Information-Disturbance Tradeoff of Individual Attack Against BBM92 Protocol

2010 ◽  
Author(s):  
Chun Zhou ◽  
Wansu Bao ◽  
Xiangqun Fu
Keyword(s):  
Individual Attack

scholarly journals Evaluation of computer network security using attack undirected geography

2019 ◽  
Vol 16 (3) ◽  
pp. 1508
Author(s):  
Chanintorn Jittawiriyanukoon
Keyword(s):  
Network Security ◽  
Computer Network ◽  
Threshold Value ◽  
Clustering Coefficient ◽  
Security Model ◽  
Security Threat ◽  
Network Nodes ◽  
Computer Network Security ◽  
Security Problem ◽  
Individual Attack

<span>To secure a wealth of data traversing the computer network at your fingertips is compulsory. But when attack arises at various parts of the network it is difficult to protect, especially when each incident is investigated separately. Geography is a necessary construct in computer networks. The analytics of geography algorithms and metrics to curate insight from a security problem are a critical method of analysis for computer systems. A geography based representation is employed to highlight aspects (on a local and global level) of a security problem which are Eigenvalue, eccentricity, clustering coefficient and cliques. Network security model based on attack undirected geography (AUG) is familiarized. First, analysis based upon association rules is presented then the attack threshold value is set from AUG. The probability of an individual attack edge and associated network nodes are computed in order to quantify the security threat. The simulation is exploited to validate that results are effective.</span>

Individual Attack on the Improved Multiparty Quantum Secret Sharing Protocol

2011 ◽  
Vol 50 (6) ◽  
pp. 1726-1730 ◽  
Author(s):  
Gan Gao ◽  
Ming Fang ◽  
Li-Ping Wang ◽  
Xue-Ying Cui
Keyword(s):  
Secret Sharing ◽  
Quantum Secret Sharing ◽  
Individual Attack

QUANTUM KEY DISTRIBUTION WITH CLASSICAL ALICE

2008 ◽  
Vol 06 (06) ◽  
pp. 1195-1202 ◽  
Author(s):  
HUA LU ◽  
QING-YU CAI
Keyword(s):  
Quantum Key Distribution ◽  
Key Distribution ◽  
Network Protocol ◽  
Eavesdropping Attack ◽  
Fixed Basis ◽  
Individual Attack

It seems that quantum key distribution (QKD) may be completely insecure when the message sender Alice always encodes her key bits in a fixed basis. In this paper, we present a QKD protocol with classical Alice, i.e. Alice always encodes her key bit in the {|0〉,|1〉} basis (we call it classical {0,1} basis) and the eavesdropper Eve knows this fact. We prove that our protocol is completely robust against any eavesdropping attack and present the amount of tolerable noise against Eve's individual attack. Next, we present a QKD protocol to demonstrate that secure key bits can be distributed even if neither Alice nor Bob has quantum capacities, and extend this idea to a QKD network protocol with numerous parties who have only classical capacities. Finally, we discuss that quantum is necessary in QKD for security reasons, but both Alice and Bob may be classical.

A TWO-STEP CHANNEL-ENCRYPTING QUANTUM KEY DISTRIBUTION PROTOCOL

2010 ◽  
Vol 08 (06) ◽  
pp. 1013-1022 ◽  
Author(s):  
FEN-ZHUO GUO ◽  
FEI GAO ◽  
QIAO-YAN WEN ◽  
FU-CHEN ZHU
Keyword(s):  
Quantum Key Distribution ◽  
Key Distribution ◽  
Entangled States ◽  
Ideal Condition ◽  
Practical Situation ◽  
Information Carrier ◽  
General Individual Attack ◽  
Epr State ◽  
Quantum Key Distribution Protocol ◽  
Individual Attack

A two-step channel-encrypting quantum key distribution protocol is proposed. Using the previously shared EPR pairs as the quantum key, two bits of classical key can be established via one information carrier EPR state on average. In theory, the efficiency of this protocol reaches 100%, and there is no need to consume any entangled states including both the quantum key and the information carriers in ideal condition. The protocol can resist the particular attack that is fatal to other some channel-encrypting schemes. Principally, we prove the security against the most general individual attack of this protocol. Entanglement collapse in practical situation, as well as the realistic implementation of this protocol is also discussed.

