4,326 research outputs found
Formal Template-Based Generation of Attack–Defence Trees for Automated Security Analysis
Systems that integrate cyber and physical aspects to create cyber-physical systems (CPS) are becoming increasingly complex, but demonstrating the security of CPS is hard and security is frequently compromised. These compromises can lead to safety failures, putting lives at risk. Attack Defense Trees with sequential conjunction (ADS) are an approach to identifying attacks on a system and identifying the interaction between attacks and the defenses that are present within the CPS. We present a semantic model for ADS and propose a methodology for generating ADS automatically. The methodology takes as input a CPS system model and a library of templates of attacks and defenses. We demonstrate and validate the effectiveness of the ADS generation methodology using an example from the automotive domain
Argumentation-based query answering under uncertainty with application to cybersecurity
Decision support tools are key components of intelligent sociotechnical systems, and their successful implementation faces a variety of challenges, including the multiplicity of information sources, heterogeneous format, and constant changes. Handling such challenges requires the ability to analyze and process inconsistent and incomplete information with varying degrees of associated uncertainty. Moreover, some domains require the system’s outputs to be explainable and interpretable; an example of this is cyberthreat analysis (CTA) in cybersecurity domains. In this paper, we first present the P-DAQAP system, an extension of a recently developed query-answering platform based on defeasible logic programming (DeLP) that incorporates a probabilistic model and focuses on delivering these capabilities. After discussing the details of its design and implementation, and describing how it can be applied in a CTA use case, we report on the results of an empirical evaluation designed to explore the effectiveness and efficiency of a possible world sampling-based approximate query answering approach that addresses the intractability of exact computations.Fil: Leiva, Mario Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: García, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Shakarian, Paulo. Arizona State University; Estados UnidosFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin
Optimising a defence-aware threat modelling diagram incorporating a defence-in-depth approach for the internet-of-things
Modern technology has proliferated into just about every aspect of life while improving the quality of life. For instance, IoT technology has significantly improved over traditional systems, providing easy life, time-saving, financial saving, and security aspects. However, security weaknesses associated with IoT technology can pose a significant threat to the human factor. For instance, smart doorbells can make household life easier, save time, save money, and provide surveillance security. Nevertheless, the security weaknesses in smart doorbells could be exposed to a criminal and pose a danger to the life and money of the household. In addition, IoT technology is constantly advancing and expanding and rapidly becoming ubiquitous in modern society. In that case, increased usage and technological advancement create security weaknesses that attract cybercriminals looking to satisfy their agendas.
Perfect security solutions do not exist in the real world because modern systems are continuously improving, and intruders frequently attempt various techniques to discover security flaws and bypass existing security control in modern systems. In that case, threat modelling is a great starting point in understanding the threat landscape of the system and its weaknesses. Therefore, the threat modelling field in computer science was significantly improved by implementing various frameworks to identify threats and address them to mitigate them. However, most mature threat modelling frameworks are implemented for traditional IT systems that only consider software-related weaknesses and do not address the physical attributes. This approach may not be practical for IoT technology because it inherits software and physical security weaknesses. However, scholars employed mature threat modelling frameworks such as STRIDE on IoT technology because mature frameworks still include security concepts that are significant for modern technology. Therefore, mature frameworks cannot be ignored but are not efficient in addressing the threat associated with modern systems.
As a solution, this research study aims to extract the significant security concept of matured threat modelling frameworks and utilise them to implement robust IoT threat modelling frameworks. This study selected fifteen threat modelling frameworks from among researchers and the defence-in-depth security concept to extract threat modelling techniques. Subsequently, this research study conducted three independent reviews to discover valuable threat modelling concepts and their usefulness for IoT technology. The first study deduced that integration of threat modelling approach software-centric, asset-centric, attacker-centric and data-centric with defence-in-depth is valuable and delivers distinct benefits. As a result, PASTA and TRIKE demonstrated four threat modelling approaches based on a classification scheme. The second study deduced the features of a threat modelling framework that achieves a high satisfaction level toward defence-in-depth security architecture. Under evaluation criteria, the PASTA framework scored the highest satisfaction value. Finally, the third study deduced IoT systematic threat modelling techniques based on recent research studies. As a result, the STRIDE framework was identified as the most popular framework, and other frameworks demonstrated effective capabilities valuable to IoT technology.
Respectively, this study introduced Defence-aware Threat Modelling (DATM), an IoT threat modelling framework based on the findings of threat modelling and defence-in-depth security concepts. The steps involved with the DATM framework are further described with figures for better understatement. Subsequently, a smart doorbell case study is considered for threat modelling using the DATM framework for validation. Furthermore, the outcome of the case study was further assessed with the findings of three research studies and validated the DATM framework. Moreover, the outcome of this thesis is helpful for researchers who want to conduct threat modelling in IoT environments and design a novel threat modelling framework suitable for IoT technology
Large Language Models for Code Analysis: Do LLMs Really Do Their Job?
Large language models (LLMs) have demonstrated significant potential in the
realm of natural language understanding and programming code processing tasks.
Their capacity to comprehend and generate human-like code has spurred research
into harnessing LLMs for code analysis purposes. However, the existing body of
literature falls short in delivering a systematic evaluation and assessment of
LLMs' effectiveness in code analysis, particularly in the context of obfuscated
code.
