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.
Connected devices will continue to grow in volume and variety. The increase of connectivity brings a drastic impact on the increase of cyber attacks. Protecting measurements are not enough, while finding who did the attack is a crucial for preventing the escalation of cyber attacks. The impact of forensics in cyber security is becoming essential for the reduction and mitigation of attacks. Forensics and attribution forensics come along with their own challenges, like the difficulties on collecting suitable evidence, and the vastness of anti-forensics tools used by the attackers to cover their traces.
The main goal of AF-Cyber is to investigate and analyse the problem of attributing cyber attacks. We plan to construct a logic-based framework for performing attribution of cyber attacks, based on cyber forensics evidence, social science approaches and an intelligent methodology for dynamic evidence collection. AF-Cyber will relieve part of the cyberattacks problem, by supporting forensics investigation and attribution with logical-based frameworks representation, reasoning and supporting tools. AF-Cyber is multi-disciplinary and collaborative, bridging forensics in cyber attacks, theoretical computer science (logics and formal proofs), security, software engineering, and social science.
AF-Cyber received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 746667.
Dr Karafili is officially a Marie Curie Fellow at the Department of Computing, Imperial College. She will work on the project “AF-Cyber: Logic-based Attribution and Forensics in Cyber Security“. The project was granted by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No 746667.
AF-Cyber: Logic-based Attribution and Forensics in Cyber Security
The main goal of AF-Cyber is to investigate and analyse the problem of attributing cyber attacks. We plan to construct a logic-based framework for performing attribution of cyber attacks, based on cyber forensics evidence, social science approaches and an intelligent methodology for dynamic evidence collection. AF-Cyber will relieve part of the cyberattacks problem, by supporting forensics investigation and attribution with logical-based frameworks representation, reasoning and supporting tools. AF-Cyber is multi-disciplinary and collaborative, bridging forensics in cyber attacks, theoretical computer science (logics and formal proofs), security, software engineering, and social science.
Video of the presentation given in the Cyber security session of the Codex ‘World’s Top 50 Innovators’. The session also included presentations from Mikko Hypponen, Chief Research Officer, F-Secure, Andrew Rubin, CEO and Founder, Illumio, and Dave Palmer, Director of Technology, Darktrace as well as a panel discussion.
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.
Abstract: The increasing use of big data comes along with the problem of ensuring correct and secure data access. There is a need to maximise the data dissemination whilst controlling their access. Depending on the type of users different qualities and parts of data are shared. We introduce an alteration mechanism, more precisely a restriction one, based on a policy analysis language. The alteration reflects the level of trust and relations the users have, and are represented as policies inside the data sharing agreements. These agreements are attached to the data and are enforced every time the data are accessed, used or shared. We show the use of our alteration mechanism with a military use case, where different parties are involved during the missions, and they have different relations of trust and partnership.
The work was supported by EPSRC Project CIPART grant no. EP/L022729/1 and DAIS ITA (Sponsored by U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001).
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 specific network targets. Such compactness allows forensic investigators to focus their efforts on critical nodes that are more likely to be part of attack paths, thus reducing the overall number of nodes (devices, network privileges) that need to be examined. Nevertheless, core graphs also allow investigators to hierarchically explore the graph in order to retrieve different levels of summarised information. We have evaluated our approach over different network topologies varying parameters such as network size, density, and forensic evaluation threshold. Our results demonstrate that we can achieve the same level of accuracy provided by standard logical attack graphs while significantly reducing the exploration rate of the network.
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 this presentation, we first describe some of the problems that currently challenge the practical use of attack graphs. We then explain our approach based on core attack graphs, a novel perspective to address attack graph complexity. Finally, we present Naggen, a tool for generating, visualising and exploring core attack graphs. We use Naggen to show the advantages of our approach on different security applications.
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 allows to estimate the probabilities of an attacker compromising different resources of the network. We show how exact and approximate inference techniques can be efficiently applied on Bayesian attack graph models with thousands of nodes.
