RISS

Resilient Information Systems Security

A Formal Approach to Analyzing Cyber-Forensics Evidence

Erisa Karafili–News, Publications–

Erisa Karafili’s paper “A Formal Approach to Analyzing Cyber-Forensics Evidence” was accepted at the European Symposium on Research in Computer Security (ESORICS) 2018. This work is part of the AF-Cyber Project, and was a joint collaboration with King’s College London and the University of Verona.

Title: A Formal Approach to Analyzing Cyber-Forensics Evidence

Authors: Erisa Karafili, Matteo Cristani, Luca Viganò

Abstract: The frequency and harmfulness of cyber-attacks are increasing every day, and with them also the amount of data that the cyber-forensics analysts need to collect and analyze. In this paper, we propose a formal analysis process that allows an analyst to filter the enormous amount of evidence collected and either identify crucial information about the attack (e.g., when it occurred, its culprit, its target) or, at the very least, perform a pre-analysis to reduce the complexity of the problem in order to then draw conclusions more swiftly and efficiently. We introduce the Evidence Logic EL for representing simple and derived pieces of evidence from different sources. We propose a procedure, based on monotonic reasoning, that rewrites the pieces of evidence with the use of tableau rules, based on relations of trust between sources and the reasoning behind the derived evidence, and yields a consistent set of pieces of evidence. As proof of concept, we apply our analysis process to a concrete cyber-forensics case study.

 

You can find the paper here.

This work was funded from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 746667.

WSNs Under Attack! How Bad Is It? Evaluating Connectivity Impact Using Centrality Measures

Rodrigo Vieira Steiner–Publications–

Our paper WSNs Under Attack! How Bad Is It? Evaluating Connectivity Impact Using Centrality Measures has been presented at the Living in the Internet of Things: A PETRAS, IoTUK & IET Conference, Forum & Exhibition.

Authors: Rodrigo Vieira Steiner, Martín Barrère, Emil C. Lupu

Abstract: We propose a model to represent the health of WSNs that allows us to evaluate a network’s ability to execute its functions. Central to this model is how we quantify the importance of each network node. As we focus on the availability of the network data, we investigate how well different centrality measures identify the significance of each node for the network connectivity. In this process, we propose a new metric named current-flow sink betweenness. Through a number of experiments , we demonstrate that while no metric is invariably better in identifying sensors’ connectivity relevance, the proposed current-flow sink betweenness outperforms existing metrics in the vast majority of cases.

Download a copy here.

Kenneth Co

eclupu–People–

Kenny joined the group as a PhD student in April 2018. He received an MSc in Machine Learning from Imperial College London and an MA in Mathematics from Johns Hopkins University.

His general interests are in machine learning, cryptography, and mathematics. His current research is on the security of machine learning algorithms, primarily adversarial machine learning. He is also interested in health or lifestyle optimization, and is very much into enjoying good food.

Find him on LinkedIn.

Javier Carnerero Cano

eclupu–People–

This image has an empty alt attribute; its file name is IMG_20190525_135742_imp2.jpgJavi joined the group as a PhD Candidate in May 2018. He received his MEng in Telecommunications Engineering and his MRes in Multimedia and Communications from Universidad Carlos III de Madrid (Spain).

He is currently interested in adversarial machine learning, aiming to investigate the security of machine learning algorithms (with special focus on data poisoning attacks); applications of machine learning in security; bilevel optimisation problems; and Generative Adversarial Networks (GANs).

You can also find him on ORCID, Google Scholar, ResearchGate, Semantic Scholar, LinkedIn, Twitter and GitHub.

Label Sanitization against Label Flipping Poisoning Attacks

Luis Muñoz González–News, Publications–

Andrea Paudice, Luis Muñoz-González, Emil C. Lupu. 2018. Label Sanitization against Label Flipping Poisoning Attacks. arXiv preprint arXiv:1803.00992.

Many machine learning systems rely on data collected in the wild from untrusted sources, exposing the learning algorithms to data poisoning. Attackers can inject malicious data in the training dataset to subvert the learning process, compromising the performance of the algorithm producing errors in a targeted or an indiscriminate way. Label flipping attacks are a special case of data poisoning, where the attacker can control the labels assigned to a fraction of the training points. Even if the capabilities of the attacker are constrained, these attacks have been shown to be effective to significantly degrade the performance of the system. In this paper we propose an efficient algorithm to perform optimal label flipping poisoning attacks and a mechanism to detect and relabel suspicious data points, mitigating the effect of such poisoning attacks.

