scholarly journals Greater awareness of security in aftermath of computer worm

Nature ◽  
1988 ◽  
Vol 336 (6197) ◽  
pp. 301-301
Author(s):  
Seth Shulman ◽  
Joseph Palca
Keyword(s):  
Computer Worm

scholarly journals Hybrid Epidemics—A Case Study on Computer Worm Conficker

PLoS ONE ◽  
2015 ◽  
Vol 10 (5) ◽  
pp. e0127478 ◽  
Author(s):  
Changwang Zhang ◽  
Shi Zhou ◽  
Benjamin M. Chain
Keyword(s):  
Computer Worm

Trusted Computing

2009 ◽  
pp. 343-370
Author(s):  
Jeff Teo
Keyword(s):  
Trusted Computing ◽  
Trusted Platform Module ◽  
The World ◽  
Trusted Hardware ◽  
Computer Attacks ◽  
History Of ◽  
Software Implementations ◽  
Computer Worm ◽  
Open Standards ◽  
Trusted Platform

Computer attacks of all sorts are commonplace in today’s interconnected, globalized society. A computer worm, written and released in one part of the world, can now traverse cyberspace in mere minutes creating havoc and untold financial hardship and loss. To effectively combat such threats and other novel and sophisticated assaults, our network defenses must be equipped to thwart such attacks. Yet, our software-dominated defenses are woefully inadequate (Bellovin, 2001). The Trusted Computing Group (TCG) has embarked on a mission to use an open standards-based interoperability framework utilizing both hardware and software implementations to defend against computer attacks. Specifically, the TCG uses a trusted hardware called the trusted platform module (TPM) in conjunction with TPM-enhanced software to provide better protection against such attacks. While millions of TPMs have been shipped with more expected annually, adoption of trusted computing technology enabled by the devices has been slow, despite escalating security infractions. This chapter will detail a brief history of trusted computing (TC), the goals of the TCG, and the workings of trusted platforms. The chapter will also look into how the TPM enables roots of trust to afford improved trust and security.

Study of the stability of a SEIRS model for computer worm propagation

2017 ◽  
Vol 479 ◽  
pp. 411-421 ◽  
Author(s):  
J.D. Hernández Guillén ◽  
A. Martín del Rey ◽  
L. Hernández Encinas
Keyword(s):  
Worm Propagation ◽  
Computer Worm ◽  
The Stability

Detecting unknown computer worm activity via support vector machines and active learning

2012 ◽  
Vol 15 (4) ◽  
pp. 459-475 ◽  
Author(s):  
Nir Nissim ◽  
Robert Moskovitch ◽  
Lior Rokach ◽  
Yuval Elovici
Keyword(s):  
Support Vector Machines ◽  
Active Learning ◽  
Support Vector ◽  
Vector Machines ◽  
Computer Worm

scholarly journals A Tour of the Computer Worm Detection Space

2014 ◽  
Vol 104 (1) ◽  
pp. 29-33 ◽  
Author(s):  
Nelson Ochieng ◽  
Waweru Mwangi ◽  
Ismael Ateya
Keyword(s):  
Worm Detection ◽  
Computer Worm

Parameter estimation of a susceptible–infected–recovered–dead computer worm model

SIMULATION ◽  
2021 ◽  
pp. 003754972110095
Author(s):  
Yue Deng ◽  
Yongzhen Pei ◽  
Changguo Li
Keyword(s):  
Parameter Estimation ◽  
Equation Model ◽  
Internet Security ◽  
Economic Losses ◽  
Unknown Parameters ◽  
Differential Equation Model ◽  
Computer Worms ◽  
Computer Worm ◽  
Worm Model ◽  
Ensemble Kalman Filtering

Computer worms are serious threats to Internet security and have caused billions of dollars of economic losses during the past decades. In this study, we implemented a susceptible–infected–recovered–dead (SIRD) model of computer worms and analyzed the characteristics and mechanisms of worm transmission. We applied the ordinary differential equation model to simulate the transmission process of computer worms and estimated the unknown parameters of the SIRD model through the methods of least squares, Markov chain Monte Carlo, and ensemble Kalman filtering (ENKF). The results reveal that the proposed SIRD model is more accurate than the susceptible–exposed–infected–recovered–susceptible model with respect to parameter estimation.

Detection of Worms Over Cloud Environment

2018 ◽  
pp. 137-165
Author(s):  
Thangavel M. ◽  
Jeyapriya B. ◽  
Suriya K. S.
Keyword(s):  
Distributed Computing ◽  
False Positive ◽  
Virtual Machines ◽  
False Positive Rate ◽  
Cloud Environment ◽  
Computer Worms ◽  
Positive Rate ◽  
Computer Worm

In recent years, computer worms are the remarkable difficulties found in the distributed computing. The location of worms turns out to be more unpredictable since they are changing quickly and much more refined. The difficulties in gathering worm's payload were recognized for identifying and gathering worm's payloads and the honey pot which is high-intelligent to gather the payload of zero-day polymorphic heterogeneous and homogeneous stages in distributed computing. The Signature-based discovery of worms strategies work with a low false-positive rate. We propose an irregularity based interruption location instrument for the cloud which specifically benefits from the virtualization advancements all in all. Our proposed abnormality location framework is detached from spreading computer worm contamination and it can recognize new computer worms. Utilizing our methodology, a spreading computer worm can be distinguished on the spreading conduct itself without getting to or straightforwardly affecting running virtual machines of the cloud.

scholarly journals Optimizing Computer Worm Detection Using Ensembles

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Nelson Ochieng ◽  
Waweru Mwangi ◽  
Ismail Ateya
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Computer File ◽  
Worm Detection ◽  
Final Model ◽  
Computer Worms ◽  
Space Network ◽  
Modern Computer ◽  
Computer Worm ◽  
Dark Space

The scope of this research is computer worm detection. Computer worm has been defined as a process that can cause a possibly evolved copy of it to execute on a remote computer. It does not require human intervention to propagate neither does it attach itself to an existing computer file. It spreads very rapidly. Modern computer worm authors obfuscate the code to make it difficult to detect the computer worm. This research proposes to use machine learning methodology for the detection of computer worms. More specifically, ensembles are used. The research deviates from existing detection approaches by using dark space network traffic attributed to an actual worm attack to train and validate the machine learning algorithms. It is also obtained that the various ensembles perform comparatively well. Each of them is therefore a candidate for the final model. The algorithms also perform just as well as similar studies reported in the literature.

scholarly journals Dynamics of a controlled discontinuous computer worm system

2020 ◽  
Vol 148 (10) ◽  
pp. 4389-4403
Author(s):  
Wenjie Li ◽  
Jinchen Ji ◽  
Lihong Huang
Keyword(s):  
Computer Worm
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