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 we propose is based on argumentation reasoning, that is a well suited technique for implementing decision making mechanisms under conflicting and incomplete information. Our proposal permits us to identify the attacker of a cyber attack and decide the regulation rule that should be used while using and sharing data. We illustrate our solution through concrete examples.
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 management, we highlight the importance of data sharing agreements (DSAs) and quality attributes for the proposed data processing mechanism. The proposed approach is taking into account the DSAs and usage policies as well as the quality attributes of the data, which were previously neglected compared to existing methods in the data processing and management field. Previous research provided techniques towards this direction; however, a more intensive research approach for processing techniques should be introduced for the future to enhance the value creation from the data and new strategies should be formed around this data generated daily from various devices and sources.
This work was supported by FP7 EU-funded project Coco Cloud grant no.: 610853, and EPSRC Project CIPART grant no. EP/L022729/1.
A number of online services nowadays rely upon machine learning to extract valuable information from data collected in the wild. This exposes learning algorithms to the threat of data poisoning, i.e., a coordinate attack in which a fraction of the training data is controlled by the attacker and manipulated to subvert the learning process. To date, these attacks have been devised only against a limited class of binary learning algorithms, due to the inherent complexity of the gradient-based procedure used to optimize the poisoning points (a.k.a. adversarial training examples).[/ezcol_2third]
In this work, we first extend the definition of poisoning attacks to multi-class problems. We then propose a novel poisoning algorithm based on the idea of back-gradient optimization, i.e., to compute the gradient of interest through automatic differentiation, while also reversing the learning procedure to drastically reduce the attack complexity. Compared to current poisoning strategies, our approach is able to target a wider class of learning algorithms, trained with gradient-based procedures, including neural networks and deep learning architectures. We empirically evaluate its effectiveness on several application examples, including spam filtering, malware detection, and handwritten digit recognition. We finally show that, similarly to adversarial test examples, adversarial training examples can also be transferred across different learning algorithms.
Attack graphs provide compact representations of the attack paths an attacker can follow to compromise network resources from the analysis of network vulnerabilities and topology. These representations are a powerful tool for security risk assessment. Bayesian inference on attack graphs enables the estimation of the risk of compromise to the system’s components given their vulnerabilities and interconnections and accounts for multi-step attacks spreading through the system. While static analysis considers the risk posture at rest, dynamic analysis also accounts for evidence of compromise, for example, from Security Information and Event Management software or forensic investigation. However, in this context, exact Bayesian inference techniques do not scale well. In this article, we show how Loopy Belief Propagation—an approximate inference technique—can be applied to attack graphs and that it scales linearly in the number of nodes for both static and dynamic analysis, making such analyses viable for larger networks. We experiment with different topologies and network clustering on synthetic Bayesian attack graphs with thousands of nodes to show that the algorithm’s accuracy is acceptable and that it converges to a stable solution. We compare sequential and parallel versions of Loopy Belief Propagation with exact inference techniques for both static and dynamic analysis, showing the advantages and gains of approximate inference techniques when scaling to larger attack graphs.
Attack graphs are a powerful tool for security risk assessment by analysing network vulnerabilities and the paths attackers can use to compromise network resources. The uncertainty about the attacker’s behaviour makes Bayesian networks suitable to model attack graphs to perform static and dynamic analysis. Previous approaches have focused on the formalization of attack graphs into a Bayesian model rather than proposing mechanisms for their analysis. In this paper we propose to use efficient algorithms to make exact inference in Bayesian attack graphs, enabling the static and dynamic network risk assessments. To support the validity of our approach we have performed an extensive experimental evaluation on synthetic Bayesian attack graphs with different topologies, showing the computational advantages in terms of time and memory use of the proposed techniques when compared to existing approaches.
