Deduplication is a key component of the data preparation process, a bottleneck in the machine learning (ML) and data mining pipeline that is very time-consuming and often relies on domain expertise and manual involvement. Further, temporal data is increasingly prevalent and is not well suited to traditional similarity and distance-based deduplication techniques. We establish a fully automated, domain-independent deduplication model for temporal data domains, known as TemporalDedup, that infers the key attribute(s), applies a base set of deduplication techniques focused on value matches for key, non-key, and elapsed time, and further detects duplicates through inference of temporal ordering requirements using Longest Common Subsequence (LCS) for records of a shared type. Using LCS, we split each record’s temporal sequence into constrained and unconstrained sequences. We flag suspicious (errant) records that are non-adherent to the inferred constrained order and we flag a record as a duplicate if its unconstrained order, of sufficient length, matches that of another record. TemporalDedup was compared against a similarity-based Adaptive Sorted Neighborhood Method (ASNM) in evaluating duplicates for two disparate datasets: (1) 22,794 records from Sony’s PlayStation Network (PSN) trophy data, where duplication may be indicative of cheating, and (2) emergency declarations and government responses related to COVID-19 for all U.S. states and territories. TemporalDedup (F1-scores of 0.971 and 0.954) exhibited combined sensitivities above 0.9 for all duplicate classes whereas ASNM (0.705 and 0.732) exhibited combined sensitivities below 0.2 for all time and order duplicate classes.

For select domains and datasets, duplicates may be, in part or in whole, instances of cheating. We may specifically observe this for Sony's PlayStation Network (PSN) that services the world's most popular gaming platform. The key to cheat detection in like domains is the ability to perform temporal deduplication. Temporal data is increasingly prevalent and is not well suited to traditional similarity and distance-based deduplication techniques. We strengthen the well-established Adaptive Sorted Neighborhood Method (ASNM) with an approach for temporal data domains ($\text{ASNM}+\text{LCS}$) that applies ASNM, infers attribute metadata, and further detects duplicates through inference of temporal ordering requirements using Longest Common Subsequence (LCS) for records of a shared type. Using LCS, we split each record's temporal sequence into constrained and unconstrained sequences. We flag suspicious (errant) records that are non-adherent to the inferred constrained order and we flag a record as a duplicate if its unconstrained order, of sufficient length, matches that of another record. ASNM and $\text{ASNM}+\text{LCS}$ were evaluated against a labeled dataset of 22,794 records from PSN trophy data where duplication may be indicative of cheating. $\text{ASNM}+\text{LCS}$ F1 scores outperformed ASNM at every similarity threshold with at least 32% improvement. ASNM's best performance was an F1 of. 708 at the 0.99 threshold; $\text{ASNM}+\text{LCS}$ yielded an F1 of. 938. The significant performance improvement costs little overhead as $\text{ASNM}+\text{LCS}$ averaged only 3.79% additional runtime.

We demonstrate the feasibility of a generalized technique for semantic deduplication in temporal data domains using graph-based representations of data records. Structured data records with multiple timestamp attributes per record may be represented as a directed graph where the nodes represent the events and the edges represent event sequences. Edge weights are based on elapsed time between connecting nodes. In comparing two records, we may merge these directed graphs and determine a representative directed acyclic graph (DAG) inclusive of a subset of nodes and edges that maintain the transitive weights of the original graphs. This DAG may then be evaluated by weighting elapsed time equivalences between records at each node and measuring the fraction of nodes represented in the DAG versus the union of nodes between the records being compared. With this information, we establish a duplication score and use a specified threshold requirement to assert duplication. This method is referred to as Temporal Deduplication using Directed Acyclic Graphs (TD:DAG). TD:DAG significantly outperformed established ASNM and ASNM+LCS methods for datasets rep-resenting two disparate domains, COVID-19 government policy data and PlayStation Network (PSN) trophy data. TD:DAG produced highly effective and comparable F1 scores of 0.960 and 0.972 for the two datasets, respectively, versus 0.864/0.938 for ASNM+LCS and 0.817/0.708 for ASNM.

We gain insight to the COVID-19 pandemic response by the various U.S. states through analysis of open source emergency declaration, mitigation, and response policy data. We propose ASNM + POD, a Partial Ordering Detection extension to the Adaptive Sorted Neighborhood Method to identify redundancies and implied temporal ordering requirements to understand how various U.S. states respond to COVID-19. We further strengthen the well-established ASNM entity matching method and address key limitations of its Longest Common Subsequence extension (ASNM + LCS) through detection of all temporal order requirements. Partial order requirements are determined probabilistically through empirical review of all records’ time-ordered event sequences. We demonstrate effectiveness against a COVID-19 U.S. state policy dataset comprised of daily time-series data pulled from February and October 2022, where attributes are partially and variably populated. ASNM + POD yielded an F1 of 0.995 and an MCC of 0.985, significantly outperforming both ASNM and ASNM + LCS with F1/MCC improvements of 22%/50% and 15%/37%, respectively. Finally, we highlight the limited consensus on policies enacted, the variability in timelines of policy activations/deactivations, and activity at and after the two-year mark.

