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Lateral Movement Attacks Datasets: Benchmarking, Challenges, and Solutions
Get PDFAdvanced Persistent Threats (APTs) pose a significant cybersecurity risk by lever- aging sophisticated techniques, with lateral movement (LM) playing a central role in these attacks. Lateral movement allows adversaries to navigate through compro- mised networks, escalating privileges, and gaining access to critical resources over extended periods. However, the detection of lateral movement has been hindered by a lack of comprehensive, high-quality datasets that accurately reflect the diverse and evolving tactics used in such attacks. Existing datasets suffer from several limita- tions, including a scarcity of lateral movement instances, outdated attack patterns, and insufficient diversity in techniques and attack paths, especially in cloud-based environments. Moreover, automatic labeling methods for dataset creation are often imprecise, complicating the training of effective detection models. This work addresses these challenges by proposing a new benchmark dataset specifically tailored for lateral movement attacks. We conduct a comprehensive anal- ysis of existing lateral movement attack datasets, highlighting gaps and providing insights into the strengths and weaknesses of current approaches. In response, we in- troduce the Lateral Movement Dataset Generator (LMDG), a framework designed to generate high-quality datasets for lateral movement and APT detection. The LMDG framework automates the generation of benign network traffic, simulates realistic at- tack scenarios, and incorporates an innovative labeling technique called process tree labeling, which improves the accuracy of automatic labeling compared to existing methods. Our contributions offer significant advancements in the development of lateral movement detection systems. The new dataset provides a valuable resource for train- ing and evaluating machine learning models, while the LMDG framework offers a reproducible toolset for generating datasets that accurately represent real-world at- tack behaviors. This work lays the foundation for future research into multi-stage APT detection, enabling the development of holistic systems that can better defend against the evolving landscape of sophisticated cyber threats
Cross-Domain Recommendations Via Aspect Sentiment Feature Extraction using Large Language Models (CRAS-LLM)
No full textCross-domain recommendation systems (CDRS) are designed to suggest products from one domain to another by analyzing data (i.e., user reviews) across multiple domains, like movies and music. CDRS addresses data sparsity (i.e., insufficient data) and cold-start (i.e., no data) problems by capturing information from a source domain (i.e., movies) to target domain (i.e., music) to enhance performance. Traditional CDRS employ various sentiment analysis techniques on user reviews to extract and map user preferences across domains. CRAS2024 only works with positive sentences within a review, discarding the negative sentences causing information loss. It then uses Biterm Topic Model (BTM), a probabilistic topic modeling technique for extracting topics from short texts by analyzing how words co-occur, to extract aspects from the remaining positive sentences. It then uses GloVe (Global Vectors for Word Representation), a statistical word embedding method, but lacks contextual understanding, making it unable to differentiate words like “bank” in different contexts. RC-DFM2019 combines user reviews and item content (i.e., descriptions) with rating data using Stacked Denoising Autoencoders (SDAEs) to learn user preferences and item features. However, it struggles with sparse data in domain where user has little, and it has cold-start issues if there are not enough shared users between domains. GA2021 (Graphical and Attention Framework) is a graph-based approach utilizing Node2Vec for graph embeddings and uses element-wise attention mechanism to combine features from different domains. However, it struggles with sparse data. This thesis proposes Cross-Domain Recommendations via Aspect-Sentiment Feature Extraction using Large Language Models (CRAS-LLM), enhancing CRAS2024 that considers user reviews irrespective of their polarity. Unlike CRAS2024, aspects from user reviews are extracted using DeBERTa (Decoding-enhanced BERT with disentangled attention) by retaining both positive and negative aspects from user reviews based on their context. Then, SimCSE-BERT (Simple Contrastive Learning of Sentence Embeddings with BERT) generates contextual embeddings of the resultant aspects and are optimized using contrastive loss, bringing embeddings of similar sentiments closer and pushing those with dissimilar sentiments farther apart. Cross-Domain mapping is then performed on these embeddings using CycleGAN (Cycle Generative Adversarial Network), which maps these embeddings into target domain embeddings. Lastly, similarity scores between embeddings generated from product reviews of the target domain and mapped aspect embeddings from the source domain are calculated. Our experimental results across the Movie, Music, and Book domains—evaluated in four cross-domain settings (Movie→Music, Music→Movie, Book→Movie, and Music→Book)—demonstrate that CDR-LLM consistently outperforms leading methods including CRAS2024, PTUPCDR2022, and CATN2020, as well as other state-of-the-art approaches. CDR-LLM reduces MSE by approximately 12.5%, while its precision improves by 15%, recall increases by about 16%, and the F1-score is improved by nearly 11% relative to the baselines systems