IoT environments are vulnerable: many devices can be accessed physically and are not designed with security in mind. It is often impractical to patch all the vulnerabilities or to eliminate all possible threats. Unlike more traditional computing systems IoT environments bring together the physical, human and cyber aspects of a system. Each can be used to compromise the other and each can contribute towards monitoring and protecting the other.
Given the complexity of possible attacks, techniques for identifying and assessing the security risk are needed. In traditional networked environments attack graphs have been proven as a powerful tool for representing the different paths through which a system can be compromised. In this project we propose to design a new generation of attack graph models capable of describing the attack surface of modern IoT infrastructures for smart buildings. We are investigating new mechanisms to reduce the complexity of the attack graph representations and efficient algorithms for their analysis.
Ransomware has become one of the most prominent threats in cyber-security and recent attacks has shown the sophistication and impact of this class of malware. In essence, ransomware aims to render the victim’s system unusable by encrypting important files, and then, ask the user to pay a ransom to revert the damage. Several ransomware include sophisticated packing techniques, and are hence difficult to statically analyse. In our previous work, we developed EldeRan, a machine learning approach to analyse and classify ransomware dynamically. EldeRan monitors a set of actions performed by applications in their first phases of installation checking for characteristics signs of ransomware.
You can download here the dataset we collected and analysed with Cuckoo sandbox, which includes 582 samples of ransomware and 942 good applications.
Further details about the dataset can be found in the paper:
Daniele Sgandurra, Luis Muñoz-González, Rabih Mohsen, Emil C. Lupu. “Automated Analysis of Ransomware: Benefits, Limitations, and use for Detection.” In arXiv preprints arXiv:1609.03020, 2016.
Please, if you use our data set don’t forget to reference our work. You can copy the BIBTEX link here.
Jukka is currently a PhD student in the RISS group participating in the HiPEDS CDT. His current research interests are in ways to measure and improve the cyber-resilience of partially compromised networks, and in network risk analysis using attack graphs. Jukka holds an MSc in Computing Science from Imperial College London, and degrees in economics from the University of Oxford and the University of St Andrews.
Attestation and measurements inspection are different but complementary approaches towards the same goal: ascertaining the integrity of sensor nodes in wireless sensor networks. In this paper we compare the benefits and drawbacks of both techniques and seek to determine how to best combine them. However, our study shows that no single solution exists, as each choice introduces changes in the measurements collection process, affects the attestation protocol, and gives a different balance between the high detection rate of attestation and the low power overhead of measurements inspection. Therefore, we propose three strategies that combine measurements inspection and attestation in different ways, and a way to choose between them based on the requirements of different applications. We analyse their performance both analytically and in a simulator. The results show that the combined strategies can achieve a detection rate close to attestation, in the range 96–99%, whilst keeping a power overhead close to measurements inspection, in the range 1–10%.
Vittorio P. Illiano, Rodrigo V. Steiner and Emil C. Lupu: Unity is strength!: combining attestation and measurements inspection to handle malicious data injections in WSNs.
Dr. Hassan Chizari has joined the RISS Group in February 2017, coming from Universiti Teknologi Malaysia (UTM). Hassan is a Post Doctorate Research Associate (PDRA) in Imperial College London. He did his bachelor and master degree in Shiraz University (IRAN) on ‘Computer Hardware’ and ‘Artificial Intelligence and Robotics’ respectively. He was a PhD candidate in UTM studying on Wireless Sensor Networks and he was awarded the PhD on Computer Networks. He worked in Imam Khomeini International University (IKIU) and Universiti Teknologi Malaysia (UTM) as a Lecturer and a Senior Lecturer for about 9 years. His main research interest is Wireless Sensor Network both in hardware and software perspectives and mainly in cyber-security area. Hassan has worked in the group in particular on using biophysical signals as a randomness source for establishing shared keys in wearable body sensor networks for health care. He is now an Associate Professor in Cyber-Security at the University of Gloucestershire. Google Scholar Profile, Research Gate Profile, ORCiD Profile, Home page
Abstract: Internet of Things environments enable us to capture more and more data about the physical environment we live in and about ourselves. The data enable us to optimise resources, personalise services and offer unprecedented insights into our lives. However, to achieve these insights data need to be shared (and sometimes sold) between organisations imposing rights and obligations upon the sharing parties and in accordance with multiple layers of sometimes conflicting legislation at international, national and organisational levels. In this work, we show how such rules can be captured in a formal representation called “Data Sharing Agreements”. We introduce the use of abductive reasoning and argumentation based techniques to detect inconsistencies in the rules applicable and resolve them by assigning priorities to the rules. We show how through the use of argumentation based techniques use-cases taken from real life application are handled flexibly addressing trade-offs between confidentiality, privacy, availability and safety.
