SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine Learning

Cyber-physical systems (CPS) and Internet-of-Things (IoT) devices are increasingly being deployed across multiple functionalities, ranging from healthcare devices and wearables to critical infrastructures, e.g., nuclear power plants, autonomous vehicles, smart cities, and smart homes. These devices are inherently not secure across their comprehensive software, hardware, and network stacks, thus presenting a large attack surface that can be exploited by hackers. In this article, we present an innovative technique for detecting unknown system vulnerabilities, managing these vulnerabilities, and improving incident response when such vulnerabilities are exploited. The novelty of this approach lies in extracting intelligence from known real-world CPS/IoT attacks, representing them in the form of regular expressions, and employing machine learning (ML) techniques on this ensemble of regular expressions to generate new attack vectors and security vulnerabilities. Our results show that 10 new attack vectors and 122 new vulnerability exploits can be successfully generated that have the potential to exploit a CPS or an IoT ecosystem. The ML methodology achieves an accuracy of 97.4% and enables us to predict these attacks efficiently with an 87.2% reduction in the search space. We demonstrate the application of our method to the hacking of the in-vehicle network of a connected car. To defend against the known attacks and possible novel exploits, we discuss a defense-in-depth mechanism for various classes of attacks and the classification of data targeted by such attacks. This defense mechanism optimizes the cost of security measures based on the sensitivity of the protected resource, thus incentivizing its adoption in real-world CPS/IoT by cybersecurity practitioners.Comment: This article has been accepted in IEEE Transactions on Emerging Topics in Computing. 17 pages, 12 figures, IEEE copyrigh

MILP-aided Cryptanalysis of Round Reduced ChaCha

The inclusion of ChaCha20 and Poly1305 into the list of supported ciphers in TLS 1.3 necessitates a security evaluation of those ciphers with all the state-of-the-art tools and innovative cryptanalysis methodologies. Mixed Integer Linear Programming (MILP) has been successfully applied to find more accurate characteristics of several ciphers such as SIMON and SPECK. In our research, we use MILP-aided cryptanalysis to search for differential characteristics, linear approximations and integral properties of ChaCha. We are able to find differential trails up to 2 rounds and linear trails up to 1 round. However, no integral distinguisher has been found, even for 1 round

Finding and Evaluating Parameters for BGV

Fully Homomorphic Encryption (FHE) is a groundbreaking technology that allows for arbitrary computations to be performed on encrypted data. State-of-the-art schemes such as Brakerski Gentry Vaikuntanathan (BGV) are based on the Learning with Errors over rings (RLWE) assumption, and each ciphertext has an associated error that grows with each homomorphic operation. For correctness, the error needs to stay below a certain threshold, requiring a trade-off between security and error margin for computations in the parameters. Choosing the parameters accordingly, for example, the polynomial degree or the ciphertext modulus, is challenging and requires expert knowledge specific to each scheme. In this work, we improve the parameter generation process across all steps of its process. We provide a comprehensive analysis for BGV in the Double Chinese Remainder Theorem (DCRT) representation providing more accurate and better bounds than previous work on the DCRT, and empirically derive a closed formula linking the security level, the polynomial degree, and the ciphertext modulus. Additionally, we introduce new circuit models and combine our theoretical work in an easy-to-use parameter generator for researchers and practitioners interested in using BGV for secure computation. Our formula results in better security estimates than previous closed formulas, while our DCRT analysis results in reduced prime sizes of up to 42% compared to previous work

Secure multi-party computation-based privacy-preserving authentication for smart cities

The increasing concern for identity confidentiality in the Smart City scenario has fostered research on privacy-preserving authentication based on pseudonymization. Pseudonym systems enable citizens to generate pseudo-identities and establish unlinkable anonymous accounts in cloud service providers. The citizen's identity is concealed, and his/her different anonymous accounts cannot be linked to each other. Unfortunately, current pseudonym systems require a trusted certification authority (CA) to issue the cryptographic components (e.g. credentials, secret keys, or pseudonyms) to citizens. This CA, generally a Smart City governmental entity, has the capability to grant or revoke privacy rights at will, hence posing a serious threat in case of corruption. Additionally, if the pseudonym system enables de-anonymization of misusers, a corrupted CA can jeopardize the citizens' privacy. This paper presents a novel approach to construct a pseudonym system without a trusted issuer. The CA is emulated by a set of Smart City service providers by means of secure multi-party computation (MPC), which circumvents the requirement of assuming an honest CA. The paper provides a full description of the system, which integrates an MPC protocol and a pseudonym-based signature scheme. The system has been implemented and tested

Risk estimation for a secure and usable user authentication mechanism for mobile passenger ID devices

