RISS

Resilient Information Systems Security

Label Sanitization against Label Flipping Poisoning Attacks

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 […]

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

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 […]

Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection

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.    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 […]

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

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 […]

Improving Data Sharing in Data Rich Environments

The paper “Improving Data Sharing in Data Rich Environments” was accepted at the IEEE Big Data International Workshop on Policy-based Autonomic Data Governance (PADG), part of the 15th IEEE International Conference on Big Data (Big Data 2017), December 11-14, 2017, Boston, MA, USA. This work was done in collaboration with our partners (BAE Systems, IBM UK and IBM US) from the DAIS International Technology Alliance (ITA). The paper can be found here. Authors: Erisa Karafili, Emil C. Lupu, Alan Cullen, Bill Williams, Saritha Arunkumar, Seraphin Calo Abstract: The increasing use of big data comes along with the problem of ensuring correct […]

Tracking the Bad Guys: An Efficient Forensic Methodology To Trace Multi-step Attacks Using Core Attack Graphs

Tracking the Bad Guys: An Efficient Forensic Methodology To Trace Multi-step Attacks Using Core Attack Graphs, has been presented at the 13th IEEE/IFIP International Conference on Network and Service Management (CNSM’17), November 2017, in Tokyo, Japan.   The paper is available here and the presentation slides (PDF) can be downloaded here. Authors: Martín Barrère, Rodrigo Vieira Steiner, Rabih Mohsen, Emil C. Lupu In this paper, we describe an efficient methodology to guide investigators during network forensic analysis. To this end, we introduce the concept of core attack graph, a compact representation of the main routes an attacker can take towards […]

Naggen: a Network Attack Graph GENeration Tool

Naggen: a Network Attack Graph GENeration Tool, has been presented at the IEEE Conference on Communications and Network Security (CNS’17), October 2017, in Las Vegas, USA. The paper is available here and the poster can be downloaded here. Authors: Martín Barrère, Emil C. Lupu Attack graphs constitute a powerful security tool aimed at modelling the many ways in which an attacker may compromise different assets in a network. Despite their usefulness in several security-related activities (e.g. hardening, monitoring, forensics), the complexity of these graphs can massively grow as the network becomes denser and larger, thus defying their practical usability. In […]

Bayesian Attack Graphs for Security Risk Assessment

Attack graphs offer a powerful framework for security risk assessment. They provide a compact representation of the attack paths that an attacker can follow to compromise network resources from the analysis of the network topology and vulnerabilities. The uncertainty about the attacker’s behaviour makes Bayesian networks suitable to model attack graphs to perform static and dynamic security risk assessment. Thus, whilst static analysis of attack graphs considers the security posture at rest, dynamic analysis accounts for evidence of compromise at run-time, helping system administrators to react against potential threats. In this paper, we introduce a Bayesian attack graph model that […]

Argumentation-based Security for Social Good

The paper “Argumentation-based Security for Social Good” presented at the AAAI Spring Symposia 2017 is now available at the AAAI Technical Report. Title: Argumentation-Based Security for Social Good Authors: Erisa Karafili, Antonis C. Kakas, Nikolaos I. Spanoudakis, Emil C. Lupu Abstract: The increase of connectivity and the impact it has in ever day life is raising new and existing security problems that are becoming important for social good. We introduce two particular problems: cyber attack attribution and regulatory data sharing. For both problems, decisions about which rules to apply, should be taken under incomplete and context dependent information. The solution […]

An argumentation reasoning approach for data processing

The paper “An argumentation reasoning approach for data processing” is now published in the Elsevier Journal Computers in Industry. Title: An argumentation reasoning approach for data processing Authors: Erisa Karafili, Konstantina Spanaki, Emil C. Lupu Abstract: Data-intensive environments enable us to capture information and knowledge about the physical surroundings, to optimise our resources, enjoy personalised services and gain unprecedented insights into our lives. However, to obtain these endeavours extracted from the data, this data should be generated, collected and the insight should be exploited. Following an argumentation reasoning approach for data processing and building on the theoretical background of data […]