Machine Learning Applications for Load Predictions in Electrical Energy Network

In this work collected operational data of typical urban and rural energy network are analysed for predictions of energy consumption, as well as for selected region of Nordpool electricity markets. The regression techniques are systematically investigated for electrical energy prediction and correlating other impacting parameters. The k-Nearest Neighbour (kNN), Random Forest (RF) and Linear Regression (LR) are analysed and evaluated both by using continuous and vertical time approach. It is observed that for 30 minutes predictions the RF Regression has the best results, shown by a mean absolute percentage error (MAPE) in the range of 1-2 %. kNN show best results for the day-ahead forecasting with MAPE of 2.61 %. The presented vertical time approach outperforms the continuous time approach. To enhance pre-processing stage, refined techniques from the domain of statistics and time series are adopted in the modelling. Reducing the dimensionality through principal components analysis improves the predictive performance of Recurrent Neural Networks (RNN). In the case of Gated Recurrent Units (GRU) networks, the results for all the seasons are improved through principal components analysis (PCA). This work also considers abnormal operation due to various instances (e.g. random effect, intrusion, abnormal operation of smart devices, cyber-threats, etc.). In the results of kNN, iforest and Local Outlier Factor (LOF) on urban area data and from rural region data, it is observed that the anomaly detection for the scenarios are different. For the rural region, most of the anomalies are observed in the latter timeline of the data concentrated in the last year of the collected data. For the urban area data, the anomalies are spread out over the entire timeline. The frequency of detected anomalies where considerably higher for the rural area load demand than for the urban area load demand. Observing from considered case scenarios, the incidents of detected anomalies are more data driven, than exceptions in the algorithms. It is observed that from the domain knowledge of smart energy systems the LOF is able to detect observations that could not have detected by visual inspection alone, in contrast to kNN and iforest. Whereas kNN and iforest excludes an upper and lower bound, the LOF is density based and separates out anomalies amidst in the data. The capability that LOF has to identify anomalies amidst the data together with the deep domain knowledge is an advantage, when detecting anomalies in smart meter data. This work has shown that the instance based models can compete with models of higher complexity, yet some methods in preprocessing (such as circular coding) does not function for an instance based learner such as k-Nearest Neighbor, and hence kNN can not option for this kind of complexity even in the feature engineering of the model. It will be interesting for the future work of electrical load forecasting to develop solution that combines a high complexity in the feature engineering and have the explainability of instance based models.publishedVersio

A Leakage-Abuse Attack Against Multi-User Searchable Encryption

Searchable Encryption (SE) allows a user to upload data to the cloud and to search it in a remote fashion while preserving the privacy of both the data and the queries. Recent research results describe attacks on SE schemes using the access pattern, denoting the ids of documents matching search queries, which most SE schemes reveal during query processing. However SE schemes usually leak more than just the access pattern, and this extra leakage can lead to attacks (much) more harmful than the ones using basic access pattern leakage only. We remark that in the special case of Multi-User Searchable Encryption (MUSE), where many users upload and search data in a cloud-based infrastructure, a large number of existing solutions have a common leakage in addition to the well-studied access pattern leakage. We show that this seemingly small extra leakage allows a very simple yet powerful attack, and that the privacy degree of the affected schemes have been overestimated. We also show that this new vulnerability affects existing software. Finally we formalize the newly identified leakage profile and show how it relates to previously defined ones

Searchable atribute-based mechanism with efficiient data sharing for secure cloud storage

To date, the growth of electronic personal data leads to a trend that data owners prefer to remotely outsource their data to clouds for the enjoyment of the high-quality retrieval and storage service without worrying the burden of local data management and maintenance. However, secure share and search for the outsourced data is a formidable task, which may easily incur the leakage of sensitive personal information. Efficient data sharing and searching with security is of critical importance. This paper, for the first time, proposes a searchable attribute-based proxy re-encryption system. When compared to existing systems only supporting either searchable attribute-based functionality or attribute-based proxy re-encryption, our new primitive supports both abilities and provides flexible keyword update service. Specifically, the system enables a data owner to efficiently share his data to a specified group of users matching a sharing policy and meanwhile, the data will maintain its searchable property but also the corresponding search keyword(s) can be updated after the data sharing. The new mechanism is applicable to many real-world applications, such as electronic health record systems. It is also proved chosen ciphertext secure in the random oracle model

A Survey on the Evolution of Cryptographic Protocols in ePassports

ePassports are biometric identification documents that contain RFID Tags and are primarily used for border security. The embedded RFID Tags are capable of storing data, performing low cost computations and cryptography, and communicating wirelessly. Since 2004, we have witnessed the development and widespread deployment of three generations of electronic passports - The ICAO First Generation ePassport (2004), Extended Access Control (EAC v1.0) ePassports (2006), and Extended Access Control with Password Authentication and Connection Establishment (EAC v2.1) ePassports (2008). Currently, over thirty million ePassports have been issued around the world. In this paper, we provide an introductory study of the technologies implemented in ePassports - Biometrics, RFID, and Public Key Infrastructures; and then go on to analyze the protocols implemented in each of the three generations of ePassports, finally we point out their shortcomings and scope for future related research

Raptor: A Practical Lattice-Based (Linkable) Ring Signature

We present Raptor, the first practical lattice-based (linkable) ring signature scheme with implementation. Raptor is as fast as classical solutions; while the size of the signature is roughly $1.3$ KB per user. Prior to our work, all existing lattice-based solutions are analogues of their discrete-log or pairing-based counterparts. We develop a generic construction of (linkable) ring signatures based on the well-known generic construction from Rivest et al., which is not fully compatible with lattices. We show that our generic construction is provably secure in random oracle model. We also give instantiations from both standard lattice, as a proof of concept, and NTRU lattice, as an efficient instantiation. We showed that the latter construction, called Raptor, is almost as efficient as the classical RST ring signatures and thus may be of practical interest

Formal Computational Unlinkability Proofs of RFID Protocols

We set up a framework for the formal proofs of RFID protocols in the computational model. We rely on the so-called computationally complete symbolic attacker model. Our contributions are: i) To design (and prove sound) axioms reflecting the properties of hash functions (Collision-Resistance, PRF); ii) To formalize computational unlinkability in the model; iii) To illustrate the method, providing the first formal proofs of unlinkability of RFID protocols, in the computational model

A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007-2022 were collected from the Scopus Database then pre-processed. Following this, the VOSviewer was used to visualize the network of authors' keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure

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

Secure and Scalable Multi-User Searchable Encryption

By allowing a large number of users to behave as readers or writers, Multi-User Searchable Encryption (MUSE) raises new security and performance challenges beyond the typical requirements of Symmetric Searchable Encryption (SSE). In this paper we identify two core mandatory requirements of MUSE protocols being privacy in face of users colluding with the CSP and low complexity for the users, pointing that no existing MUSE protocol satisfies these two requirements at the same time. We then come up with the first MUSE protocol that satisfies both of them. The design of the protocol also includes new constructions for a secure variant of Bloom Filters (BFs) and multi-query Oblivious Transfer (OT)