dataesr/scientific-paragraphs-categorization huggingface dataset:
Brain Coverage of Functional Activity A comprehensive brain coverage was already attained from the overall set of tasks comprising the rst release of the IBC dataset (see Figure  3 ). All brain areas are covered by this map. Stronger eects are observed in medial sections of the occipital, parietal and frontal lobes as well as lateral areas of the occipital lobe plus posterior portions of both parietal and frontal lobes. By contrast, some temporal, prefrontal and sub-cortical regions display weaker eects. Future releases may thus encompass tasks extensively addressing high-level visual object discrimination, including face selection, but also reward, decision-making and episodic memory. However, one must notice that brain coverage may not be fully attained due  This rst release brings together a wide range of tasks covering many psychological domains.
It features a total of 59 independent contrasts along with high brain coverage (see Section 4. 3 for further details). The collection of new data continues till year 2022 and more releases, e. g. especially dedicated to particular cognitive domains, are expected for the upcoming years. For instance, data from a set of tasks tackling in detail the visual system, such as visualization of naturalistic scenes or classic-retinotopy tasks, are expected in the upcoming releases. Moreover, tasks addressing higher cognitive functions pertaining to a ner coverage of prefrontal areas, like incentive salience or chronesthesia, are also included in the medium-term plan of the acquisitions. Ultimately, a comprehensive brain coverage of functional signatures linked to a large variety of cognitive functions is expected by the end of the project.
In addition, future releases will also include high-resolution T1-, T2-and diusion-weighted MRI images as well as T1-and T2-relaxometry data and myelin water fraction estimates. The present release applies only BIDS Specication to data source. Nonetheless, a new version of BIDS providing support for preprocessed data is expected in the near future. The upcoming releases of the IBC dataset should comply with the normative organization of preprocessed data.
) and then with secondary antibody (Donkey anti-Rabbit Alexa 488) diluted in PBS and incubated for 1 h at room temperature with three intermediary washes. Cells were permeabilized with 0. 1% saponin in PBS and the cytoskeleton was stained using phalloidin Alexa 647 at room temperature. Cell nuclei were stained with Dapi and coverslips were mounted on glass slides in Fluormount mounting medium. Cells were observed under a SP8 confocal laser-scanning microscope equipped with a 100× oil immersion objective (Leica). Images of 1,024 × 1,024 pixels were acquired using LaserX software (Leica). Sections 0. 28 µm thick (38 per image) were assembled into Z stacks using Las AF lite 2. 6. 3 build 8173 software (Leica). Figure  2  shows one section representative of the Z stacks.
Statistical Analyses Asymptotic two-sample Fisher-Pitman permutation tests (One-Way-Test) were performed with the software R, package Rcmdr version 2. 3. 1 (2016-10-25). Significance was * * * * at p < 0. 0001, * * * p < 0. 001, * * p < 0. 01, * p < 0. 05 (http://www. r-project. org, http://socserv. socsci. mcmaster. ca/jfox/Misc/Rcmdr/).
RESULTS The Salmonella Typhimurium 14028 Strain Deleted for the Three Known Invasion Factors Remains Invasive in Several Eukaryotic Cell Lines Both S. Typhimurium 14028 and STM-3 strains were investigated with gentamicin protection assays conducted on different cell lines. Fibroblasts, epithelial and endothelial cells from human, chicken, mouse, pig and monkey origins were tested. Primary fibroblasts (mouse primary kidney cloned, termed finite cell line RS-F1, minipig male ileum finite cell line I31) were also added. The numbers of total cell-associated (TCA) wild-type bacteria and its mutant, which correspond to surface adherent and intracellular bacteria, were not significantly different (data not shown). In the gentamicin protection assay, the cell lines were classified according to their dependence or not of T3SS-1 for invasion (Figure  1 ).
Gentamicin protection assays were performed on HeLa and AML-12 cells. Bacteria (MOI = 10) were deposited on cells for 15 min followed by the addition of gentamicin (100 µg/mL) for 1. 5 h. Experiments were performed three times in duplicate and expressed as a percentage of relative infection. Data were analyzed using asymptotic two-sample Fisher-Pitman permutation tests. Significance was **P < 0. 01. Statistical analyses also showed no significantly differences between the invasion rates of the STM-3 and the STM-invA mutants (p = 0. 1969 in the HeLa cells vs. p = 0. 3514 in the AML-12 cells). performed a gentamicin protection assay on HeLa (a T3SS-1 dependent cell line) and AML-12 cells (a T3SS-1 independent cell line) with an infection time of 15 min instead of exposure for 1. 5 h in 2. 1.
