exist alongside a generator (usually the attacker with potential deep fakes), as is the case with a Generative Adversarial Network (GAN).

We have selected the example of GANs because they illustrate a crucial issue: the capacity to learn from others. Just as a generator develops the ability to create high-quality deepfakes over time (with feedback from the discriminator), a defender can enhance their capabilities by developing effective methods for defending against malicious techniques. Since we cannot restrict the use of neural networks to prevent attackers from exploiting this capability, it is necessary to leverage their techniques in a defensive manner.

Areas of application within cybersecurity

The following section will examine a number of exemplary application areas that could benefit from the use of neural networks in the context of cybersecurity.

Identifying cyberattacks

As cyber attacks become more complex and varied, the analysis and detection of corresponding attacks is also becoming increasingly complex. It is crucial to prevent unnoticed intrusions, which can be achieved through the use of machine learning methods for anomaly detection. This involves analysing network activities to identify unusual patterns, if any exist. A neural network can observe ‘usual’ procedures in a network over time and flag any differences as potentially harmful, possibly preventing an attack from taking any negative effect. ⁷ ⁸

Identifying malware

In addition to the actual detection of currently active threats, cybersecurity also relies on identifying previously inactive attack vectors, such as those that may be embedded in delivered executable files containing malware. It is noteworthy that an entire field of research has developed around this issue, with the explicit goal of implementing the most accurate malware classification possible.

This field of research is focused on analyzing the corresponding data. Due to the particular homogeneity of the data in this area, rather unusual structures for neural networks are used, such as graph neural networks or convolutional neural networks. However, the latter have been increasingly replaced by transformer architectures due to better performance and hardware utilization in recent years. ⁹ ¹⁰ ¹¹ ¹²

For further information on this topic, please refer to the Nvidia presentation, which provides a well structured overview of this matter.

Identifying pishing

Furthermore, we can leverage neural networks to establish an additional protective barrier against phishing attacks. Given that phishing is typically conducted via email, we have the advantage of interacting primarily with text.

To create this additional barrier, a variety of methods can be employed, all of which can be integrated into a spam filter. One common method is to search for specific keywords associated with malicious content identified by the system. Alternatively, machine learning methods can also be used to identify phishing URLs. ¹³ ¹⁴ ¹⁵

Predictive analytics

Building on the sub-areas already mentioned, we can also leverage neural networks in the field of predictive analytics to more effectively assess potential future threats.

There are several sub-areas in which this technology can have an impact, as follows: ¹⁶ ¹⁷ ¹⁸

The ability to recognize the severity of an attack
Identifying system vulnerabilities
Prioritizing specific remediations of vulnerabilities in the system
The detection of advanced persistent threats
Identifying potential developments in the aforementioned areas.

Possible risks

As we have just seen, neural networks are capable of making great progress in the field of cybersecurity. However, these methods also present a number of disadvantages that need to be identified and mitigated. In this section, we will examine the most significant of these disadvantages in order to develop effective systems.

Adversarial Attacks

Adversarial attacks involve adapting the input data for a neural network in a way that causes the network to produce incorrect results. An attacker uses knowledge about the training data set or other limitations to obtain corresponding results and therefore carry out an undetected attack on a target system.

It is challenging to design a neural network that is resistent to adversarial attacks. This would require a model to generate perfect outputs for every combination of input data, which is not a realistic expectation in practice. However, there are ways to mitigate this risk.

To avoid such problems, it is essential that attackers do not obtain any information about the training or the data set used. A robust security system should also be designed to be as a multi-layered solution, with the ability to compensate for errors in one model by another. In the field of data science, this approach is often characterized as a mixture of experts. ¹⁹ ²⁰

Data privacy concerns

In order to train a neural network in the context of cybersecurity, a wide range of information is required to generate a training set. The following types of information can be used to train such a model:

Personal identification information, including name, email address, phone number, and home address.
Sensitive personal data, including health information, financial records, and biometric data.
User profiles may be constructed based on browser data, search queries, purchasing behavior, and location history.

This data may be transmitted indirectly or pseudonymized, rather than transmitted directly via network data. However, this information can pose problems for individuals because it can potentially be used to commit identity theft or other criminal offenses.

It is also important to consider how the necessary training data is stored and what security measures are in place to prevent unauthorized access. These factors can lead to increased costs or time requirements for the development process.

One example of problematic data protection handling can be found in the implementation of the ‘Windows Recall’ feature.

Overfitting

As is typically the case with neural networks, overfitting is a common issue. However, in the context of safety-critical applications such as the identification of cyberattacks, it can be particularly problematic. We will now present a few examples to illustrate this point.

An intrusion detection system that has been overfitted on the training data will be particularly effective at recognizing already known types of attack. However, this system will have difficulty generalizing attack types due to this specialization, making it more challenging or impossible to identify new types of attack. This also affects the detection of variations of specific malware or phishing. ²¹ ²²
A user profile data analysis model that suffers from overfitting is more likely to produce false positives or false negatives due to greater specialization, which reduces the model’s overall usefulness.
A model that suffers from overfitting is more susceptible to adversarial attacks than a model without overfitting. ²²

Please note that although the title of the last source is “Robustness to adversarial examples can be improved with overfitting,” the source initially supports the described behavior. The source subsequently provides a more detailed approach of how overfitting could be integrated with enhanced generalization to enhance resilience to adversarial attacks. However, to the best of our knowledge, such methodologies have not yet been developed.

Resource intensive calculations

In addition to the financial implications of implementing such a network, which will likely require frequent data processing based on incoming data, we must also consider two other crucial aspects of cybersecurity.

From a cybersecurity standpoint, latency represents a significant challenge. If the inference on the network takes too long, the system faces a problem: should an incoming call be delayed until the calculation is complete? Should an important video call not be accepted until the neural network has confirmed that it is not a malicious call? Especially if the availability of the model’s execution system is not set correctly and latencies of up to several minutes occur, this can lead to a significant loss of productivity or even customers, depending on how urgent customer inquiries are.

This also raises the issue of scalability. The system used to host the model must be scalable enough to ensure the highest possible availability at all times. This requires a large amount of potentially expensive hardware, which must be both obtained and maintained. On the other hand, the regular costs should not be too high so as not to jeopardize other organizational commitments.

TL;DR

Neural networks are becoming an invaluable asset in the fight against cyber threats. Their capacity to discern intricate patterns from vast data sets makes them an optimal choice for identifying and neutralising a multitude of security vulnerabilities. By analysing network traffic, user behaviour and system logs, neural networks can identify irregularities and anticipate potential attacks before they occur. This proactive approach allows security teams to respond rapidly and effectively, thereby minimising the impact of any security breaches. Furthermore, neural networks can be trained to detect and block malicious software such as ransomware and phishing attacks by analysing their unique characteristics. The ability of neural networks to learn and adapt makes them a valuable technology in the constantly evolving field of cybersecurity. By leveraging the distinctive capabilities of these technologies, we can foster a safer digital environment.