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Institute
Object-Centric Event Logs (OCELs) form the basis for Object-Centric Process Mining (OCPM). OCEL 1.0 was first released in 2020 and triggered the development of a range of OCPM techniques. OCEL 2.0 forms the new, more expressive standard, allowing for more extensive process analyses while remaining in an easily exchangeable format. In contrast to the first OCEL standard, it can depict changes in objects, provide information on object relationships, and qualify these relationships to other objects or specific events. Compared to XES, it is more expressive, less complicated, and better readable. OCEL 2.0 offers three exchange formats: a relational database (SQLite), XML, and JSON format. This OCEL 2.0 specification document provides an introduction to the standard, its metamodel, and its exchange formats, aimed at practitioners and researchers alike.
Recently, several robust principal component analysis (RPCA) models were presented to enhance the robustness of PCA by exploiting the robust norms as their loss functions. But an important problem is that they have no ability to discriminate outliers from correct samples. To solve this problem, we propose a robust principal component analysis based on discriminant information (RPCA-DI). RPCA-DI disentangles the robust PCA with a two-step fashion: the identification and the processing of outliers. To identity outliers, a sample representation model based on entropy regularization is constructed to analyze the membership of data belonging to the principal component space(PC) and its orthogonal complement(OC), the discriminative information of data will be extracted based on measuring the differences of retained information on PC(or OC) of data. By this way, we can discriminate correct samples when we deal with outliers, which is more reasonable for robustness learning respective to previous works. In the noise processing step, in addition to considering the levels of noise, the resistance of the sample points to noise is also considered to prevent overfitting, thereby improving the generalization performance of RPCA-DI. Finally, an iterative algorithm is designed to solve the corresponding model. Compared with some state-of-art RPCA methods on artificial datasets, UCI datasets and face databases that verifies the effectiveness of our proposed algorithm.
Nowadays the mobile phone has become an indispensable tool in the lives of many people. While facilitating people's lives, it also provides criminals with a very important tool for spreading the terrorist video. Traditional manual detection of the terrorist video has the problem of low accuracy and inefficiency. To address the issue, this paper proposes a terrorist video detection system based on Light Gradient Boosting Machine (LightGBM) and Faster Region-based Convolutional Neural Network (Faster R-CNN) for mobile phone forensics system, which is used to quickly detect whether there is a terrorist video in the suspect's mobile phone. The system uses a multi-model method for detection, which includes preliminary detection and deep detection in two stages. Experimental research shows that it can effectively and accurately detect terrorist videos in mobile phones, thereby helping criminal investigation personnel to quickly grasp criminal evidence and provide some clues for the detection of the case.
This paper presents a tool to Explore Process Discovery (EPD) results using activity projection. Our EPD-Tool aims at exploring quality changes after removing activities from an event log. The main idea is to create a projected event log for every non-empty subset of activities and apply process discovery and conformance checking on them. The tool has been implemented as a plugin in ProM. First, EPD-Tool uses a process discovery algorithm to discover Petri net models for each projected event log. Then, EPD-Tool uses a conformance checking technique to compute conformance measures for each projected event log and model pair (L, N), e.g., fitness, precision, and F1-score. Finally, a dendrogram is generated to visualize the relationship between each log-model pair, thus enabling the systematic exploration of the different models using the dendrogram to find the best-performing node, i.e., a best log-model pair. This method prioritizes activities and detects redundancy in the process, which contributes to process enhancement. Conversely, critical activities are uncovered to help to shorten the processing time or save the process cost. This paper presents the EPD-Tool implementation and some example results.
Support vector machine (SVM) is a classification model, which learns the decision surface that maximizes the margin in the feature space. Such a decision surface has a good classification ability for unknown new samples. In real-world applications, the data set usually contains many noises and outliers, which will affect the learning of the decision surface, thus the maximum margin cannot be obtained, and the generalization ability of SVM will be reduced. In this paper, we introduce an adjacency factor to each input point to characterize the local neighbor relationship between each point. Weighting each sample point by the adjacency factor can let different sample points make different contributions to the learning of the decision surface. Thus, we can filter out the influence of noises and outliers on the decision surface by this weighting method. We propose this new method namely local neighborhood reliability weighted support vector machine (LN-SVM).
Principal component analysis (PCA) is an important method for processing high-dimensional data. In recent years, PCA models based on various norms have been extensively studied to improve the robustness. However, on the one hand, these algorithms do not consider the relationship between reconstruction error and covariance; on the other hand, they lack the uncertainty of considering the principal component to the data description. Aiming at these problems, this paper proposes a new robust PCA algorithm. Firstly, the L2,p-norm is used to measure the reconstruction error and the description variance of the projection data. Based on the reconstruction error and the description variance, the adaptive probability error minimization model is established to calculate the uncertainty of the principal component's description of the data. Based on the uncertainty, the adaptive probability weighting PCA is established. The corresponding optimization method is designed. The experimental results of artificial data sets, UCI data sets and face databases show that RPCA-PW is superior than other PCA algorithms.