On maximum likelihood estimators for a jump-type affine diffusion two-factor model
No full textWe consider a jump-type two-factor affine diffusion model driven by a subordinator in the context of continuous time observations. We study the asymptotic properties of the maximum likelihood estimator (MLE) for the drift parameters. In particular, we prove the strong consistency and the asymptotic normality of MLE in the subcritical case. We also present some numerical illustrations to confirm the theoretical results. The main difficulty of this major paper consists in proving the ergodicity of the model in the subcritical case and deriving the limiting behavior of the process
Cross-Border Cooperation and Trade in Post-Brexit Northern Ireland
No full textThe border between Northern Ireland (part of the United Kingdom) and the Republic of Ireland (an EU member) has been one of the most contested issues throughout the Brexit process. The possibility of reintroducing border checkpoints has reignited fears of sectarian conflict in the border region and presented serious obstacles to cross-border cooperation between Ireland, the UK, and the EU. This research provides a historical and institutional background on the border and the Good Friday Agreement, which was put in place in 1998 to bring peace to Northern Ireland and the border region. Explanations of the effects of Brexit on the Good Friday Agreement and cross-border cooperation are also examined, as well as current barriers to Brexit negotiations -- cross-border cooperation, trade and, customs agreements -- through a comparative study between the Irish border and the Swedish-Norwegian border. The likely conclusions drawn from this research will outline the problems to be faced throughout remaining Brexit negotiations and provide possible solutions and scenarios of trade regulation for the Irish border. This study relates to "Understanding and Optimizing Borders" as it seeks to provide a deeper understanding of logistical, economical, political, and trade issues associated with the Irish border, which are currently some of the most difficult issues to confront in world politics
The Consequences of the Supreme Court of Canada's Approach to the Exclusive Jurisdiction of Labour Arbitrators
No full text['UNSDG 8: Decent Work and Economic Growth (https://sdgs.un.org/goals/goal8)', 'UNSDG 10: Reduced Inequalities (https://sdgs.un.org/goals/goal10)', 'UNSDG 16: Peace, Justice and Strong Institutions (https://sdgs.un.org/goals/goal16)', 'Convention on the Rights of Persons with Disabilities (CRPD) - https://www.un.org/development/desa/disabilities/convention-on-the-rights-of-persons-with-disabilities.html']Viable, Healthy and Safe CommunitiesThis project intends to examine the approach the Supreme Court of Canada has taken to articulating the jurisdiction of labour arbitrators, and how its expansive framing of exclusive jurisdiction in the labour arbitration context limits access to justice for unionized workers. To set context, the project will outline the Supreme Court's series of decisions expanding the scope of the exclusive jurisdiction of labour arbitrators, beginning with St. Anne Nackawic in 1986 and Weber in 1995, and more recently culminating in Horrocks in 2021, emphasizing the scope of rights and interests that unionized workers lose the ability to protect if their unions opt to not pursue a grievance. Building on the jurisprudential background, the project will then combine data relating to the disposition of duty of fair representation proceedings in various Canadian jurisdictions with qualitative discussion surrounding the relatively low bar unions must clear to demonstrate that they met their duty of fair representation, suggesting that the duty of fair representation is not much of a safeguard in cases where unions opt to not take up members' grievances. Finally, through an access to justice lens, this project will discuss the wider public policy implications of some unionized workers essentially being denied an avenue to assert highly individual rights, such as the right to be free from discrimination
A simplified model for simulating flow around automotive fans using computational fluid dynamics (CFD)