User Authentication in mobile devices acts as a first line of defense verifying the user's identity to allow access to the resources of a device and typically was based on “something the user knows”, known also as knowledge-based user authentication for several decades. However, recent studies point out that although knowledge-based user authentication has been the most popular for authenticating an individual, nowadays it is no more considered secure and convenient for the mobile user as it is imposing several limitations in terms of security and usability. These limitations stress the need for the development and implementation of more secure and usable user authentication methods. Toward this direction, user authentication based on the “something the user is” has caught the attention. This category includes authentication methods which make use of human physical characteristics (also referred to as physiological biometrics), or involuntary actions (also referred to as behavioral biometrics). In particular, risk-based user authentication based on behavioral biometrics appears to have the potential to increase the reliability of authentication without sacrificing usability. In this context, we focus on the estimation of the risk score, in a continuous mode, of the risk-based user authentication mechanism that we have proposed in our previous work for mobile passenger identification (ID) devices for land/sea border control

Farasha: A Provable Permutation-based Parallelizable PRF

The pseudorandom function Farfalle, proposed by Bertoni et al. at ToSC 2017, is a permutation based arbitrary length input and output PRF. At its core are the public permutations and feedback shift register based rolling functions. Being an elegant and parallelizable design, it is surprising that the security of Farfalle has been only investigated against generic cryptanalysis techniques such as differential/linear and algebraic attacks and nothing concrete about its provable security is known. To fill this gap, in this work, we propose Farasha, a new permutation-based parallelizable PRF with provable security. Farasha can be seen as a simple and provable Farfalle-like construction where the rolling functions in the compression and expansion phases of Farfalle are replaced by a uniform almost xor universal (AXU) and a simple counter, respectively. We then prove that in the random permutation model, the compression phase of Farasha can be shown to be an uniform AXU function and the expansion phase can be mapped to an Even-Mansour block cipher. Consequently, combining these two properties, we show that Farasha achieves a security of min(keysize, permutation size/2). Finally, we provide concrete instantiations of Farasha with AXU functions providing different performance trade-offs. We believe our work will bring new insights in further understanding the provable security of Farfalle-like constructions

Survey on Fully Homomorphic Encryption, Theory, and Applications

Data privacy concerns are increasing significantly in the context of Internet of Things, cloud services, edge computing, artificial intelligence applications, and other applications enabled by next generation networks. Homomorphic Encryption addresses privacy challenges by enabling multiple operations to be performed on encrypted messages without decryption. This paper comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. The paper delves into the mathematical foundations required to understand fully homomorphic encryption (FHE). It consequently covers design fundamentals and security properties of FHE and describes the main FHE schemes based on various mathematical problems. On a more practical level, the paper presents a view on privacy-preserving Machine Learning using homomorphic encryption, then surveys FHE at length from an engineering angle, covering the potential application of FHE in fog computing, and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art FHE libraries and tools, implemented in software and hardware, and the performance thereof

Architectures for Efficient Face Authentication in Embedded Systems Abstract

Biometrics represent a promising approach for reliable and secure user authentication. However, they have not yet been widely adopted in embedded systems, particularly in resource-constrained devices such as cell phones and personal digital assistants (PDAs). In this paper, we investigate the challenges involved in using face-based biometrics for authenticating a user to an embedded system. To enable high authentication accuracy, we consider robust face verifiers based on principal component analysis/linear discriminant analysis (PCA-LDA) algorithms and Bayesian classifiers, and their combined use (multi-modal biometrics). Since embedded systems are severely constrained in their processing capabilities, algorithms that provide sufficient accuracy tend to be computationally expensive, leading to unacceptable authentication times. On the other hand, achieving acceptable performance often comes at the cost of degradation in the quality of results. Our work aims at developing embedded processing architectures tha

Machine Learning Assisted Security Analysis of 5G-Network-Connected Systems

The core network architecture of telecommunication systems has undergone a paradigm shift in the fifth-generation (5G) networks. 5G networks have transitioned to software-defined infrastructures, thereby reducing their dependence on hardware-based network functions. New technologies, like network function virtualization and software-defined networking, have been incorporated in the 5G core network (5GCN) architecture to enable this transition. This has resulted in significant improvements in efficiency, performance, and robustness of the networks. However, this has also made the core network more vulnerable, as software systems are generally easier to compromise than hardware systems. In this article, we present a comprehensive security analysis framework for the 5GCN. The novelty of this approach lies in the creation and analysis of attack graphs of the software-defined and virtualized 5GCN through machine learning. This analysis points to 119 novel possible exploits in the 5GCN. We demonstrate that these possible exploits of 5GCN vulnerabilities generate five novel attacks on the 5G Authentication and Key Agreement protocol. We combine the attacks at the network, protocol, and the application layers to generate complex attack vectors. In a case study, we use these attack vectors to find four novel security loopholes in WhatsApp running on a 5G network