We are proud to announce that the 43rd International Conference on Computer Safety, Reliability and Security (SafeComp) will be in Florence, from 17 to 20 September 2024, chaired by Andrea Bondavalli and Andrea Ceccarelli.

See the website: https://www.safecomp2024.unifi.it/

And you can download the Call for Papers. 
 

IWES 2023 , chaired by Andrea Bondavalli, Andrea Ceccarelli, and Enrico Vicario, was successfully conducted in Florence, with 75 participants in two days. The active support of the Resilient Computing Lab has been the key to the success of the event.

RCL welcomes you at the 8th Italian Workshop on Embedded Systems (IWES 2023), in Florence, on the 4th and 5th of September!

The Italian Workshop on Embedded Systems is a well established meeting point for the exchange of research experience in Academy and Industry on all aspects of embedded systems.

Students should consider attending the Students' orientation session, where companies will present themselves and interact with students to scout prospective talents.

More information: https://mclabservices.di.uniroma1.it/iwes/2023/

The second edition of the ARTISAN Summer School has been announced: see the website 

https://artisan2023.sciencesconf.org/

The ARTISAN PhD Summer School (Role and effects of ARTificial  Intelligence in Secure ApplicatioNs) tackles the issues related to  Artificial Intelligence (AI) and Machine Learning (ML) with regards to security and safety applications.

Alongside with his colleagues Oum El-Kheir and Oliver Jung, Andrea Ceccarelli, associate professor at RCL, is organizing the second edition of the ARTISAN Summer School. Prof. Andrea Bondavalli will held a talk titled "Dependability Challenges in Safety-Critical Systems: the adoption of Machine learning", and Dr. Tommaso Zoppi will held a talk titled "Meta-Learning for Intrusion Detection: Teamwork or Fight against each other?".

The chair of RCL Group, Andrea Bondavalli, just finished his Keynote speech at The 41st International Symposium on Reliable Distributed Systems (SRDS 2022)

Keynote Details

Title: Dependability Challenges in Safety-Critical Systems: the adoption of Machine learning

Abstract: Machine Learning components in safety-critical applications can perform some complex tasks that would be unfeasible otherwise. However, they are also a weak point concerning safety assurance. We will illustrate two specific cases where ML must be incorporated in SCS with much care. One is related to the interactions between machine-learning components and other non-ML components and how they evolve with training of the former. We argue that it is theoretically possible that learning by the Neural Network may reduce the effectiveness of error checkers or safety monitors, creating a major complication for safety assurance. An example on automated driving is shown. Among the results, we observed that indeed improving the Controller could make the Safety Monitor less effective; to a limit where a training increment makes the Controller's own behavior safer but results in the vehicle to be less safe. The other one regards ML algorithms that perform binary classification as error, intrusion or failure detectors. They can be used in SCS provided that their performance complies with SCS safety requirements. However, the performance analysis of MLs relies on metrics that were not developed with safety in mind and consequently may not provide meaningful evidence to decide whether to incorporate a ML into a SCS. We analyze the distribution of misclassifications and thus show how to better assess the adequacy of a given ML.

This year, RCL presented a contribution to the Machine Learning for CyberSecurity (MLCS 2022) workshop of the European Conference of Machine Learning (ECML), in Grenoble - France

The paper is entitled "Towards a General Model for Intrusion Detection: An Exploratory Study", and originated many questions and discussions from the audience.

Paper Abstract: Exercising Machine Learning (ML) algorithms to detect intrusions is nowa-days the de-facto standard for data-driven detection tasks. This activity re-quires the expertise of the researchers, practitioners, or employees of compa-nies that also have to gather labeled data to learn and evaluate the model that will then be deployed into a specific system. Reducing the expertise and time required to craft intrusion detectors is a tough challenge, which in turn will have an enormous beneficial impact in the domain. This paper conducts an exploratory study that aims at understanding to which extent it is possi-ble to build an intrusion detector that is general enough to learn the model once and then be applied to different systems with minimal to no effort. Therefore, we recap the issues that may prevent building general detectors and propose software architectures that have the potential to overcome them. Then, we perform an experimental evaluation using several binary ML classifiers and a total of 16 feature learners on 4 public attack datasets. Re-sults show that a model learned on a dataset or a system does not generalize well as is to other datasets or systems, showing poor detection performance. Instead, building a unique model that is then tailored to a specific dataset or system may achieve good classification performance, requiring less data and far less expertise from the final user.

The chair of the group, Andrea Bondavalli, just finished his keynote speech at the IEEE CyberSecurity and Resilience Conference (CSR 2022)

Keynote Details

Title: Intrusion Detection Through (Unsupervised) Machine Learning: Pros, Limitations and Workarounds

Abstract: It is undeniable that new cyber-attacks are continuously crafted against essentially any kind of system and service. Systems are subject to a mix of usual practiced attacks and new ones that were not previously known, motivating the need for building Intrusion Detectors (IDs) that can effectively deal with those zero-day attacks. Different studies have been devised Unsupervised Machine Learning (ML) algorithms belonging to different families as clustering, neural networks, density-based, neighbor-based, statistical, and classification. Those algorithms have the potential to detect even unknown threats thanks to a training phase that does not rely on labels in data. The talk shows how different algorithms are better suited for the detection of specific anomalies of system indicators, which manifest when attacks are conducted against a system. Unfortunately, those algorithms show inferior detection performance of known threats with respect to supervised ML algorithms; to fill this gap, we show improvements achieved when adopting Meta-Learning techniques. In any case, the quality of the best solution that can be devised depends strongly on the problem at hand and demands for high cost for selecting and finding the optimal set up of Unsupervised algorithms. To this end, we conclude the talk by proposing a cheap method to quantitatively understand the achievable results without exercising the full optimization activities.

We just get to know that,

On behalf of the IEEE Systems, Man, and Cybernetics (SMC) Technical Committee on Homeland Security (TCHS), and its award committee, Andrea Bondavalli has been granted with the:

*** IEEE TCHS Outstanding Leadership Award ***

This award recognizes an individual's outstanding leadership contributions to the homeland security community in general and to the field of cyber security and resilience in particular. The award recognizes the leadership and visionary contributions to the society at large through the promotion and applications of security and resilience concepts to a variety of technology, science, and business domains.

CONGRATULATIONS Andrea!