The OFMC back-end in AVISPA is used to carry out security verification experiments in our scheme for the login and authentication phase,  Case1 in the password and biometric renewal phase, and Case2 in the password and biometric renewal phase, respectively.

Here's the experimental result for the login and authentication phase as a show.

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This is a dataset is an example of a distribution of 20 correlated Bernoulli random variables.

Instructions: 

Q_joint ... is 5 cells each consists of the joint distributions of 4,8,12,16,20 bits, respectively. The dimension of each cell is 2^n X 1, .e., a vertical column and n=4,8,12,16,20.

Q_conditional... is 5 cells each consists of the conditional distributions of 4 bits given 0, 4, 8,12,16 bits, respectively. In other words, 1:4 bits, 5:8 bits given 1:4 bits, 9:12 bits given 1:8 bits, 13:16 bits given 1:12 bits, 17:20 given 1:16 bits. The dimension of each cell is 2^4=16 X 2^n, i.e., a vertical column and n=4,8,12,16.

Q_ marginal... is 5 cells each consists of the marginal distributions of each 4 consecutive bits, i.e., 1:4 - 5:8 - 9:12 - 13:16 - 17:20, respectively.  The dimension of each cell is 16 X 1, i.e., q vertical column.

Also, a MATLAB code is uploaded to extract conditional and marginal distributions from any given discrete distribution.

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This dataset is a hand noted dataset that consists of two categories, evasion and normal methods. By evasion methods we mean the methods that are used by Android malware to hide their malicious payload, and hinder the dynamic analysis. Normal methods are any other methods that cannot be used as evasion techniques. Also, the evasion methods are categorized into six categories: File access, Integrity check, Location, SMS, Time, Anti-emulation. This dataset can be used by any ML or DL approaches to predict new evasion techniques that can be used by malware to hinder the dynamic analysis.

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Cyber-physical systems (CPS) have been increasingly attacked by hackers. Recent studies have shown that CPS are especially vulnerable to insider attacks, in which case the attacker has full knowledge of the systems configuration. To better prevent such types of attacks, we need to understand how insider attacks are generated. Typically, there are three critical aspects for a successful insider attack: (i) Maximize damage, (ii) Avoid detection and (iii) Minimize the attack cost.

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Communication of selected devices captured on LAN. The communication is stored in pcapng format. Files captures device's communication such as: connection to a network, configuration and other activities specific to each device.

Selected devices are:

- TP-LINK LB110

- TP-LINK HS100

- NETATMO smart radiators valves

- BML smart IP camera

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  Physical layer secret key generation is a promising candidate to achieve one-time-pad encryption approach for the wireless communication system. However, in a static environment, the secret key rate is low due to the lack of channel time-variation. To solve this problem, this paper proposes a novel secret key generation scheme assisted by an intelligent reflecting surface with discrete phase shifts. In the scheme, legitimate nodes construct the dynamic time-varying channel by rapidly and randomly switching the phase of IRS elements.

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We generated attack datasets 1 based on real data from Austin, Texas.

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Dataset used in the article "The Reverse Problem of Keystroke Dynamics: Guessing Typed Text with Keystroke Timings". Source data contains CSV files with dataset results summaries, false positives lists, the evaluated sentences, and their keystroke timings. Results data contains training and evaluation ARFF files for each user and sentence with the calculated Manhattan and euclidean distance, R metric, and the directionality index for each challenge instance.

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