Ensuring the resilience of WSN to Malicious Data Injections through Measurements Inspection

eclupu–Publications–

Malicious data injections pose a severe threat to the systems based on Wireless Sensor Networks (WSNs) since they give the attacker control over the measurements, and on the system’s status and response in turn. Malicious measurements are particularly threatening when used to spoof or mask events of interest, thus eliciting or preventing desirable responses. Spoofing and masking attacks are particularly difficult to detect since they depict plausible behaviours, especially if multiple sensors have been compromised and collude to inject a coherent set of malicious measurements. Previous work has tackled the problem through measurements inspection, which analyses the inter-measurements correlations induced by the physical phenomena. However, these techniques consider simplistic attacks and are not robust to collusion. Moreover, they assume highly predictable patterns in the measurements distribution, which are invalidated by the unpredictability of events. We design a set of techniques that effectively detect malicious data injections in the presence of sophisticated collusion strategies, when one or more events manifest. Moreover, we build a methodology to characterise the likely compromised sensors. We also design diagnosis criteria that allow us to distinguish anomalies arising from malicious interference and faults. In contrast with previous work, we test the robustness of our methodology with automated and sophisticated attacks, where the attacker aims to evade detection. We conclude that our approach outperforms state-of-the-a
rt approaches. Moreover, we estimate quantitatively the WSN degree of resilience and provide a methodology to give a WSN owner an assured degree of resilience by automatically designing the WSN deployment. To deal also with the extreme scenario where the attacker has compromised most of the WSN, we propose a combination with software attestation techniques, which are more reliable when malicious data is originated by a compromised software, but also more expensive, and achieve an excellent trade-off between cost and resilience.

 

Download Thesis from here. 

Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection

Luis Muñoz González–News, Publications–

Andrea Paudice, Luis Muñoz-González, Andras Gyorgy, Emil C. Lupu. 2018. Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection. arXiv preprint arXiv:1802.03041.

 

Machine learning has become an important component for many systems and applications including computer vision, spam filtering, malware and network intrusion detection, among others. Despite the capabilities of machine learning algorithms to extract valuable information from data and produce accurate predictions, it has been shown that these algorithms are vulnerable to attacks.
 Data poisoning is one of the most relevant security threats against machine learning systems, where attackers can subvert the learning process by injecting malicious samples in the training data. Recent work in adversarial machine learning has shown that the so-called optimal attack strategies can successfully poison linear classifiers, degrading the performance of the system dramatically after compromising a small fraction of the training dataset. In this paper we propose a defence mechanism to mitigate the effect of these optimal poisoning attacks based on outlier detection. We show empirically that the adversarial examples generated by these attack strategies are quite different from genuine points, as no detectability constrains are considered to craft the attack. Hence, they can be detected with an appropriate pre-filtering of the training dataset.

 

Determining Resilience Gains From Anomaly Detection for Event Integrity in Wireless Sensor Networks

eclupu–Publications–

Vittorio P. Illiano, Andrea Paudice, Luis Muñoz-González, and Emil C. Lupu. 2018. Determining Resilience Gains From Anomaly Detection for Event Integrity in Wireless Sensor Networks. ACM Trans. Sen. Netw. 14, 1, Article 5 (February 2018), 35 pages. DOI: https://doi.org/10.1145/3176621

Abstract: Measurements collected in a wireless sensor network (WSN) can be maliciously compromised through several attacks, but anomaly detection algorithms may provide resilience by detecting inconsistencies in the data. Anomaly detection can identify severe threats to WSN applications, provided that there is a sufficient amount of genuine information. This article presents a novel method to calculate an assurance measure for the network by estimating the maximum number of malicious measurements that can be tolerated. In previous work, the resilience of anomaly detection to malicious measurements has been tested only against arbitrary attacks, which are not necessarily sophisticated. The novel method presented here is based on an optimization algorithm, which maximizes the attack’s chance of staying undetected while causing damage to the application, thus seeking the worst-case scenario for the anomaly detection algorithm. The algorithm is tested on a wildfire monitoring WSN to estimate the benefits of anomaly detection on the system’s resilience. The algorithm also returns the measurements that the attacker needs to synthesize, which are studied to highlight the weak spots of anomaly detection. Finally, this article presents a novel methodology that takes in input the degree of resilience required and automatically designs the deployment that satisfies such a requirement.