With the addition of brick slabs and stone brick slabs, two of the smooth stone blocks have been effectively replaced with the double slabs of these, effectively removing them in the game and leaving only two remaining visually-identical smooth stone blocks. These are currently obtainable only through inventory editing.Ĭhanges to piston mechanics now allow for the creation of block transmutation machines, allowing for smooth stone (4-7, obtainability of 8-15 unknown ) to be obtained in survival without inventory editors. Its inclusion within the game was likely accidental, as this version also added sandstone, cobblestone and petrified oak slabs to the game as such, the four remaining data values were left undefined, and probably defaulted to using the smooth stone slab texture due to it being the default texture for its block ID, but applying it to all sides instead. Smooth stone slabs arose as a double slab form of the "seamless" stone slab block, and would drop two of their single versions when broken. There are actually twelve smooth stone blocks, which are visually identical. ↑ The block's direct item form has the same id with the block.↑ ID of block's direct item form, which is used in savegame files and addons.Journal of artificial intelligence research, 16: 321–357, 2002.While the block is in the process of being broken Smote: synthetic minority over-sampling technique. Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. A feature classification scheme for network intrusion detection. Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Ali Shiravi, Hadi Shiravi, Mahbod Tavallaee, and Ali A Ghorbani. The large number of features space combined with having a balanced version of the data provides the research community with a good dataset to test algorithms and analyse the effect of various parameters on the quality of generated models. Clean up: any useless features were removed, before source and destination zone features were added, to reduce the large address space. All synthetic records are identifiable by the ‘synthetic’ variable.ĥ. This balancing phase used the SMOTE algorithm. Balance: synthetic records (connections) were generated to balance the number of Normal and Attack connections in the dataset by generating synthetic records of the attack connections. Part of Onut’s feature classification schema was used in this phase.Ĥ. Deriving these features depended on the chronological order of the original connections. Extend the basic-features: every connection was processed to derive two sets of features (time-based and connection-based). Validation and connection labelling: the accurate capture of every (ICMP, TCP, UDP) packet in every PCAP file was validated, then every processed connection (in the PCAP files) was matched to its corresponding flow in the XML file (in UNB ISCX 2012 dataset) using the label provided. These features consisted of information that can be extracted from frame and packet headers such as the source and destination IP addresses and ports, connection duration, transport protocol etc.Ģ. Basic-features extraction: every PCAP file (in UNB ISCX 2012 dataset) was processed using Bro software to extract 193 features for every ICMP, TCP and UDP connection. ![]() Overall, the transformation process had five main stages:ġ. The STA2018 dataset contains the profiled sessions (connections) of the network traffic of five simulation days, where data records are grouped by day so that every data file aggregated all of the connections within that simulation day. The generation process used traffic trace files to extract 193 basic features, which were then extended to 550 features by employing part of Onut’s feature classification schema (549 independent variables plus one dependent (class) variable). STA2018 is a new dataset generated by transforming the network traces of the UNB ISCX Intrusion Detection Evaluation DataSet 2012 into a suitable format for Machine Learning (ML) and Data Mining (DM) tasks.
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