Veris Technologies (United States)

In today’s digital age, intrusion detection systems help protect IoT (Internet of Things) systems from harmful activities. This study presents a new method using fuzzy clustering and swarm intelligence to spot hackers in wireless networks. More dependence on internet systems has caused a rise in cybersecurity threats, making it clear that traditional security methods are not enough. Swarm intelligence combined with fuzzy logic in the suggested approach allows the system to create flexible clusters that can spot unusual traffic in a network. With swarm intelligence, finding optimal ways to group data is enhanced and fuzzy logic helps cope with cases where things are unclear or share similar traits. With this method, you see more accurate detection and fewer false alarms which works well in the complicated and various settings of IoT environments. It outlines the design, explains how the algorithm works and shows the outcomes of the simulation, verifying that the approach is successful in early threat detection and responding rapidly which helps make IoT security systems more secure.

<div class="section abstract"><div class="htmlview paragraph">Commercial Vehicle (CV) market is growing rapidly with the advancement of Software-Defined Vehicles (SDVs), which provide greater level of flexibility, efficiency and integration of AI & cutting-edge technology. This research provides an in-depth analysis of E&E architecture of CVs, focusing on the integration of SDV-based technology, which represents the transition from hardware-focused to a more dynamic, software-focused methodology. The research begins with the fundamental concepts of E&E architecture in CVs, including virtualization, centralized computing, feature based ECU, CAN and modular frameworks which are then upgraded to meet various operational and customer requirements. The capacity of SDV-based architecture designs to scale to handle heavy duty commercial vehicles is a primary focus, with an emphasis on ensuring the safety and security, to defend against potential vulnerabilities. Furthermore, the integration of real-time data processing capabilities and advanced E&E architecture is proposed, considering features available in the current and potential future market. We also examine current and future market trends in CVs related to the E&E architecture, discussing advanced features such as OTA, fleet management, telematics, and more. This research focuses on adding SDV features to upcoming CVs, with the goal of improving vehicle performance. Future considerations include the integration of autonomous driving technology and the growing significance of cloud and edge computing technology in enhancing commercial vehicles' decision-making capabilities. This research offers a comprehensive understanding of how SDV architecture is poised to revolutionize the heavy-duty commercial vehicle industry, providing insights into how it will make CVs more automated, connected, and efficient.</div></div>

Production of a natural language description for the function of a source code segment is commonly referred to as source code summarization. Useful comments in source code can facilitate more rapid onboarding of new engineers and contribute to decreased maintenance costs. Unfortunately, the documentation task can also be labor intensive. In this paper, we introduce a new model agnostic tool for AI driven automation of source code summarization at scale. The initial version of the adVanced AI Driven Enhancement to Readability for Spurce Code (VADER-SC) software offers numerous options for customization and the ability to leverage a variety of AI models to enable experimentation in resource constrained environments, while also scaling up to benefit from larger models in contexts with increased compute resources. It further supports private cloud, self-hosted, and air-gapped network configurations for environments with strict intellectual property protections or processing of sensitive or controlled data. Qualitative and quantitative results suggest model selection, fine-tuning, and multi-shot tailoring significantly impact the quality of generated comments. VADER-SC could be an enabler for practitioners to explore large-scale automation of AI driven source code summarization and researchers may find it enables studies with larger volumes of disparate data across a diversity of AI model options and target programming languages.

This paper assesses the benefits of using Sliding Windows Forward error codes (SWF) to protect transport protocol sessions over a SATCOM link within IP tunnels. We consider two commonly deployed protocols and congestion control algorithms : TCP/CUBIC, currently deployed by default inside the most recent OS kernels, and QUIC/BBR implementation named Picoquic. Our objective is to evaluate the performance of these protocols in challenging SATCOM environments and to assess if SWF can contribute to improve their performance. We consider two different scenarios based on real loss mobility patterns played over the OpenSAND satellite emulator. Results show that using SWF tunnels can hide losses to a CUBIC server: this reduces the download time of 20MB by more than 90 %. However, the main finding is that SWF does not contribute to the download time reduction for BBR, making its deployment ineffective. We conclude that the use of BBR over SATCOM could be an efficient way to perform communications over unreliable links, resulting from a high mobility context for instance, considering that BBR flows are managed by an adequate QoS allocation.

ABSTRACT Information security is an imperative factor in organizational success, driven by the need to protect information assets. The continuous evolution of external and internal threats and the associated need to protect and secure information from exploitation of vulnerabilities has become a struggle for many organizations in both the public and private sectors. This struggle is the direct result of the narrow focus on operational security. Just as the lines between business and information technology have disappeared, so have the lines between business and information security. Some organizations simply “check the box” by performing the minimum actions required to pass or meet mandated compliance standards. Without practicing due diligence and by only meeting the minimum requirements, leads to the reactive response of exploited vulnerabilities in addition to the increase of after the fact incident investigations. Organizations need to take a proactive approach using established methodologies known to incorporate security into information technologies and systems. The Sherwood Applied Business Security Architecture (SABSA) is a solution oriented methodology for any business enterprise that seeks to enable its information infrastructure by applying security solutions within every layer of the organization. This article describes how SABSA can be integrated into organizations' existing architectures utilizing organizational business drivers.

Comment on: Chan E, et al. Cell Cycle 2011; 10:1845-52.

The measurement of soil properties on site-specific basis is desired for modern production agriculture. This paper addresses this need using an on-the-go in-situ spectrophotometer to acquire NIR reflectance spectra of soil. The spectral data is optionally augmented with electrical conductivity, temperature, and pH sensor data. Calibrations are a particular problem in that they may need to be optimized for particular soils and temporal conditions in order to achieve acceptable accuracy. This work tests the effectiveness of locally weighted partial least squares regression (LWPLS), a recently developed memory-based learning algorithm, in creating calibrations for the measurement of soil properties. As its name implies, the algorithm inherently optimizes predictions based upon the data space near the query point. LWPLS is used to create calibrations for multiple soil properties using two data sets with measurements from a total of 7 fields. In comparisons with three classical regression algorithms, LWPLS is found to produce calibrations with the highest accuracy for the majority of soil properties. None of the algorithms showed a significant advantage in improving calibrations when the spectral data was augmented with scalar sensor data.