Much research has been conducted to detect vulnerabilities of Web Applications; however, these never proposed a methodology to measure the vulnerabilities either qualitatively or quantitatively.In this paper, a methodology is proposed to investigate the quantification of vulnerabilities in Web Applications.We applied the Goal Question Metrics (GQM) methodology to determine all possible security factors and subfactors of Web Applications in the Department of Transportation (DOT) as our proof of concept.Then we introduced a Multi-layered Fuzzy Logic (MFL) approach based on the security sub-factors' prioritization in the Analytic Hierarchy Process (AHP).Using AHP, we weighted each security sub-factor before the quantification process in the Fuzzy Logic to handle imprecise crisp number calculation.

Integration of relevant hands-on cybersecurity content into laboratories supporting our required operating systems course helps to provide all computing students a baseline level of cybersecurity knowledge, even if those students never enroll in a dedicated cybersecurity course. However, these hands-on laboratories often require rebuilding of kernels, capturing network traffic, running offensive cybersecurity tools, so that they either require dedicated computer infrastructure, thus increasing the costs in budget-strapped higher education institutions or frequent administrative actions in case of shared infrastructure. To address this problem, we have developed a low-cost single-board computing constellation and support infrastructure as a cost-effective means of delivering both instruction in traditional operating systems topics and related cybersecurity concepts, such as two-factor authentication and isolation via virtualization. Use of physical infrastructure instead of virtualized laboratory infrastructure has several advantages. The computing constellation offers simplified configuration and management, flexibility, isolation, deployment of diverse laboratory assignments, and uniformity of user experience. The use of physical infrastructure built upon a low-cost single board computer makes the computing constellation scalable to address rising enrollments. This paper includes an overview of the security-focused laboratories added to the operating systems course, and the construction and operation of the computing constellation infrastructure so that other educators may replicate the infrastructure and integrate similar hands-on cybersecurity laboratories into their existing computing curriculum.

Network security is one of the crucial components of an organization’s security system. Much research has been conducted to come up with a clear-cut approach in order to quantify organizations’ network system vulnerabilities. Many security standards such as NIST SP-800 and ISO 27001 with the guidelines and clauses are published with a reasonable outline to pave the ground for a safe track towards secure system design, however, these standards do not clearly show the details of work implementation. In this paper, we apply Fuzzy Logic methodology to quantify each factor and sub-factors derived using Goal Question Metrics in network security. Our procedure follows a bottom-up hierarchy model from the details of a security component to the desired goal in order to address vulnerabilities in a quantified manner in the Department of Transportation. Thus, our approach measures different types of potential vulnerabilities in a network.

Quantifying vulnerabilities of network systems has been a highly controversial issue in the fields of network security and IoT.Much research has been conducted on this purpose; however, these have many ambiguities and uncertainties.In this paper, we investigate the quantification of vulnerability in the Department of Transportation (DOT) as our proof of concept.We initiate the analysis of security requirements, using Security Quality Requirements Engineering (SQUARE) for security requirements elicitation.Then we apply published security standards such as NIST SP-800 and ISO 27001 to map our security factors and sub-factors.Finally, we propose our Multi-layered Fuzzy Logic (MFL) approach based on Goal question Metrics (GQM) to quantify network security and IoT (Mobile Devices) vulnerability in DOT.

Of particular importance for an organization in building an effective and comprehensive secure system is to addressing a mechanism to provide a standard framework that is free from vulnerabilities. Cybersecurity experts and security requirement engineers have been addressing security issues that originated from cybersecurity requirements. Many security issues can be avoided if the security requirements are configured appropriately. In this paper, we proposed a hierarchy security requirements model based on the Goal Question Metrics (GQM) and its application mapped with the security standards towards constructing vulnerability measurements at the early stage of security development of the system design.

The integration of driver assistance and autonomous driving capabilities has increased the complexity of automotive software, making the verification of software updates more challenging. The utility of simulators in software verification is limited in terms of their level of fidelity to real-world hardware and driving conditions. The use of physical test vehicles on a test track is both costly and not representative of real-world conditions and vehicle configurations.

When matching different semantically tagged web service interfaces in the semantic web, it is often necessary to map one ontology to another ontology at runtime, on the fly. In many cases, knowing which nodes are the best nodes to start the mapping process before the process begins (these are called anchor nodes) can make the ontology mapping much easier. Traditionally, all anchor node selection has been performed by humans. However, this is not practical for situations on the fly, at runtime. Here, we validate a methodology for determining anchor nodes automatically. Our results are shown to be successful, in that we were able to create equations based on various metrics that enabled us to fairly accurately identify anchor nodes.