Large Language Models (LLMs) have gained widespread popularity across diverse domains involving text generation, summarization, and various natural language processing tasks. Despite their inherent limitations, LLM-based designs have shown promising capabilities in planning and navigating open-world scenarios. This paper introduces a novel application of pre-trained LLMs as agents within cybersecurity network environments, focusing on their utility for sequential decision-making processes.
There are very few SotA deception systems based on Large Language Models. The existing ones are limited only to simulating one type of service, mainly SSH shells. These systems - but also the deception technologies not based on LLMs - lack an extensive evaluation that includes human attackers. Generative AI has recently become a valuable asset for cybersecurity researchers and practitioners, and the field of cyber-deception is no exception. Researchers have demonstrated how LLMs can be leveraged to create realistic-looking honeytokens, fake users, and even simulated systems that can be used as honeypots.
Emerging studies demonstrate that machine learning (ML) has the potential to improve the detection capabilities of network intrusion detection systems (NIDS) against evolving cyber threats. However, recent adversarial ML (AML) studies have revealed critical ML vulnerabilities. This paper presents innovative multistage red-teaming techniques to evaluate the robustness of ML-NIDS in real-world adversarial settings.
Domain Generation Algorithms (DGAs) are malicious techniques used by malware to dynamically generate seemingly random domain names for communication with Command & Control (C&C) servers. Due to the fast and simple generation of DGA domains, detection methods must be highly efficient and precise to be effective. Large Language Models (LLMs) have demonstrated their proficiency in real-time detection tasks, making them ideal candidates for detecting DGAs. Our work validates the effectiveness of fine-tuned LLMs for detecting DGAs and DNS exfiltration attacks. We developed LLM models and conducted comprehensive evaluation using a diverse dataset comprising 59 distinct real-world DGA malware families and normal domain data. Our LLM model significantly outperformed traditional natural language processing techniques, especially in detecting unknown DGAs. We also evaluated its performance on DNS exfiltration datasets, demonstrating its effectiveness in enhancing cybersecurity measures. To the best of our knowledge, this is the first work that empirically applies LLMs for DGA and DNS exfiltration detection.
Understanding cybercrime communications is paramount for cybersecurity defence. This often involves translating communications into English for processing, interpreting, and generating timely intelligence. The problem is that translation is hard. Human translation is slow, expensive, and scarce. Machine translation is inaccurate and biased. We propose using fine-tuned Large Language Models (LLM) to generate translations that can accurately capture the nuances of cybercrime language. We apply our technique to public chats from the NoName057(16) Russian-speaking hacktivist group. Our results show that our fine-tuned LLM model is better, faster, more accurate, and able to capture nuances of the language. Our method shows it is possible to achieve high-fidelity translations and significantly reduce costs by a factor ranging from 430 to 23,000 compared to a human translator.
Honeypots are essential tools in cybersecurity for early detection, threat intelligence gathering, and analysis of attacker's behavior. However, most of them lack the required realism to engage and fool human attackers long-term. Being easy to distinguish honeypots strongly hinders their effectiveness. This can happen because they are too deterministic, lack adaptability, or lack deepness. This work introduces shelLM, a dynamic and realistic software honeypot based on Large Language Models that generates Linux-like shell output. We designed and implemented shelLM using cloud-based LLMs. We evaluated if shelLM can generate output as expected from a real Linux shell. The evaluation was done by asking cybersecurity researchers to use the honeypot and give feedback if each answer from the honeypot was the expected one from a Linux shell. Results indicate that shelLM can create credible and dynamic answers capable of addressing the limitations of current honeypots. ShelLM reached a TNR of 0.90, convincing humans it was consistent with a real Linux shell. The source code and prompts for replicating the experiments have been publicly available.
The domain of digital networking has witnessed a lot of advancements, one of which is peer-to-peer (P2P) networks for file sharing. These networks gained significant traction with the success of Napster, BitTorrent, Gnutella, and Kazaa [13]. P2P networks offer enhanced scalability and resilience by eliminating the need to go through a server [24]. While the P2P network has transformed the file-sharing landscape, the main challenge these networks face is the ability to determine their peers’ trustworthiness accurately.
Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, but their potential in cybersecurity remains largely unexplored. Despite their inherent limitations, LLM-based designs have shown promising ability in planning and navigating open-world scenarios. This paper investigates the application of pre-trained LLMs as agents in network security environments, a domain traditionally dominated by reinforcement learning (RL) approaches. We introduce a novel method that leverages LLMs for sequential decision-making in cybersecurity scenarios, that is flexible and does not need re-training to adapt to new scenarios.
Deciding which XAI technique is best depends not only on the domain, but also on the given task, the dataset used, the model being explained, and the target goal of that model. We argue that the evaluation of XAI methods has not been thoroughly analyzed in the network security domain, which presents a unique type of challenge. While there are XAI methods applied in network security there is still a large gap between the needs of security stakeholders and the selection of the optimal method. We propose to approach the problem by first defining the stack-holders in security and their prototypical tasks. Each task defines inputs and specific needs for explanations. Based on these explanation needs (eg understanding the performance, or stealing a model), we created five XAI evaluation techniques that are used to compare and select which XAI method is best for each task (dataset, model, and goal). Our proposed approach was evaluated by running experiments for different security stakeholders, machine learning models, and XAI methods. Results were compared with the AutoXAI technique and random selection. Results show that our proposal to evaluate and select XAI methods for network security is well-grounded and that it can help AI security practitioners find better explanations for their given tasks.
Due to the proliferation of malware, defenders are increasingly turning to automation and machine learning as part of the malware detection toolchain. However, machine learning models are susceptible to adversarial attacks, requiring the testing of model and product robustness. Meanwhile, attackers also seek to automate malware generation and evasion of antivirus systems, and defenders try to gain insight into their methods. This work proposes a new algorithm that combines Malware Evasion and Model Extraction (MEME) attacks. MEME uses model-based reinforcement learning to adversarially modify Windows executable binary samples while simultaneously training a surrogate model with a high agreement with the target model to evade. To evaluate this method, we compare it with two state-of-the-art attacks in adversarial malware creation, using three well-known published models and one antivirus product as targets. Results show that MEME outperforms the state-of-the-art methods in terms of evasion capabilities in almost all cases, producing evasive malware with an evasion rate in the range of 32–73%. It also produces surrogate models with a prediction label agreement with the respective target models between 97–99%. The surrogate could be used to fine-tune and improve the evasion rate in the future.
DNS over HTTPS (DoH) is one of the standards to protect the security and privacy of users. The choice of DoH provider has controversial consequences, from monopolisation of surveillance to lost visibility by network administrators and security providers. More importantly, it is a novel security business. Software products and organisations depend on users choosing well-known and trusted DoH resolvers. However, there is no comprehensive study on the number of DoH resolvers on the Internet, its growth, and the trustworthiness of the organisations behind them. This paper studies the deployment of DoH resolvers by (i) scanning the whole Internet for DoH resolvers in 2021 and 2022; (ii) creating lists of well-known DoH resolvers by the community; (iii) characterising what those resolvers are, (iv) comparing the growth and differences. Results show that (i) the number of DoH resolvers increased 4.8 times in the period 2021–2022, (ii) the number of organisations providing DoH services has doubled, and (iii) the number of DoH resolvers in 2022 is 28 times larger than the number of well-known DoH resolvers by the community. Moreover, 94% of the public DoH resolvers on the Internet are unknown to the community, 77% use certificates from free services, and 57% belong to unknown organisations or personal servers. We conclude that the number of DoH resolvers is growing at a fast rate; also that at least 30% of them are not completely trustworthy and users should be very careful when choosing a DoH resolver.
Malware authors constantly obfuscate their files and defenders regularly develop new techniques to detect them. Given this cat-and-mouse game, specialized obfuscation services have appeared in the cybercrime industry. These services allow malware authors to obfuscate their code for a fee. This study investigates an automated obfuscation-as-a-service platform for Android applications and yields unique insights on the technical difficulties and business reality of those behind such a specialized service. The service investigated was found to be average in quality, mainly using known obfuscation techniques, and generating obfuscated applications that were still detected by anti-viruses. It had a small clientele of large-scale attackers who used the service to decrease anti-virus detections of highly malicious applications, thus increasing their chances of compromising devices. Depending on the price bundles considered, operators offering the service were estimated to have made a minimum revenue ranging from USD 5,100 (conservative) to USD 61,160 (optimistic) for a six-month operation. This study illustrates that even though obfuscation-as-a-service is a market niche, taking advantage of the value added from this specialization is not effortless nor easily accessible to everyone involved in cybercrime.
