arXiv subjects

Aleksander Essex

Publications and source records attributed to Aleksander Essex.

4 recordsLinked to original sources

arXiv2026

It Doesn't Take a Thief: Optical-Scan Voting Systems Fail Even Without Adversaries

Optical-scan voting systems and their supporting ecosystem of people, processes, and technology are fallible. While a substantial body of work examines adversarial threats to such systems, we have encountered jurisdictions where the possibility of tabulator error is not fully internalized. Stakeholders there often find hypothetical attacks unconvincing, but some are persuaded by real-world accounts of equipment and procedural failures. This paper introduces a taxonomy of non-adversarial failure modes organized into intuitive categories: recording votes on paper, reading votes from the paper, combining votes as read into a reported outcome, and testing and verifying, all illustrated with documented incidents. We map common verification mechanisms against this taxonomy, identifying gaps that no paper-based audit can detect or correct, most notably failures that compromise the trustworthiness of the paper trail, such as giving voters the wrong ballot style (omitting contests they are eligible for, or including ones they are not), using ballot-marking devices to record votes, or failing to keep voted ballots secure and organized.

cs.CY↗

arXiv2020

Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection

Network attacks have been very prevalent as their rate is growing tremendously. Both organization and individuals are now concerned about their confidentiality, integrity and availability of their critical information which are often impacted by network attacks. To that end, several previous machine learning-based intrusion detection methods have been developed to secure network infrastructure from such attacks. In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique to tune the parameters of Support Vector Machine with Gaussian Kernel (SVM-RBF), Random Forest (RF), and k-Nearest Neighbor (k-NN) algorithms. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.

cs.LG↗

arXiv2020

Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review

Cloud computing has become a powerful and indispensable technology for complex, high performance and scalable computation. The exponential expansion in the deployment of cloud technology has produced a massive amount of data from a variety of applications, resources and platforms. In turn, the rapid rate and volume of data creation has begun to pose significant challenges for data management and security. The design and deployment of intrusion detection systems (IDS) in the big data setting has, therefore, become a topic of importance. In this paper, we conduct a systematic literature review (SLR) of data mining techniques (DMT) used in IDS-based solutions through the period 2013-2018. We employed criterion-based, purposive sampling identifying 32 articles, which constitute the primary source of the present survey. After a careful investigation of these articles, we identified 17 separate DMTs deployed in an IDS context. This paper also presents the merits and disadvantages of the various works of current research that implemented DMTs and distributed streaming frameworks (DSF) to detect and/or prevent malicious attacks in a big data environment.

cs.CR↗

arXiv2017

Trust Implications of DDoS Protection in Online Elections

Online elections make a natural target for distributed denial of service attacks. Election agencies wary of disruptions to voting may procure DDoS protection services from a cloud provider. However, current DDoS detection and mitigation methods come at the cost of significantly increased trust in the cloud provider. In this paper we examine the security implications of denial-of-service prevention in the context of the 2017 state election in Western Australia, revealing a complex interaction between actors and infrastructure extending far beyond its borders. Based on the publicly observable properties of this deployment, we outline several attack scenarios including one that could allow a nation state to acquire the credentials necessary to man-in-the-middle a foreign election in the context of an unrelated domestic law enforcement or national security operation, and we argue that a fundamental tension currently exists between trust and availability in online elections.

cs.CR↗