No full textSustainable IndustryComputational fluid dynamics (CFD) is a field which makes use of mathematics, physics and computer softwares to predict the flow of a gas or liquid - and also how these fluids affect the objects around them. For automotive engineers, CFD provides an easier way to evaluate the performance of automotive fans. It does so by eliminating the necessity of physically constructing fan models for experimental testing, saving time and money. A "CFD simulation" usually involves constructing a computer-aided design (CAD) model of an object and then using software to compute the flow field around it under some specified conditions. The complexity of the simulation depends on the complexity of the object being investigated. Building a CAD model of a fan with complex blade shapes (such as the ones found in car radiators) can prove to be a computationally expensive and time-consuming task. However, this work presents an alternative CFD approach to simulating fan flows using the concept of "body forces". A body force model-based simulation calculates the flow field through a fan by approximating the forces that a given fan design would exert on the fluid. By eliminating the need to construct 3D CAD models, this approach greatly simplifies the process of evaluating the fan performance thus making simulations even faster, allowing better design decision to be made
Detecting Volatile Organic Compounds from Cadaveric Decomposition using Polydiacetylene
No full text['UNSDG 9: Industry, Innovation and Infrastructure (https://sdgs.un.org/goals/goal9)']Viable, Healthy and Safe CommunitiesPolydiacetylenes (PDA) are a class of polymers that have been used extensively in materials chemistry. This unique conjugated polymer has rich optical properties that can be used for sensing of various analytes. These PDA-based sensors mainly focus on the colorimetric or fluorescent properties, where the polymer can be classified in active or inactive phases, and can be utilized in a wide variety of applications. This immediate optical change makes PDA sensing a great option for in-field forensic utilization. Since most forensic testing is bulky, expensive, or requires training and expertise to use, a small and portable PDA based sensor would revolutionize forensic investigation. In this research project, PDA-based sensing is evaluated in the emerging field of forensics to examine volatile organic compounds (VOCs) released from cadaveric decomposition. With this new optical sensor platform that reacts to these VOCs, post-mortem intervals, stage and rate of decomposition will be determined proactively by onsite forensics, before the body is sent away for further testing, opening new opportunities for the portable and accurate detection of various VOCs at the point-of-use
“These are fragments of our experiences of this cruel war… Starving and freezing, constantly fearing for our lives.”: A bottom-up history of the forced displacements of Polish civilians from lands annexed directly into the German Reich, 1939-1941
Get PDFThis MRP fills a gap in the historiography on the forced displacements of Polish civilians by the Nazi German administration between 1939-1941 from lands annexed directly into the German Reich onto the Generalgouvernement. Drawing on a set of forty-four primary sources consisting of written memoirs and video interviews, this MRP constructs a bottom-up history of the evictions, temporary transit camps, and train journeys that the forcibly displaced Poles were forced to endure. Contrasting previous works, this MRP describes how different groups of people reacted – varying in age, urbanicity, location, and profession, in particular also looking at the perspective of children. It extends the description to the pre-war lives and to the beginnings of the German Occupation of Poland through the eyes of the forcibly displaced Poles, creating partial microhistories, as well as considering the new identities of the forcibly displaced as exiles – wygnańce. It gives a voice to the primary source, while continually examining them against Polish-language and English-language secondary sources on the topic
A Day in the Life
No full textWhat does the mind of a person who has depression and anxiety experience
Increasing Stretchability of Conjugated Polymers Using Metal-Ligand Coordination
No full textSustainable IndustryStretchable and mechanically robust materials are now becoming crucial for the development of wearable electronics. In particular, semiconducting conjugated polymers have been shown to be remarkable candidates when preparing new electronic devices as the exhibit good charge transport properties, synthetic versatility and easy tunability. In recent years, the development of these types of materials have been utilized the use of dynamic crosslinking, especially metal-ligand interactions, is a promising avenue to prepare and design stretchable materials while also enabling novel properties such as self-healing. However, in their synthesis and application, there are many challenges overcome to achieve stretchable conjugated polymers, due to the intrinsic competition between electronic and mechanical properties. The objective of the project is to develop a novel strategy towards developing intrinsically stretchable and self-healing conjugated polymers for application in stretchable electronics. This main objective will be achieved by incorporating metal coordinating moieties, namely imine side-chains, to the polymer in order to chelate to Iron(II). This dynamic coordination will allow for the polymer network to dissipate strain, thus enhancing the mechanical properties of the materials. Moreover, this will also allow for regeneration of the polymer network after being damaged through a process known as self-healing. This presentation will discuss our recent progress toward new metal-coordinating conjugated polymers, especially focusing on their design and preparation