In the last three decades there have been significant changes in the cybercrime world in terms of organization, type of attacks, and tools. Remote Access Trojans (RAT) are an intrinsic part of traditional cybercriminal activities but they have become a standard tool in advanced espionage and scams attacks. The overly specialized research in our community on Remote Access Trojans has resulted in a seemingly lack of general perspective and understanding on how RATs have evolved as a phenomenon. This work presents a new generalist perspective on Remote Access Trojans, an analysis of their growth in the last 30 years, and a discussion on how they have become a commodity in the last decade. We found that the amount of RATs increased drastically in the last ten years and that nowadays they have become standardized commodity products that are no very different from each other.
Honeypots have been a long-established form of passive defense in a wide variety of systems. They are often used for the reliability and low false positive rate. However, the deployment of honeypots in the Active Directory (AD) systems is still limited. Intrusion detection in AD systems is a difficult task due to the complexity of the system and its design, where any authenticated account is able to query other entities in the system. Therefore, the positioning of the honeypot in such structures brings two main con trains: (i) the placement has to be organic, with similar properties to other, real entities in the structure, and (ii) the placement must not give away the nature of the honeypot to the attacker.
Ensemble learning techniques have been successfully proposed and used to improve threats detection in cybersecurity. These techniques usually improve the detection results by combining algorithms that together have less errors. However there has not been any ensemble learning algorithm used to classify network flows when several methods are used to give individual detections for each of the flows.
El crecimiento vertiginoso de nuevas tecnologıas, trae aparejado el crecimiento de aplicaciones maliciosas. Estas aplicaciones hacen uso de los recursos de los dispositivos infectados para realizar actividades ilıcitas, enviar mails de forma masiva (spam) o minar para obtener criptomonedas. Para minar, se requiere grandes capacidades de cómputo Las botnets pueden ser consideradas como un tipo de de aplicación de computación distribuıda. La palabra botnet significa red de robots. Es un tipo de malware, instalado en una computadora que ha sido infectada, con la habilidad de auto propagarse hacia otras máquinas. Todas las computadoras infectadas conforman la “red de bots”, o botnet. Este tipo de malware utiliza los recursos de la computadora infectad (CPU, RAM, ancho de banda), para comunicarse con su Botnet Master, que es quien le da órdenes. El presente trabajo es un estado del arte, se analiza el comportamiento de las botnets como aplicaciones de computación distribuıda. Se considera el comportamiento a nivel de consumo de recursos de los dispositivos que son infectados por el malware, en particular los miners.
A normal computer infected with malware is difficult to detect. There have been several approaches in the last years which analyze the behavior of malware and obtain good results. The malware traffic may be detected, but it is very common to miss-detect normal traffic as malicious and generate false positives. This is specially the case when the methods are tested in real and large networks. The detection errors are generated due to the malware changing and rapidly adapting its domains and patterns to mimic normal connections.
Through the analysis of a long-term botnet capture, we identified and modeled the behaviors of its C&C channels. They were found and characterized by periodicity analyses and statistical representations. The relationships found between the behaviors of the UDP, TCP and HTTP C&C channels allowed us to unify them in a general model of the botnet behavior. Our behavioral analysis of the C&C channels gives a new perspective on the modeling of malware behavior, helping to better understand botnets.
Hack me-do. Deploying an IoT Malware Laboratory to Analyze Malicious Behavior. María José Erquiaga (Universidad Nacional de Cuyo); Sebastián García (CTU University, ATG Group)
In this paper we describe the main ensemble learning techniques and their application in the cybersecurity threats detection. The state of the art in the use of ensemble learning techniques is presented here as an alternative to the current intrusion detection mechanisms, analyzing their advantages and disadvantages. We propose to incorporate ensemble learning to SLIPS [3], a behavioral-based intrusion detection and prevention system that uses machine learning algorithms to detect malicious behaviors, to obtain better results, taking advantage of the benefits of the SLIPS classifiers and modules. As part of this work we extend ensembling by considering algorithms from different domains (not machine learning domains), as Thread Intelligence. As a first stage of this project, performance tests of ensemble learning algorithms were performed to detect malware from flows evaluating its accuracy. The results of these tests are presented here, as well as the conclusions obtained and the future work.
