This dataset was created using Wireshark. The dataset contains a total of 30 encrypted communication records, 3 records (.pcap) were created for each application. The records were obtained from a mobile device that was connected to the laptop using wifi technology. The laptop was connected to the Internet and contained a running instance of Wireshark to create a record. The telephone had been restarted before each record was created. After connecting to the network, the device was left without user interaction for 5 minutes.

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“ProVerif” is a powerful utility designed to examine “reachability properties,” “correspondence assertions,” and “observational equivalences.” Our protocol modelling addresses both the elemental security requirements, like “impersonation” or “replay” attack, and the most advanced ones, like “perfect forward secrecy” or “password guessing attack.”

Because we had a limited space in our published paper, the program source codes are provided here. The codes can be tested online at "http://proverif16.paris.inria.fr/".

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Port scanning attack is popular method to map a remote network or identify operating systems and applications. It allows the attackers to discover and exploit the vulnerabilities in the network.

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The goal of our research is to identify malicious advertisement URLs and to apply adversarial attack on ensembles. We extract lexical and web-scrapped features from using python code. And then 4 machine learning algorithms are applied for the classification process and then used the K-Means clustering for the visual understanding. We check the vulnerability of the models by the adversarial examples. We applied Zeroth Order Optimization adversarial attack on the models and compute the attack accuracy.

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This dataset's data is from the Alibaba-Security-Algorithm-Challenge, and the related web site is: https://tianchi.aliyun.com/competition/entrance/231694/information

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This dataset is used to illustrate an application of the "klm-based profiling and preventing security attack (klm-PPSA)" system. The klm-PPSA system is developed to profile, detect, and then prevent known and/or unknown security attacks before a user access a cloud. This dataset was created based on “a.patrik” user logical attempts scenarios when accessing his cloud resources and/or services. You will find attached the CSV file associated with the resulted dataset. The dataset contains 460 records of 13 attributes (independent and dependent variables).

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This dataset is used for network anomaly detection and is based on the UGR16 dataset network traffic flows. We used June week 2 to 4 tensors generated from raw flow data to train the models. The dataset includes a set of tensors generated from the whole UGR’16 network traffic (general tensor data) and several sets of port tensors (for specific port numbers). It also includes the trained models for each type of tensor. The tensors extracted from network traffic in the period from July week 5 to the end of August can be used for evaluation. The naming convention is as follows:

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BGP volume data aggregated from RIPE and Route Views based on data used by researchers in other work [1].

[1] Z. Li, A. L. G. Rios, and L. Trajkovic, “Detecting Internet Worms, Ransomware, and Blackouts Using Recurrent Neural Networks,” in 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Toronto, ON, Canada: IEEE, Oct. 2020, pp. 2165–2172.

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The dataset comprises of several files that contain smart grid communication, namely protocols IEC 60870-104 (IEC 104) and IEC 61850 (MMS) in form of CSV traces. The traces were generated from PCAP files using IPFIX flow probe or an extraction script. CSV traces include the timestamp, IP addresses and ports of communicating devices, and selected IEC 104 and MMS headers that are interesting for security monitoring and anomaly detection. Datasets were by obtained partly by monitoring communication of real ICS devices and partly by monitoring communication of virtual ICS applications.

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364 Views

With the large-scale adaptation of Android OS and ever-increasing contributions in the Android application space, Android has become the number one target of malware authors. In recent years, a large number of automatic malware detection and classification systems have evolved to tackle the dynamic nature of malware growth using either static or dynamic analysis techniques. Performance of static malware detection methods degrades due to the obfuscation attacks.

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