The rise in popularity and the large amount of improvements done to Machine Learning (ML) resulted in the emergence of a new type of attack called model extraction attack. Model extraction attacks are privacy attacks, which aim to extract information about a victim model or even steal its functionality. These types of attacks are being heavily researched, however, it is very hard to perform comparisons between the proposed papers. In this work, we present MET, which implements state-of-the-art model extraction attacks on arbitrary ML models and datasets. Using the tool, we performed a comprehensive comparison between the implemented attacks to see how they perform under different settings. Our results show that in the case of black-box scenarios, the attacks perform similarly. Based on the results, we propose and implement improvements for some of the attacks both in terms of speed and performance.

Url: https://dspace.cvut.cz/handle/10467/95288

Mobile devices are at risk of cyber attacks, and the most dangerous attacks on mobile phones are Remote Access Trojans (RAT). RAT are malicious programs that allow for unauthorized remote access of the infected phones to see their resources. Detecting Android RAT in the phone is a challenging task, that is why we propose to detect it in the network traffic. However, it is hard to access the network traffic in the phone, since there is no easy way to capture its traffic. More importantly, it's very hard or even impossible to have applications in the phones that can protect it from these attacks, leaving the detection in the network as the only option. In this bachelor thesis we research this problem of detecting RATs in phones by (1) creating an Android RATs’ dataset of real infected phones, (2) analysing RATs' network traffic behaviours, (3) proposing new detections model, and (4) implementing this detection module for RATs in a open-source Python-based intrusion detection system called Slips.

Active Directory (AD) is a crucial element of large organizations, given its central role in managing access to resources. However, since AD is used by all users in the organization, it is hard to detect attackers. We propose to generate and place fake users (honeyusers) in AD structures to help detect attacks. However, not any honeyuser will attract attackers. Our method generates honeyusers with a Variational Autoencoder that enriches the AD structure with well-positioned honeyusers. Our model first learns the embeddings of the original nodes and edges in the AD, then it uses a modified Bidirectional DAG-RNN to encode the parameters of the probability distribution of the latent space of node representations. Finally, it samples nodes from this distribution and uses an MLP to decide where the nodes are connected. The model was first evaluated by the similarity of the generated AD with the original AD, second by the positions of the new nodes, and finally making real intruders attack the AD structure enriched with honeyusers to see if they selected the honeyusers. Results show that our machine learning model is good enough to generate well-placed honeyusers for existing AD structures so that intruders are lured into them.

Mobile devices are at risk of cyber attacks, and the most dangerous attacks on mobile phones are Remote Access Trojans (RAT). RAT are malicious programs that allow for unauthorized remote access of the infected phones to see their resources. Detecting Android RAT in the phone is a challenging task, that is why we propose to detect it in the network traffic. However, it is hard to access the network traffic in the phone, since there is no easy way to capture its traffic. More importantly, it's very hard or even impossible to have applications in the phones that can protect it from these attacks, leaving the detection in the network as the only option. In this bachelor thesis we research this problem of detecting RATs in phones by (1) creating an Android RATs’ dataset of real infected phones, (2) analysing RATs' network traffic behaviours, (3) proposing new detections model, and (4) implementing this detection module for RATs in a open-source Python-based intrusion detection system called Slips.

The goal of this work is to propose a protocol for sharing data in a decentralized network of peers, where each node gains reputation for their actions. Information from nodes with low reputation may be discarded, while nodes with high reputation will be heard. This serves as a protection, because malicious nodes would first have to gain trust of the network before they could affect it.

There are multiple approaches to compute reputation, but they rely mostly on adherence to the protocol, uptime and other simple features. The trust model used by the Sality botnet simply measures how many “good” interactions a node had with its neighbor. There are numerous attacks that an adversary can use to gain trust of the network. In this thesis, the trust model will not only use data from the protocol itself, but also network monitoring and statistics provided by SLIPS. We will analyze different trust models and options to attack them. A new trust model that uses data from SLIPS will be proposed, and its performance will be evaluated. Finally, the model will be implemented as a module inside SLIPS, and will enable sharing said network data with other nodes running SLIPS.

Civil society members face threats not only in the physical world but in cyberspace. Their critical work leaves them in a permanent risk of surveillance and abuse. Mobile phones are vital for their activities, however these are often vastly unprotected. The lack of a standardized method to measure and analyze these risks hinders the efforts to protect them. The Civilsphere Project at the Czech Technical University in Prague created the Emergency VPN (EVPN) to help civil workers at risk. This free service helps discover data leaks or malware infections through network traffic analysis of mo- bile devices. The goal of this thesis is to create the first standardized risk measurement score for mobile phones at risk. In order to do so we processed 65 packet captures from the civil society along with the manual assessment reports done by Civilsphere analysts, creating a unique dataset suitable for further analysis. We assessed data leaked from mobile devices to identify potential risks and security threats. We developed a new method to standardize the severity rating and created a metric describing the nature of the reported data leaks. While none of the analyzed devices showed indications of malware presence, we discovered that they leak a lot of data that puts the civil workers at risk, most commonly the user’s location.

Bachelor Thesis

This thesis proposes to design, implement and test a machine learning improvement of Stratosphere IPS which aggregates the partial detections of hosts and classifies them using the XGBoost algorithm to improve the overall performance of the tool. Our method is based on an additional layer of abstraction called Source Address layer which collects the partial data and pre-processes it or the classifier. Compared to the first version of Stratosphere IPS proposed extension results in 40% increase in accuracy and 26% improvement in the False Positive rate.

Master Thesis

The precise identification of users in the network at different moments in time is a well known and difficult problem. Identifying users by their actions (and not their IP addresses) allows administrators to apply policy controls on users, to find intruders that are impersonating legitimate users, and to find anomalous user behaviors that could be due to malware infections. More importantly, the behavioral analysis of users actions raises important moral questions about the power to identify users in unknown networks. This thesis explores this question by trying to identify users by converting the user's behavior into user's profiles. These profiles are time-dependent and they have dozen of features.

Bachelor Thesis

There are many malware families and every each of them has some unique features. The aim of this work is to focus on detecting malicious behavior using leaving network communication. Our hypothesis is that this malicious communication has sequential behavioral patterns. We present a new graph representation of leaving network communication using (IP address, port, protocol)-triplets as vertices.

Master Thesis

This project has two primary goals: First, to help analysts by means of a web interface, in evaluating the weblogs to better find and process the information. Second, to create a machine learning method that can identify domains which share some similarity in their WHOIS Information. Our algorithm can work as a WHOIS classification of similar domains also called WHOIS similarity distance. The conclusions of our research are: First, ManaTI can increase the speed of the security analysts by a factor of 3.4. Second, the WHOIS information of related domains has quantifiable similarities that make possible an accurate comparison. Third, there are WHOIS fields which are more important for relating domains than others. Finally, the accuracy of finding related domains using a linear model classifier based on the WHOIS Similarity Distance algorithm is around 98%.