Cells were pre-treated with amiloride (1 mM) (black bars) or the culture medium (gray bars) for 30 min. Bacteria (MOI = 10) were deposited on cells for 1. 5 h followed by the addition of gentamicin (100 µg/mL) for 1. 5 h. The numbers of internalized bacteria were determined. Data are the mean ± SEM in duplicate and repeated at least twice for each strain. Statistical analyses, using asymptomatic two-sample Fisher-Pitman permutation tests, were performed on intracellular bacterial counts between the infection in presence of culture medium (gray) or amiloride (black). Significance was *P < 0. 05. T3SS-1-independent invasion of Salmonella into AML-12 cells occurs even after a short contact time, known to be sufficient for T3SS-1 invasion  (Steele-Mortimer et al. , 2002). Additionally, these data demonstrated that the entry of Salmonella into AML-12 cells was specific, since an E.
Bacteria (MOI = 10) were deposited on cells for 1. 5 h followed by the addition of gentamicin (100 µg/mL) for 1. 5 h. The dilution-effect of each drug was performed and viability of the bacteria was checked in the presence of all dilutions of the drugs used. The numbers of internalized bacteria were determined and expressed relative to values obtained for cells treated with control-diluted reagent arbitrarily set at 100%. Data are the mean ± SEM in duplicate and repeated at least twice for each strain. Data were analyzed using asymptotic two-sample Fisher-Pitman permutation tests. Significance was ***p < 0. 001, **p < 0. 01, *p < 0. 05. (A). represents the results obtained for the wild-type bacteria. (B). represents the results obtained for the STM-3. rearrangement. As extensive or low cell rearrangement leads to trigger-or a zipper-like entry mechanism, respectively and as confocal imaging did not enable identification of the process involved, we next examined the morphological alterations occurring in AML-12 cells during bacterial uptake under a scanning-electron microscope (Figure  4 ).
This translocation was T3SS-1dependent since the invA mutant, which is unable to assemble a functional T3SS-1 and thus to inject T3SS-1 effectors, did not induce blue cells even when carrying the pCX340-sopD plasmid. These data demonstrate that S. Typhimurium 14028 was able to translocate at least the SopD T3SS-1 effector into the AML-12 cytosol indicating that the T3SS-1 was functional during the interaction of S. Typhimurium 14028 with AML-12 cells. Both Clathrin-and Non-clathrin-Dependent Pathways Are Used by a S. Typhimurium Mutant Inactivated for the Three Known Invasion Factors Previous results showed that S. Typhimurium 14028 had a functional T3SS-1. However, no trigger invasion images were observed using scanning-electron microscopy. To determine the pathways involved in the entry of the S.
Finding multi-step attacks in datasets of events The basis of OMMA is the consideration of attacks as an ensemble of events, a strategy composed of different steps. A system would not be able to deduce the objective and nature of the attack considering only one of these individual events. These attacks are denoted as multi-stage  [22] , multistage  [23] , or multi-step  [2]  attacks. Some papers about the topic also refer to them as attack strategies  [24] , attack plans,  [25]  or attack scenarios  [26]. Lastly, many authors use the name of Advanced Persistent Threats (APT)  [13] , a vague denomination which is much used in the industry. The first public mention of attack strategy as an important point for attack detection is probably in the work by Huang et al. [24]. Taking inspiration from battleground management, they propose a communication protocol between IDS (Intrusion Detection System) agents distributed through the network, with a master controlling their behavior.
DARPA 2000 Intrusion Detection Scenario-Specific datasets are broadly used as the main data source for evaluating the performance of these systems, sometimes combined with tests over private network data. OMMA has been conceived to work with heterogeneous events, not only with IDS alerts, which are a type of security event. That is why we focus only in the literature about multi-step attack detection from general events. For this purpose, some specific languages have been developed to allow a security expert to code the behavior of multi-step attacks as a set of events. Two examples are STATL (State Transition Analysis Technique Language), developed by Eckmann et al. [28] , and EDL (Event Description Language). EDL was first proposed by Meier  [29]  and later improved by Jaeger et al. [2].
Another example of a distributed system applying multi-step attack signatures, expressed in a language different from EDL, is Quicksand  [32]. Signatures in Quicksand are expressed as graphs, whose nodes are used by each of the agents for local detection. Hidden Markov Models (HMMs) have also been used for multi-step attack detection. The HMMs represent sequences of normal events and are derived from a clean dataset of events. If a sequence does not correspond to any of the models, it is considered as anomalous and identified as a potential multi-step attack. This approach is used by Anming et al. [33] , who propose the use of the segmental K-means algorithm to create the HMMs. They just work with OS audit data, thus coming from only one source, Fig. 2  Example of an EDL signature called UserLogin for a simple login onto a Windows system, by Jaeger et al.