To obtain a compact and effective low-dimensional representation, recently, most existing discriminant manifold learning methods have integrated manifold learning into discriminant analysis (DA) for extracting the intrinsic structure of data. These methods learn two kinds of adjacency graphs, such as intrinsic graph and penalty graph, to characterize the similarity between samples from intraclass and the pseudo similarity of interclass. However, they treat every sample equally, which results in the following defects: (1) These methods cannot accurately characterize the marginal region among different classes only through penalty graphs. (2) They can not identify the noisy and outlier samples which reduce the robustness of these methods. To address these problems, we introduce an adaptive adjacency factor to perform the discriminative based reliability analysis for each sample. By integrating the adjacency factor into discriminant manifold learning methods, we propose a novel method for DA namely discriminant analysis based on reliability of local neighborhood (DA-RoLN). We mainly have three contributions in this paper: (1) By the introduction of adjacency factor, sample points can be divided into three parts: intraclass samples, marginal samples, and outliers. Therefore, DA-RoLN emphasizes the effect of valid samples and filters the influence of outliers. (2) We adaptively calculate the adjacency factor in low-dimensional space, thus, the margin between different classes in low-dimensional space is emphasized. (3) An iterative algorithm is developed to solve the objective function of DA-RoLN, and it is easy to solve with a low computational cost. Extensive experimental results show the effectiveness of DA-RoLN.
Controller Area Network (CAN) is a masterless serial bus designed and widely used for the exchange of mission and time-critical information within commercial vehicles. In-vehicle communication is based on messages sent and received by Electronic Control Units (ECUs) connected to this serial bus network. Although unencrypted, CAN messages are not easy to interpret. In fact, Original Equipment Manufacturers (OEMs) attempt to achieve security through obscurity by encoding the data in their proprietary format, which is kept secret from the general public. As a result, the only way to obtain clear data is to reverse engineer CAN messages. Driven by the need for in-vehicle message interpretation, which is highly valuable in the automotive industry, researchers and companies have been working to make this process automated, fast, and standardized. In this paper, we provide a comprehensive review of the state of the art and summarize the major advances in CAN bus reverse engineering. We are the first to provide a taxonomy of CAN tokenization and translation techniques. Based on the reviewed literature, we highlight an important issue: the lack of a public and standardized dataset for the quantitative evaluation of translation algorithms. In response, we define a complete set of requirements for standardizing the data collection process. We also investigate the risks associated with the automation of CAN reverse engineering, in particular with respect to the security network and the safety and privacy of drivers and passengers. Finally, we discuss future research directions in CAN reverse engineering.
The overwhelmingly widespread use of Internet of Things (IoT) in different application domains brought not only benefits, but, alas, security concerns as a result of the increased attack surface and vectors. One of the most critical mechanisms in IoT infrastructure is key management. This paper reflects on the problems and challenges of existing key management systems, starting with the discussion of a recent real-world attack. We identify and elaborate on the drawbacks of security primitives based purely on physical variations and – after highlighting the problems of such systems – continue on to deduce an effective and cost-efficient key management solution for IoT systems extending the symbiotic security approach in a previous work. The symbiotic architecture combines software, firmware, and hardware resources for secure IoT while avoiding the traditional scheme of static key storage and generating entropy for key material on-the-fly via a combination of a Physical Unclonable Func tion (PUF) and pseudo-random bits pre-populated in firmware.
Reviewing War: Unconventional User Reviews as a Side Channel to Circumvent Information Controls
(2023)
During the first days of the 2022 Russian invasion of Ukraine, Russia’s media regulator blocked access to many global social media
platforms and news sites, including Twitter, Facebook, and the
BBC. To bypass the information controls set by Russian authorities,
pro-Ukrainian groups explored unconventional ways to reach out
to the Russian population, such as posting war-related content in
the user reviews of Russian business available on Google Maps or
Tripadvisor. This paper provides a first analysis of this new phenomenon by analyzing the creative strategies to avoid state censorship.
Specifically, we analyze reviews posted on these platforms from
the beginning of the conflict to September 2022. We measure the
channeling of war messages through user reviews in Tripadvisor
and Google Maps, as well as in VK, a popular Russian social network.
Our analysis of the content posted on these services reveals
that users leveraged these platforms to seek and exchange humanitarian
and travel advice, but also to disseminate disinformation and
polarized messages. Finally, we analyze the response of platforms
in terms of content moderation and their impact.
