[HTML][HTML] Cybersecurity threats and their mitigation approaches using Machine Learning—A Review

M Ahsan, KE Nygard, R Gomes… - … of Cybersecurity and …, 2022 - mdpi.com
… Another example is the application of various clustering techniques (K-means, DBSCAN
and Hierarchical) [79]. Clustering is useful for malware detection, phishing attack detection, …

[PDF][PDF] Prevention of cooperative black hole attack in wireless ad hoc networks.

…, H Fu, M Sreekantaradhya, J Dixon, KE Nygard - … conference on wireless …, 2003 - Citeseer
Mobile ad hoc networks (MANETs) are extensively used in military and civilian applications.
The dynamic topology of MANETs allows nodes to join and leave the network at any point of …

Gideon: A genetic algorithm system for vehicle routing with time windows

SR Thangiah, KE Nygard, PL Juell - Proceedings The Seventh IEEE …, 1991 - computer.org
… to ‘ڴ’ at level (k+1) as children to topic node t(T+k)j. o Step 3.3: k=k+1 Starting with a single
topic, tT, … Assuming that ഠT contains K number of topics, this is denoted by ןሺ ǡᐚ୘ǡ ሻ and is …

Threat-driven modeling and verification of secure software using aspect-oriented Petri nets

D Xu, KE Nygard - IEEE transactions on software engineering, 2006 - ieeexplore.ieee.org
Design-level vulnerabilities are a major source of security risks in software. To improve
trustworthiness of software design, this paper presents a formal threat-driven approach, which …

Dynamic network flow optimization models for air vehicle resource allocation

KE Nygard, PR Chandler… - Proceedings of the 2001 …, 2001 - ieeexplore.ieee.org
A weapon system consisting of a swarm of air vehicles whose mission is to search for,
classify, attack, and perform battle damage assessment, is considered. It is assumed that the …

[HTML][HTML] Enhancing machine learning prediction in cybersecurity using dynamic feature selector

…, R Gomes, MM Chowdhury, KE Nygard - Journal of Cybersecurity …, 2021 - mdpi.com
Machine learning algorithms are becoming very efficient in intrusion detection systems with
their real time response and adaptive learning process. A robust machine learning model …

Agent-oriented designs for a self healing smart grid

…, J Tang, D Loegering, KE Nygard - 2010 first IEEE …, 2010 - ieeexplore.ieee.org
Electrical grids are highly complex and dynamic systems that can be unreliable, insecure,
and inefficient in serving end consumers. The promise of Smart Grids lies in the architecting …

Preoperative radiotherapy for esophageal carcinoma

…, H Hansen, B Launois, K Nygaard… - Cochrane database …, 1996 - cochranelibrary.com
Background The existing randomized evidence has failed to conclusively demonstrate the
benefit or otherwise of preoperative radiotherapy in treating patients with potentially …

[PDF][PDF] Ligand-dependent nuclear receptor corepressor LCoR functions by histone deacetylase-dependent and-independent mechanisms

I Fernandes, Y Bastien, T Wai, K Nygard, R Lin… - Molecular cell, 2003 - cell.com
LCoR (ligand-dependent corepressor) is a transcriptional corepressor widely expressed in
fetal and adult tissues that is recruited to agonist-bound nuclear receptors through a single …

[PDF][PDF] Convolutional Neural Networks with LSTM for Intrusion Detection.

M Ahsan, KE Nygard - CATA, 2020 - academia.edu
… Mini batch K-means clustering produced very good accuracy by using K-means principal
idea of allocating different random groups of distinct memory sizes, which facilitates the easiest …