The bibliography concerning ant algorithms includes plenty of papers trying to give answers to the problem of network attack detection. However, the only ant-based work we know addressing the problem of multi-step attack is Janus, a model developed by Zhang et al. [49]. It is based on Partially Observable Markov Decision Process and it is two-sided, as it has a double point of view, that of the attacker and that of the defender. Based on this representation, the objective is to search for attack paths with minimum action cost in the case of the attackers and to highlight the most critical points for a successful attack in the case of the defenders. Several ACO-based papers related to security directly translate the ant metaphor to software agents moving through the network  [50, 51]. Nonetheless, if we consider the analysis of events in a central location, as we do in this paper, the most widespread technique is anomaly detection through the clustering of network data  [52] [53] [54] , in some cases combined with supervised methods such as SVM  [55].
Once Morwilog is built, we need a dataset accomplishing a certain number of characteristics for testing the system: • Firstly, the feedback provided by the operator has to be simulated during the experiments. We assume the human expert is infallible so no mistakes are introduced in the system. For simulating this, we need the data to be labelled 4 , so the evaluating system can know if an event is part of a multi-step attack or not. • Another requisite of the dataset is that events should be sent by a broad range of devices. This is the case in real networks, where SIEM devices are able to deal with any kind of events at the same time. Moreover, most of the attacks we want to detect have to be detected through their traces in different locations of the network. • The final requisite is that attacks injected in the dataset have to represent a multi-step strategy.
Single-step attacks can be detected by specific security systems without the need for processing events from different sources at the same time. We then have to find a labelled dataset of heterogeneous logs where attacks are represented as being composed of several events. During our research, we have not been able to find an up-to-date dataset meeting all these requirements. Because of that, we use an artificial dataset for this initial implementation of the system. The details of this artificial dataset are explained in Section 8.
Event operations OMMA uses the events generated by the devices in the network as data source. An event e is the record of an action happening in a given device. It can be considered as an entity in the set of all possible events E. We can view the event as a finite set of identifier/value pairs containing information about certain aspects of the event. The event is then expressed as a tuple of components id n /v n , so e = {id b /v b , id c /v c ,. , id a /v a ,. }. This abstract construction is represented as a log in the context of a network. The identifier id n defines a unique meaning for each value e (id n ) = v n in the event e. Thanks to the identifiers, it is easy to find a correspondence with other events. They determine the meaning of each of the fields we can find in a log. They should be universal and easy to understand, so any kind of log can be merged with others generated by different devices after proper mapping.
• A set F n of N f children κ m ∈ δ j. • A pointer to its ancestor κ (α) n ∈ δ j. This is not strictly necessary for the definition of the tree, but it is very useful if we work on the tree with a system that needs to propagate information from the deepest node of the tree to κ 0 , as with ant pheromones in Morwilog. On the element e * n , we can use the operator previously defined. With this operator, a system could look for events matching e * n in a dataset. For easing the representation, we extend the operator for working between nodes and events, so e κ n = e e * n ∀ κ n ∈ K, e ∈ E.
Log collection is made by several daemons running in parallel, called captors. There are two general kinds of captors, one push-based and another pull-based. The first one is for log sources able to send the logs to the Collector by themselves, using syslog  [62]  or a similar mechanism. On the other side, we need pull-based daemons for devices which need to be periodically requested to send the logs (e. g. , Microsoft Windows operating system). The Collector could also apply basic filtering, discarding events we do not want to include in the analysis and merging duplicate ones. The output of this module is a set of normalized events, which come out from it in real time.
Visualizer (VZ) The Visualizer is the module that represents the information of alerts and logs to the human user. This module controls the presentation of the data generated by the other modules but also transmits the input from the user into the feedback loop, so pheromones in Morwilog can be modified and the algorithms in the Classifier can identify false and true positives and learn and modify the classification methods for improving the detection rate. New alerts are generated and represented in a console so that the human expert can progressively evaluate and mark them as attacks or not. The Visualizer is also provided with tools for doing forensic investigation on events and alerts stored in the Logger.
Morwilog The most important element in our engineering system is the feedback loop, as it is responsible for the learning process. Thanks to the feedback from the human experts, the system improves its multi-step attack detection rate. Here we present the Morwilog system, which directly uses the feedback data retrieved from the expert  [9]. Once suspicious events have been selected, Morwilog tries to find the links between the events belonging to the same multistep attack using the historical data of event trees. The database of event trees can be built through training data or manually from experience. A mechanism of random generation of trees, which is used in the experiments presented in this paper, is also integrated in Morwilog, so if there is no tree corresponding to an input sequence of logs, a new event tree is built.
