91 research outputs found
Software Protection and Secure Authentication for Autonomous Vehicular Cloud Computing
Artificial Intelligence (AI) is changing every technology we deal with. Autonomy has been a sought-after goal in vehicles, and now more than ever we are very close to that goal. Vehicles before were dumb mechanical devices, now they are becoming smart, computerized, and connected coined as Autonomous Vehicles (AVs). Moreover, researchers found a way to make more use of these enormous capabilities and introduced Autonomous Vehicles Cloud Computing (AVCC). In these platforms, vehicles can lend their unused resources and sensory data to join AVCC.
In this dissertation, we investigate security and privacy issues in AVCC. As background, we built our vision of a layer-based approach to thoroughly study state-of-the-art literature in the realm of AVs. Particularly, we examined some cyber-attacks and compared their promising mitigation strategies from our perspective. Then, we focused on two security issues involving AVCC: software protection and authentication.
For the first problem, our concern is protecting clientâs programs executed on remote AVCC resources. Such a usage scenario is susceptible to information leakage and reverse-engineering. Hence, we proposed compiler-based obfuscation techniques. What distinguishes our techniques, is that they are generic and software-based and utilize the intermediate representation, hence, they are platform agnostic, hardware independent and support different high level programming languages. Our results demonstrate that the control-flow of obfuscated code versions are more complicated making it unintelligible for timing side-channels.
For the second problem, we focus on protecting AVCC from unauthorized access or intrusions, which may cause misuse or service disruptions. Therefore, we propose a strong privacy-aware authentication technique for users accessing AVCC services or vehicle sharing their resources with the AVCC. Our technique modifies robust function encryption, which protects stakeholderâs confidentiality and withstands linkability and âknown-ciphertextsâ attacks. Thus, we utilize an authentication server to search and match encrypted data by performing dot product operations. Additionally, we developed another lightweight technique, based on KNN algorithm, to authenticate vehicles at computationally limited charging stations using its ownerâs encrypted iris data. Our security and privacy analysis proved that our schemes achieved privacy-preservation goals. Our experimental results showed that our schemes have reasonable computation and communications overheads and efficiently scalable
Handbook of Digital Face Manipulation and Detection
This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area
Handbook of Digital Face Manipulation and Detection
This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area
Trustworthy Edge Machine Learning: A Survey
The convergence of Edge Computing (EC) and Machine Learning (ML), known as
Edge Machine Learning (EML), has become a highly regarded research area by
utilizing distributed network resources to perform joint training and inference
in a cooperative manner. However, EML faces various challenges due to resource
constraints, heterogeneous network environments, and diverse service
requirements of different applications, which together affect the
trustworthiness of EML in the eyes of its stakeholders. This survey provides a
comprehensive summary of definitions, attributes, frameworks, techniques, and
solutions for trustworthy EML. Specifically, we first emphasize the importance
of trustworthy EML within the context of Sixth-Generation (6G) networks. We
then discuss the necessity of trustworthiness from the perspective of
challenges encountered during deployment and real-world application scenarios.
Subsequently, we provide a preliminary definition of trustworthy EML and
explore its key attributes. Following this, we introduce fundamental frameworks
and enabling technologies for trustworthy EML systems, and provide an in-depth
literature review of the latest solutions to enhance trustworthiness of EML.
Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table
Microsoft Defender Will Be Defended: Memoryranger Prevents Blinding Windows Av
Windows OS is facing a huge rise in kernel attacks. An overview of popular techniques that result in loading kernel drivers will be presented. One of the key targets of modern threats is disabling and blinding Microsoft Defender, a default Windows AV. The analysis of recent driver-based attacks will be given, the challenge is to block them. The survey of user- and kernel-level attacks on Microsoft Defender will be given. One of the recently published attackersâ techniques abuses Mandatory Integrity Control (MIC) and Security Reference Monitor (SRM) by modifying Integrity Level and Debug Privileges for the Microsoft Defender via syscalls. However, this user-mode attack can be blocked via the Windows âtrust labelsâ mechanism. The presented paper discovered the internals of MIC and SRM, including the analysis of Microsoft Defender during malware detection. We show how attackers can attack Microsoft Defender using a kernel-mode driver. This driver modifies the fields of the Token structure allocated for the Microsoft Defender application. The presented attack resulted in disabling Microsoft Defender, without terminating any of its processes and without triggering any Windows security features, such as PatchGuard. The customized hypervisor-based solution named MemoryRanger was used to protect the Windows Defender kernel structures. The experiments show that MemoryRanger successfully restricts access to the sensitive kernel data from illegal access attempts with affordable performance degradation
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ENABLING IOT AUTHENTICATION, PRIVACY AND SECURITY VIA BLOCKCHAIN
Although low-power and Internet-connected gadgets and sensors are increasingly integrated into our lives, the optimal design of these systems remains an issue. In particular, authentication, privacy, security, and performance are critical success factors. Furthermore, with emerging research areas such as autonomous cars, advanced manufacturing, smart cities, and building, usage of the Internet of Things (IoT) devices is expected to skyrocket. A single compromised node can be turned into a malicious one that brings down whole systems or causes disasters in safety-critical applications. This dissertation addresses the critical problems of (i) device management, (ii) data management, and (iii) service management in IoT systems. In particular, we propose an integrated platform solution for IoT device authentication, data privacy, and service security via blockchain-based smart contracts. We ensure IoT device authentication by blockchain-based IC traceability system, from its fabrication to its end-of-life, allowing both the supplier and a potential customer to verify an ICâs provenance. Results show that our proposed consortium blockchain framework implementation in Hyperledger Fabric for IC traceability achieves a throughput of 35 transactions per second (tps). To corroborate the blockchain information, we authenticate the IC securely and uniquely with an embedded Physically Unclonable Function (PUF). For reliable Weak PUF-based authentication, our proposed accelerated aging technique reduces the cumulative burn-in cost by ⌠56%. We also propose a blockchain-based solution to integrate the privacy of data generated from the IoT devices by giving users control of their privacy. The smart contract controlled trust-base ensures that the users have private access to their IoT devices and data. We then propose a remote configuration of IC features via smart contracts, where an IC can be programmed repeatedly and securely. This programmability will enable users to upgrade IC features or rent upgraded IC features for a fixed period after users have purchased the IC. We tailor the hardware to meet the blockchain performance. Our on-die hardware module design enforces the hardware configurationâs secure execution and uses only 2,844 slices in the Xilinx Zedboard Zynq Evaluation board. The blockchain framework facilitates decentralized IoT, where interacting devices are empowered to execute digital contracts autonomously
On the malware detection problem : challenges and novel approaches
Orientador: AndrĂ© Ricardo Abed GrĂ©gioCoorientador: Paulo LĂcio de GeusTese (doutorado) - Universidade Federal do ParanĂĄ, Setor de CiĂȘncias Exatas, Programa de PĂłs-Graduação em InformĂĄtica. Defesa : Curitiba,Inclui referĂȘnciasĂrea de concentração: CiĂȘncia da ComputaçãoResumo: Software Malicioso (malware) Ă© uma das maiores ameaças aos sistemas computacionais atuais, causando danos Ă imagem de indivĂduos e corporaçÔes, portanto requerendo o desenvolvimento de soluçÔes de detecção para prevenir que exemplares de malware causem danos e para permitir o uso seguro dos sistemas. Diversas iniciativas e soluçÔes foram propostas ao longo do tempo para detectar exemplares de malware, de Anti-VĂrus (AVs) a sandboxes, mas a detecção de malware de forma efetiva e eficiente ainda se mantĂ©m como um problema em aberto. Portanto, neste trabalho, me proponho a investigar alguns desafios, falĂĄcias e consequĂȘncias das pesquisas em detecção de malware de modo a contribuir para o aumento da capacidade de detecção das soluçÔes de segurança. Mais especificamente, proponho uma nova abordagem para o desenvolvimento de experimentos com malware de modo prĂĄtico mas ainda cientĂfico e utilizo-me desta abordagem para investigar quatro questĂ”es relacionadas a pesquisa em detecção de malware: (i) a necessidade de se entender o contexto das infecçÔes para permitir a detecção de ameaças em diferentes cenĂĄrios; (ii) a necessidade de se desenvolver melhores mĂ©tricas para a avaliação de soluçÔes antivĂrus; (iii) a viabilidade de soluçÔes com colaboração entre hardware e software para a detecção de malware de forma mais eficiente; (iv) a necessidade de predizer a ocorrĂȘncia de novas ameaças de modo a permitir a resposta Ă incidentes de segurança de forma mais rĂĄpida.Abstract: Malware is a major threat to most current computer systems, causing image damages and financial losses to individuals and corporations, thus requiring the development of detection solutions to prevent malware to cause harm and allow safe computers usage. Many initiatives and solutions to detect malware have been proposed over time, from AntiViruses (AVs) to sandboxes, but effective and efficient malware detection remains as a still open problem. Therefore, in this work, I propose taking a look on some malware detection challenges, pitfalls and consequences to contribute towards increasing malware detection system's capabilities. More specifically, I propose a new approach to tackle malware research experiments in a practical but still scientific manner and leverage this approach to investigate four issues: (i) the need for understanding context to allow proper detection of localized threats; (ii) the need for developing better metrics for AV solutions evaluation; (iii) the feasibility of leveraging hardware-software collaboration for efficient AV implementation; and (iv) the need for predicting future threats to allow faster incident responses
Digital Watermarking for Verification of Perception-based Integrity of Audio Data
In certain application fields digital audio recordings contain sensitive content. Examples are historical archival material in public archives that preserve our cultural heritage, or digital evidence in the context of law enforcement and civil proceedings. Because of the powerful capabilities of modern editing tools for multimedia such material is vulnerable to doctoring of the content and forgery of its origin with malicious intent. Also inadvertent data modification and mistaken origin can be caused by human error. Hence, the credibility and provenience in terms of an unadulterated and genuine state of such audio content and the confidence about its origin are critical factors.