The development of the prototype of the system SGAi (Generation System for InteractiveApplications) designed to obtain feedback from TV viewers about mass services, is reported; in a particularway, it is applied to evaluate the quality of telecommunication services. SGAi consists of three components: 1)Survey Composer, which lets users create and customize interactive applications for surveys and automaticallygenerate the NCL code (Nested Context Language) that will run on the STB (Set Top Box) of the viewer;2) A manageable web application that provides the survey management system, receives and storages opinionsas well as the display of results; and 3) the return channel, responsible for communications between the STBand the server of the manageable web application. The system is aimed, in particular Survey Composer, tofacilitate the generation of interactive applications that use the return channel, allowing the user to focus onthe content and aesthetics of the surveys rather than learning and using NCL. SGAi has been developed as partof research activities carried out in the digital TV area in the National Polytechnic School, considering theISDB-Tb standard, adopted in Ecuador in March 2010.
The Routing Protocol for Low Power and Lossy Networks (RPL) has become the standard routing protocol for the Internet of Things (IoT). This paper investigates the use of RPL in dynamic networks and presents an enhanced RPL for different applications with dynamic mobility and diverse network requirements. This implementation of RPL is designed with a new dynamic Objective-Function (D-OF) to improve the Packet Delivery Ratio (PDR), end-to-end delay and energy consumption while maintaining low packet overhead and loop-avoidance. We propose a controlled reverse-trickle timer based on received signal strength identification (RSSI) readings to maintain high responsiveness with minimum overhead and consult the objective function when a movement or an inconsistency is detected to help nodes make an informed decision. Simulations are done using Cooja with random waypoint mobility scenario for healthcare applications considering multi-hop routing. The results show that the proposed dynamic RPL (D-RPL) adapts to the nodes mobility and has a higher PDR, slightly lower end-to-end delay and reasonable energy consumption compared to related existing protocols.
Trustless, Censorship-Resilient and Scalable Votings in the Permission-Based Blockchain Model
(2021)
Voting systems are the tool of choice when it comes to settle an agreement of different opinions. We propose a solution for a trustless, censorship-resilient and scalable electronic voting platform. By leveraging the blockchain together with the functional encryption paradigm, we fully decentralize the system and reduce the risks that a voting provider, like a corrupt government, does censor or manipulate the outcome.
We define the concept of and present provably secure constructions for Anonymous RAM (AnonRAM), a novel multi-user storage primitive that offers strong privacy and integrity guarantees. AnonRAM combines privacy features of anonymous communication and oblivious RAM (ORAM) schemes, allowing it to protect, simultaneously, the privacy of content, access patterns and user’s identity, from curious servers and from other (even adversarial) users. AnonRAM further protects integrity, i.e., it prevents malicious users from corrupting data of other users. We present two secure AnonRAM schemes, differing in design and time complexity. The first scheme has a simpler design; like efficient ORAM schemes, its time complexity is poly-logarithmic in the number of cells (per user); however, it is linear in the number of users. The second AnonRAM scheme reduces the overall complexity to poly-logarithmic in the total number of cells (of all users) at the cost of requiring two (non-colluding) servers.
With the recent emergence of efficient zero-knowledge (ZK) proofs for general circuits, while efficient zero-knowledge proofs of algebraic statements have existed for decades, a natural challenge arose to combine algebraic and non-algebraic statements. Chase et al. (CRYPTO 2016) proposed an interactive ZK proof system for this cross-domain problem. As a use case they show that their system can be used to prove knowledge of a RSA/DSA signature on a message m with respect to a publicly known Pedersen commitment gmhr. One drawback of their system is that it requires interaction between the prover and the verifier.
This is due to the interactive nature of garbled circuits, which are used in their construction. Subsequently, Agrawal et al. (CRYPTO 2018) proposed an efficient non-interactive ZK (NIZK) proof system fo cross-domains based on SNARKs, which however require a trusted setup assumption. In this paper, we propose a NIZK proof system for cross domains that requires no trusted setup and is efficient both for the prover and the verifier.
Our system constitutes a combination of Schnorr based ZK proofs and ZK proofs for general circuits by Giacomelli et al. (USENIX 2016). The proof size and the running time of our system are comparable to the approach by Chase et al. Compared to Bulletproofs (SP 2018), a recent NIZK proofs system on committed inputs, our techniques achieve asymptotically better performance on prover and verifier, thus presenting a different trade-off between the proof size and the running time.
The emergence of distributed digital currencies has raised the need for a reliable consensus mechanism. In proof-of-stake cryptocurrencies, the participants periodically choose a closed set of validators, who can vote and append transactions to the blockchain. Each validator can become a leader with the probability proportional to its stake. Keeping the leader private yet unique until it publishes a new block can significantly reduce the attack vector of an adversary and improve the throughput of the network. The problem of Single Secret Leader Election (SSLE) was first formally defined by Boneh et al. in 2020.