With a higher T max , the system is able to detect multi-step attacks lasting longer. It is important to note that we use a slightly different notation in OMMA than the one presented in the original Morwilog paper  [9]. The sequence of actions performed by each morwi is summarized in pseudocode form in Algorithm 1. We now explain the whole process carried out by the morwi, which begins when a suspicious event e i is chosen by the Orchestrator and arrives at Morwilog.
Results Among all the elements composing OMMA, we have already developed Morwilog as a submodule of the Orchestrator, which is explained in Section 7. Even if we have not yet completed the development of the Collector, we have also implemented some of its functionalities for being able to collect the events to be used by Morwilog. The system has been developed in C++. We have performed a set of experiments for evaluating the performance of Morwilog. As we do not have the previous phases in OMMA yet, we can make Morwilog work with events already processed, as if they had been classified. The goal of these experiments is to show the positive effect of pheromone evolution. As we do not have a set of pre-built event trees yet, trees are built during the execution of the algorithm, using the mechanism used when no matching tree is found.
As we explained in Section 4, we have worked with an artificial dataset. We have created it using Splunk Event Generator 9. It is composed of a total of 1038 different types of events, represented in the shape of logs. They are stored following the standard audit trail format developed by Bishop  [65]. Event types are randomly taken by the generator program in different proportions according to the event type, and a random IP source is included. We have defined several devices and the probabilities of occurrence of an event from this device. Inside each one of them, we have also defined a probability of appearance of each    2  possible type of event. Timestamps are generated by a counter, with intervals of increment that are also random. Once the events have been generated, we inject sequences of events with the IP source as the overarching element.
These sequences represent 40% of events in the dataset. Half of them are labelled as attacks, while the others are innocuous. In each dataset, there are at least 10 types of attacks and 40 types of innocuous sequences. The types have been randomly defined. The exact composition of an attack during the test does not matter, the important thing is that we are able to identify it as a different sequence. The length of the sequences is a parameter of each simulation. Simulations have been carried out on an Intel Core i5 machine running at 1. 4 GHz with 8 GB RAM. Each simulation has been repeated 50 times and then taken the Fig. 10  Diagram of the structure inside the Orchestrator. Input is coming from the Matcher. Output goes towards the Logger, the Visualizer, and the Actuator average of the results.
Morwilog needs nine parameters to be determined before execution. The values chosen for these simulations are shown in Table  2. The execution time grows linearly with the number of events to analyze, as we can see in Fig. 11. The metrics for evaluating Morwilog have been taken from the classical definitions collected by Marchette  [66]. The probability of detection (PD) and the probability of false alarm (PFA) are used. PD is the number of detections made correctly over the total number of attacks presented in the dataset. PFA is the quantity of false alerts over the total number of alerts generated by Morwilog. Simulation results are shown in Fig. 12  as a ROC curve. Horizontal axis represents the PFA and vertical one, PD. Each point in the graph corresponds to the average result from 50 simulations over datasets with different sizes.
Each one is tagged with the number of events used, from 100 to 40,000 (40k). We have represented the results for different lengths (L) of sequences. On the other hand, we have also represented a ROC curve for simulations with the same parameters but removed the pheromone update. The results are shown in Fig. 13. We have clearly better results when there are more events contained in the dataset. We see that the curves in Fig. 12  move closer to the upper left corner of the plot when the number of events analyzed are higher. There is also a variation in results with the length of the sequences. If they are higher, results are worse, because the sequences are more intercalated with normal traffic. Moreover, if we compare with the ROC curve in Fig. 13 , we can verify the positive effect of pheromone update, since the results    2.
Discussion Nowadays, organizations seem more protected than ever against cyberattacks. Systems, software, and protocols are created with security in mind from the first stages, and security devices contain more detection tools and mechanisms than ever. Infecting an organization seems harder than in the era when simple malware campaigns were successful. Consequently, attacks are ever more sophisticated. As they require a lot of resources, attackers give priority to large victims. The actions of the attackers are hidden Fig. 13  ROC curve (PFA vs. PD) representing the results from simulations of Morwilog without doing the pheromone update. Conditions are the same as in Fig. 12  into the vast volume of events generated daily within the organization. OMMA, the Engineering System we have presented in this paper, provides security researchers with a framework to develop effective algorithms for multi-step attack detection.
This brings us to the problem of finding proper open datasets in security research. OMMA is conceived under the perspective of open collaboration. Our aim is to establish the foundations of a platform for multi-step attack detection through event analysis where methods with different origins can work together. This framework already considers the inclusion of the human expert in the detection process as a key piece whose creative thinking we find indispensable for threat verification. Our objective is to continue the development both of OMMA general framework and the instantiation of their elements. The next step is to formally define the interfaces of the different modules to promote standardization. The detection of traces left by multi-step attacks in log files and the generation of event trees are key challenges for enabling production-level deployment of the OMMA framework.
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