Results 51 to 60 of about 10,938 (187)
CALIPER: Continuous Authentication Layered with Integrated PKI Encoding Recognition
, 2016 Architectures relying on continuous authentication require a secure way to challenge the user's identity without trusting that the Continuous Authentication Subsystem (CAS) has not been compromised, i.e., that the response to the layer which manages ...
Boult, Terrance E., Rudd, Ethan M.
core +1 more source
MalGrid: Visualization of Binary Features in Large Malware Corpora
MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM), 2022 The number of malware is constantly on the rise. Though most new malware are modifications of existing ones, their sheer number is quite overwhelming. In this paper, we present a novel system to visualize and map millions of malware to points in a 2-dimensional (2D) spatial grid. This enables visualizing relationships within large malware datasets that
Mohammed, Tajuddin Manhar +4 more
openaire +2 more sources
Visualizing Sensor Network Coverage with Location Uncertainty
, 2017 We present an interactive visualization system for exploring the coverage in sensor networks with uncertain sensor locations. We consider a simple case of uncertainty where the location of each sensor is confined to a discrete number of points sampled ...
Hair, Jessica +3 more
core +1 more source
From Ambiguous Queries to Verifiable Insights: A Task‐Driven Framework for LLM‐Powered SOC Analysis⋆
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Security operations centre (SOC) analysts must investigate alerts, correlate threat intelligence and interpret heterogeneous telemetry under tight timing constraints. Although large language models (LLMs) offer strong understanding capabilities, directly applying them to SOC environments remains challenging due to semantic ambiguity in analyst
Huan Zhang +5 more
wiley +1 more source
NEMESYS: Enhanced Network Security for Seamless Service Provisioning in the Smart Mobile Ecosystem
, 2013 As a consequence of the growing popularity of smart mobile devices, mobile malware is clearly on the rise, with attackers targeting valuable user information and exploiting vulnerabilities of the mobile ecosystems.
Baltatu, Madalina +6 more
core +1 more source
Generating Pattern‐Based Datasets for Cyber Attack Detection Using Machine‐Learning Techniques
WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 2, June 2026.The aim of this work is to review the state of the art in the design, generation, and labeling of attack pattern datasets for training of detection systems based on machine learning. ABSTRACT This work aims to review the state of the art in the design, generation, and labeling of attack pattern datasets for the training of detection systems based on ...
Pedro Díaz García +4 more
wiley +1 more source
Android Malware Familial Classification Based on DEX File Section Features
IEEE Access, 2020 The rapid proliferation of Android malware is challenging the classification of the Android malware family. The traditional static method for classification is easily affected by the confusion and reinforcement, while the dynamic method is expensive in ...
Yong Fang +3 more
doaj +1 more source
MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network.
International Journal of Interactive Multimedia and Artificial Intelligence, 2022 Technological advancement of smart devices has opened up a new trend: Internet of Everything (IoE), where all devices are connected to the web. Large scale networking benefits the community by increasing connectivity and giving control of physical ...
Mahendra Deore, Uday Kulkarni
doaj +1 more source
"Influence Sketching": Finding Influential Samples In Large-Scale Regressions
, 2017 There is an especially strong need in modern large-scale data analysis to prioritize samples for manual inspection. For example, the inspection could target important mislabeled samples or key vulnerabilities exploitable by an adversarial attack.
Crable, Caleb +6 more
core +1 more source
Accelerated‐USE: A Benchmark Framework for GPU‐Driven Graph Neural Network Training
Concurrency and Computation: Practice and Experience, Volume 38, Issue 9, May 2026.ABSTRACT Graph processing is used in many domains to extract knowledge from real‐world data. With the rise of deep neural networks and scaled compute infrastructure in artificial intelligence (AI), specialized techniques emerged to leverage graphs in applications such as recommendation systems and social networks.
Lucas de Angelo Martins Ribeiro +5 more
wiley +1 more source