To address this issue, this PhD thesis proposes a mechanism for verifying the integrity and authenticity of digital sound recordings. It is designed and implemented to be insensitive to common post-processing operations of the audio data that influence the subjective acoustic perception only marginally (if at all). Examples of such operations include lossy compression that maintains a high sound quality of the audio media, or lossless format conversions. It is the objective to avoid de facto false alarms that would be expectedly observable in standard crypto-based authentication protocols in the presence of these legitimate post-processing. For achieving this, a feasible combination of the techniques of digital watermarking and audio-specific hashing is investigated.
At first, a suitable secret-key dependent audio hashing algorithm is developed. It incorporates and enhances so-called audio fingerprinting technology from the state of the art in contentbased audio identification. The presented algorithm (denoted as ârMACâ message authentication code) allows âperception-basedâ verification of integrity. This means classifying integrity breaches as such not before they become audible. As another objective, this rMAC is embedded and stored silently inside the audio media by means of audio watermarking technology. This approach allows maintaining the authentication code across the above-mentioned admissible post-processing operations and making it available for integrity verification at a later date. For this, an existent secret-key ependent audio watermarking algorithm is used and enhanced in this thesis work.
To some extent, the dependency of the rMAC and of the watermarking processing from a secret key also allows authenticating the origin of a protected audio. To elaborate on this security aspect, this work also estimates the brute-force efforts of an adversary attacking this combined rMAC-watermarking approach. The experimental results show that the proposed method provides a good distinction and classification
performance of authentic versus doctored audio content. It also allows the temporal localization of audible data modification within a protected audio file. The experimental evaluation finally provides recommendations about technical configuration settings of the combined watermarking-hashing approach.
Beyond the main topic of perception-based data integrity and data authenticity for audio, this PhD work provides new general findings in the fields of audio fingerprinting and digital watermarking. The main contributions of this PhD were published and presented mainly at conferences about multimedia security. These publications were cited by a number of other authors and hence had some impact on their works
Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving
In Autonomous Vehicles (AVs), one fundamental pillar is perception, which
leverages sensors like cameras and LiDARs (Light Detection and Ranging) to
understand the driving environment. Due to its direct impact on road safety,
multiple prior efforts have been made to study its the security of perception
systems. In contrast to prior work that concentrates on camera-based
perception, in this work we perform the first security study of LiDAR-based
perception in AV settings, which is highly important but unexplored. We
consider LiDAR spoofing attacks as the threat model and set the attack goal as
spoofing obstacles close to the front of a victim AV. We find that blindly
applying LiDAR spoofing is insufficient to achieve this goal due to the machine
learning-based object detection process. Thus, we then explore the possibility
of strategically controlling the spoofed attack to fool the machine learning
model. We formulate this task as an optimization problem and design modeling
methods for the input perturbation function and the objective function. We also
identify the inherent limitations of directly solving the problem using
optimization and design an algorithm that combines optimization and global
sampling, which improves the attack success rates to around 75%. As a case
study to understand the attack impact at the AV driving decision level, we
construct and evaluate two attack scenarios that may damage road safety and
mobility. We also discuss defense directions at the AV system, sensor, and
machine learning model levels.Comment: Accepted at the ACM Conference on Computer and Communications
Security (CCS), 201
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