Wireless Sensor Networks (WSNs) have become popular for monitoring critical infrastructures, military applications, and Internet of Things (IoT) applications.
However, WSNs carry several vulnerabilities in the sensor nodes, the wireless medium, and the environment. In particular, the nodes are vulnerable to tampering on the field, since they are often unattended, physically accessible, and use of tamper-resistant hardware is often too expensive.
Malicious data injections consist of manipulations of the measurements-related data, which threaten the WSN’s mission since they enable an attacker to solicit a wrong system’s response, such as concealing the presence of problems, or raising false alarms.
Measurements inspection is a method for counteracting malicious measurements by exploiting internal correlations in the measurements themselves. Since it does not need extra data it is a lightweight approach, and since it makes no assumption on the attack vector it is caters for several attacks at once.
Our first achievement was to identify the benefits and shortcomings of the current measurements inspection techniques and produce a literature survey, which was published in ACM Computing Surveys: V. P. Illiano and E. C. Lupu. ”Detecting malicious data injections in wireless sensor networks: A survey”, Oct. 2015 . The survey has revealed a large number of algorithms proposed for measurements inspection in sensor measurements. However, malicious data injections are usually tackled together with faulty measurements. Nevertheless, malicious measurements are, by and large, more difficult to detect than faulty measurements, especially when multiple malicious sensors collude and produce measurements that are consistent with each other.
We have designed an initial algorithm, which detects effectively malicious data injections in the presence of sophisticated collusion strategies among a subset of sensor nodes when a single event of interest (e.g. fire, earthquake, power outage) occurs at a time. The detection algorithm selects only information that appears reliable. Colluding sensors are not allowed to compensate for each other in the detection metric whilst still injecting malicious data thanks to an aggregation operator that is accurate in the presence of genuine measurements as well as resistant to malicious data. This work was published in IEEE Transactions on Network and Service Management, V. Illiano and E. Lupu, Detecting malicious data injections in event detection wireless sensor networks, Sept 2015
How many sensors does a malicious actor need to compromise to be successful? The white crosses in the middle diagram show these sensors when attempting to mask an event. The image on the right shows the sensors that need to be compromised to spoof an event. These diagrams are the result of an optimisation problem aiming to determine the worse possible attack. The method of determining the worst possible attack by solving an optimisation problem was published in IEEE Transactions on Sensor Networks Feb 2018 and provides a more general framework that allows to quantify the resilience gains obtained through detection algorithms or even to compare different detection algorithms.
Whilst detection proved highly reliable also in the presence of several colluding nodes, we have witnessed that more genuine nodes are needed to make a correct characterisation of malicious nodes. Hence, we have studied techniques to increase the reliability in identifying malicious nodes through occasional recourse to Software Attestation, a technique that is particularly reliable in detecting compromised software, but is also expensive for the limited computation and energy resources of the sensor nodes. Based on a thorough analysis of the aspects that make measurements inspection and software attestation complementary, we have designed the methods that allow to achieve a reliability as high as for attestation with an overhead as low as for measurements inspection. This work was presented at the 10th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec 2017).