This paper seeks to bridge this gap by offering a comprehensive evaluation of
LLMs' capabilities in performing code analysis tasks. Additionally, it presents
real-world case studies that employ LLMs for the analysis of malicious code.
Our findings indicate that LLMs can indeed serve as valuable tools for
automating code analysis, albeit with certain limitations. Through meticulous
exploration, this research contributes to a deeper understanding of the
potential and constraints associated with utilizing LLMs in code analysis,
paving the way for enhanced applications in this critical domain
Gotcha! I Know What You are Doing on the FPGA Cloud: Fingerprinting Co-Located Cloud FPGA Accelerators via Measuring Communication Links
In recent decades, due to the emerging requirements of computation
acceleration, cloud FPGAs have become popular in public clouds. Major cloud
service providers, e.g. AWS and Microsoft Azure have provided FPGA computing
resources in their infrastructure and have enabled users to design and deploy
their own accelerators on these FPGAs. Multi-tenancy FPGAs, where multiple
users can share the same FPGA fabric with certain types of isolation to improve
resource efficiency, have already been proved feasible. However, this also
raises security concerns. Various types of side-channel attacks targeting
multi-tenancy FPGAs have been proposed and validated. The awareness of security
vulnerabilities in the cloud has motivated cloud providers to take action to
enhance the security of their cloud environments.
In FPGA security research papers, researchers always perform attacks under
the assumption that attackers successfully co-locate with victims and are aware
of the existence of victims on the same FPGA board. However, the way to reach
this point, i.e., how attackers secretly obtain information regarding
accelerators on the same fabric, is constantly ignored despite the fact that it
is non-trivial and important for attackers. In this paper, we present a novel
fingerprinting attack to gain the types of co-located FPGA accelerators. We
utilize a seemingly non-malicious benchmark accelerator to sniff the
communication link and collect performance traces of the FPGA-host
communication link. By analyzing these traces, we are able to achieve high
classification accuracy for fingerprinting co-located accelerators, which
proves that attackers can use our method to perform cloud FPGA accelerator
fingerprinting with a high success rate. As far as we know, this is the first
paper targeting multi-tenant FPGA accelerator fingerprinting with the
communication side-channel.Comment: To be published in ACM CCS 202
An Open Source Based Data Warehouse Architecture to Support Decision Making in the Tourism Sector
In this paper an alternative Tourism oriented Data Warehousing architecture is proposed which makes use of the most recent free and open source technologies like Java, Postgresql and XML. Such architecture's aim will be to support the decision making process and giving an integrated view of the whole Tourism reality in an established context (local, regional, national, etc.) without requesting big investments for getting the necessary software.Tourism, Data warehousing architecture
DAG-Based Attack and Defense Modeling: Don't Miss the Forest for the Attack Trees
This paper presents the current state of the art on attack and defense
modeling approaches that are based on directed acyclic graphs (DAGs). DAGs
allow for a hierarchical decomposition of complex scenarios into simple, easily
understandable and quantifiable actions. Methods based on threat trees and
Bayesian networks are two well-known approaches to security modeling. However
there exist more than 30 DAG-based methodologies, each having different
features and goals. The objective of this survey is to present a complete
overview of graphical attack and defense modeling techniques based on DAGs.
This consists of summarizing the existing methodologies, comparing their
features and proposing a taxonomy of the described formalisms. This article
also supports the selection of an adequate modeling technique depending on user
requirements
Discovering, quantifying, and displaying attacks
In the design of software and cyber-physical systems, security is often
perceived as a qualitative need, but can only be attained quantitatively.
Especially when distributed components are involved, it is hard to predict and
confront all possible attacks. A main challenge in the development of complex
systems is therefore to discover attacks, quantify them to comprehend their
likelihood, and communicate them to non-experts for facilitating the decision
process. To address this three-sided challenge we propose a protection analysis
over the Quality Calculus that (i) computes all the sets of data required by an
attacker to reach a given location in a system, (ii) determines the cheapest
set of such attacks for a given notion of cost, and (iii) derives an attack
tree that displays the attacks graphically. The protection analysis is first
developed in a qualitative setting, and then extended to quantitative settings
following an approach applicable to a great many contexts. The quantitative
formulation is implemented as an optimisation problem encoded into
Satisfiability Modulo Theories, allowing us to deal with complex cost
structures. The usefulness of the framework is demonstrated on a national-scale
authentication system, studied through a Java implementation of the framework.Comment: LMCS SPECIAL ISSUE FORTE 201
Security Code Smells in Android ICC
Android Inter-Component Communication (ICC) is complex, largely
unconstrained, and hard for developers to understand. As a consequence, ICC is
a common source of security vulnerability in Android apps. To promote secure
programming practices, we have reviewed related research, and identified
avoidable ICC vulnerabilities in Android-run devices and the security code
smells that indicate their presence. We explain the vulnerabilities and their
corresponding smells, and we discuss how they can be eliminated or mitigated
during development. We present a lightweight static analysis tool on top of
Android Lint that analyzes the code under development and provides just-in-time
feedback within the IDE about the presence of such smells in the code.
Moreover, with the help of this tool we study the prevalence of security code
smells in more than 700 open-source apps, and manually inspect around 15% of
the apps to assess the extent to which identifying such smells uncovers ICC
security vulnerabilities.Comment: Accepted on 28 Nov 2018, Empirical Software Engineering Journal
(EMSE), 201
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