Security

These datasets were generated in a computer network environment where eXfiltration Advanced Persistent Threats were launched against a number of high-value targets.

It is the alert log of the Security Onion SIEM which aggregates alerts from network and host-based intrusion detection systems that are securing the network environment.

Efficient intrusion detection and analysis of the security landscape in big data environments present challenge for today's users. Intrusion behavior can be described by provenance graphs that record the dependency relationships between intrusion processes and the infected files. Existing intrusion detection methods typically analyze and identify the anomaly either in a single provenance path or the whole provenance graph, neither of which can achieve the benefit on both detection accuracy and detection time.

The raw EEG signals are collected from seven adult participants (a~g, 4 males and 3 females, their ages range from 21 to 45, the average age is 24.71 and the average deviation is 6.49). None of them has a case history of brain injury or brain disease. the "EMOTIV EPOC+"EEG head-worn device is employed, which has a total of 14 channels, namely: AF3, AF4, F3, F4, F7, F8, FC5, FC6, T7, T8, P7, P8, O1 and O2. The sampling frequency is 128Hz and the signals can generate 128 sample points per second per channel. 

Desktops and laptops can be maliciously exploited to violate privacy. In this paper, we consider the daily battle between the passive attacker who is targeting a specific user against a user that may be adversarial opponent. In this scenario, while the attacker tries to choose the best vector attack by surreptitiously monitoring the victim’s encrypted network traffic in order to identify user’s parameters such as the Operating System (OS), browser and apps. The user may use tools such as a Virtual Private Network (VPN) or even change protocols parameters to protect his/her privacy.