Graph state-based quantum authentication scheme

2017 ◽  
Vol 31 (09) ◽  
pp. 1750067 ◽  
Author(s):  
Longxia Liao ◽  
Xiaoqi Peng ◽  
Jinjing Shi ◽  
Ying Guo
Keyword(s):  
Physical System ◽  
Quantum Channel ◽  
Authentication Scheme ◽  
Graph State ◽  
Quantum Authentication ◽  
In The Beginning ◽  
General Individual Attack ◽  
Individual Attack

Inspired by the special properties of the graph state, a quantum authentication scheme is proposed in this paper, which is implemented with the utilization of the graph state. Two entities, a reliable party, Trent, as a verifier and Alice as prover are included. Trent is responsible for registering Alice in the beginning and confirming Alice in the end. The proposed scheme is simple in structure and convenient to realize in the realistic physical system due to the use of the graph state in a one-way quantum channel. In addition, the security of the scheme is extensively analyzed and accordingly can resist the general individual attack strategies.

QUANTUM DIRECT COMMUNICATION BASED ON QUANTUM SEARCH ALGORITHM

2010 ◽  
Vol 08 (03) ◽  
pp. 443-450 ◽  
Author(s):  
CHUAN WANG ◽  
LIANG HAO ◽  
SI YU SONG ◽  
GUI LU LONG
Keyword(s):  
Quantum Communication ◽  
Communication Protocol ◽  
Search Algorithm ◽  
Quantum Secure Direct Communication ◽  
Quantum Search ◽  
Quantum Search Algorithm ◽  
Direct Communication ◽  
Quantum Direct Communication ◽  
Direct Communications ◽  
Individual Attack

Quantum direct communications, including deterministic secure quantum communication and quantum secure direct communication protocol using two-qubit quantum search algorithm are proposed in this paper. Secret messages are encoded by two-qubit unitary operations and exchanged by the two communication parties directly. We discussed the security of the protocol under intercept-resend attack and individual attack. We found that the protocols are secure against eavesdropping attacks.

scholarly journals Deep Learning Classification Methods Applied to Tabular Cybersecurity Benchmarks

2021 ◽  
Vol 13 (03) ◽  
pp. 1-13
Author(s):  
David A. Noever ◽  
Samantha E. Miller Noever
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Classification Problem ◽  
Research Community ◽  
Classification Methods ◽  
Detection Problem ◽  
Neural Network Architecture ◽  
Network Attack ◽  
Feature Importance ◽  
Individual Attack

This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2’s convolutional neural network architecture, the work demonstrates a 97% accuracy in distinguishing normal and attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 54% accuracy. Using feature importance rank, a random forest solution on subsets shows the most important source-destination factors and the least important ones as mainly obscure protocols. It further extends the image classification problem to other cybersecurity benchmarks such as malware signatures extracted from binary headers, with an 80% overall accuracy to detect computer viruses as portable executable files (headers only). Both novel image datasets are available to the research community on Kaggle.

scholarly journals Image Classifiers for Network Intrusions

2021 ◽  
Author(s):  
David A. Noever ◽  
Samantha E. Miller Noever
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Random Forest ◽  
Network Architecture ◽  
Detection Problem ◽  
Neural Network Architecture ◽  
Network Attack ◽  
Network Intrusions ◽  
Feature Importance ◽  
Individual Attack

This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2’s convolutional neural network architecture, the work demonstrates a 97% accuracy in distinguishing normal and attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 56% accuracy. Using feature importance rank, a random forest solution on subsets show the most important sourcedestination factors and the least important ones as mainly obscure protocols. The dataset is available on Kaggle.

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