In this work, we propose a novel framework for constructing SSLE protocols, which relies on secure multi-party computation (MPC) and satisfies the desired security properties. Our framework does not use any shuffle or sort operations and has a computational cost for N parties as low as O(N) of basic MPC operations per party. We improve the stateof-the-art for SSLE protocols that do not assume a trusted setup. Moreover, our SSLE scheme efficiently handles weighted elections. That is, for a total weight S of N parties, the associated costs are only increased by a factor of log S. When the MPC layer is instantiated with techniques based on Shamir’s secret-sharing, our SSLE has a communication cost of O(N2) which is spread over O(logN) rounds, can tolerate up to t < N/2 of faulty nodes without restarting the protocol, and its security relies on DDH in the random oracle model. When the MPC layer is instantiated with more efficient techniques based on garbled circuits, our SSLE requires all parties to participate, up to N −1 of which can be malicious, and its security is based on the random oracle model.
Vote privacy is a fundamental right, which needs to be protected not only during an election, or for a limited time afterwards, but for the foreseeable future. Numerous electronic voting (e-voting) protocols have been proposed to address this challenge, striving for everlasting privacy. This property guarantees that even computationally unbounded adversaries cannot break privacy of past elections. The broad interest in secure e-voting with everlasting privacy has spawned a large variety of protocols over the last three decades. These protocols differ in many aspects, in particular the precise security properties they aim for, the threat scenarios they consider, and the privacy-preserving techniques they employ. Unfortunately, these differences are often opaque, making analysis and comparison cumbersome. In order to overcome this non-transparent state of affairs, we systematically analyze all e-voting protocols designed to provide everlasting privacy. First, we illustrate the relations and dependencies between all these different protocols. Next, we analyze in depth which protocols do provide secure and efficient approaches to e-voting with everlasting privacy under realistic assumptions, and which ones do not. Eventually, based on our extensive and detailed treatment, we identify which research problems in this field have already been solved, and which ones are still open. Altogether, our work offers a well - founded reference point for conducting research on secure e - voting with everlasting privacy as well as for future - proofing privacy in real - world electronic elections.
Internet voting systems are supposed to meet the same high stan-
dards as traditional paper-based systems when used in real political
elections: freedom of choice, universal and equal suffrage, secrecy
of the ballot, and independent verifiability of the election result.
Although numerous Internet voting systems have been proposed
to achieve these challenging goals simultaneously, few come close
in reality.
We propose a novel publicly verifiable and practically efficient
Internet voting system, DeVoS, that advances the state of the art.
The main feature of DeVoS is its ability to protect voters’ freedom
of choice in several dimensions. First, voters in DeVoS can intu-
itively update their votes in a way that is deniable to observers but
verifiable by the voters; in this way voters can secretly overwrite
potentially coerced votes. Second, in addition to (basic) vote privacy,
DeVoS also guarantees strong participation privacy by end-to-end
hiding which voters have submitted ballots and which have not.
Finally, DeVoS is fully compatible with Perfectly Private Audit Trail,
a state-of-the-art Internet voting protocol with practical everlasting
privacy. In combination, DeVoS offers a new way to secure free
Internet elections with strong and long-term privacy properties.
Security and Performance Implications of BGP Rerouting-resistant Guard Selection Algorithms for Tor
(2023)
Tor is the most popular anonymization network with millions of daily users. This makes it an attractive target for attacks, e.g., by malicious autonomous systems (ASs) performing active routing attacks to become man in the middle and deanonymize users. It was shown that the number of such malicious ASs is significantly larger than previously expected due to the lack of security guarantees in the Border Gateway Protocol (BGP). In response, recent works suggest alternative Tor path selection methods preferring Tor nodes with higher resilience to active BGP attacks.
In this work, we analyze the implications of such proposals and demonstrate that two state-of-the-art path selection methods, namely Counter-RAPTOR and DPSelect, are not as secure as thought before. First, we show that DPSelect achieves only one third of its originally claimed resilience and, thus, is not as resilient as widely accepted. Second, we reveal that the resilience to active BGP attacks on the way back, i.e., from the first anonymization node to a given Tor user, provided by both methods is significantly lower than on the forward path. Beside their lower resilience in specific cases, we show that for particular users the usage of Counter-RAPTOR and DPSelect also leads to leakage of user’s location. Furthermore, we uncover the performance implications of both methods and identify scenarios where their usage leads to significant performance bottlenecks. Finally, we propose a new metric to quantify the user’s location leakage by path selection. Using this metric and performing large-scale analysis, we show to which extent a malicious Tor middle node can fingerprint the user’s location and the confidence it can achieve. Our findings shed light on the implications of path selection methods on the users’ anonymity and the need for further research.