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Cyber-Physical Production Systems (CPPS) are the key enabling for industrial businesses and economic growth. The introduction of the Internet of Things (IoT) in industrial processes represents a new Internet revolution, mostly known as 4th Industrial Revolution, towards the Smart Manufacturing concept. Despite the huge interest from the industry side to innovate their production systems, in order to increase revenues at lower costs, the IoT concept is still immature and fuzzy, which increases security related risks in industrial systems.

Instructions: 

The generation of the dataset containing OPC UA traffic was possible due to the setup and execution of a laboratory CPPS testbed. This CPPS uses OPC UA standard for horizontal and vertical communications.Regarding the CPPS testbed setup, it consists on seven nodes in the network.Each network node consist on a Raspberry Pi device, running the Python FreeOpcUa implementation. In this configuration, there are two production units, each one containing three devices, and one node representing a Manufacturing Execution System (MES). Each device implements both OPC UA server and client, where the server publish to a OPC UA variable updates regarding sensor readings and the client subscribes all OPC UA variables from all other devices in the same production unit. On the other side, the MES only implements the OPC UA client, which subscribes all OPC UA variables from all devices in both production units. Also, connected to this network, is an attack node as it is assumed that the attacker already gained access to the CPPS network.After setting up the CPPS testbed, a python implementation that implements Tshark was used to capture OPC UA packets and export this traffic to a csv file format dataset. This traffic includes both normal and anomalous behaviour. Anomalous behaviour is achieved with the malicious node, which injects attacks into the CPPS network, targeting one or more device nodes and the MES. The attacks selected for the malicious activities are:

    • Denial of Service(DoS);
    • Eavesdropping or Man-in-the-middle (MITM) attacks;
    • Impersonation or Spoofing attacks.

 

To perform the attacks mentioned, a python script is used, which implements the Scapy module for packet sniffing, injection and modification. Regarding the dataset generation, another python script, that implements Tshark (in this case Pyshark) was used to capture only OPC UA packets and export this traffic to a csv file format dataset. Actually, the OPC UA packets are converted to bidirectional communication flows, which are characterized by the following 32 features:

    • src_ip: Source IP address;
    • src_port: Source port;
    • dst_ip: Destination IP address;
    • dst_port: Destination port;
    • flags: TCP flag status;
    • pktTotalCount: Total packet count;
    • octetTotalCount: Total packet size;
    • avg_ps: Average packet size;
    • proto: Protocol;
    • service: OPC UA service call type;
    • service_errors: Number of service errors in OPC UA request responses;
    • status_errors: Number of status errors in OPC UA request responses;
    • msg_size: OPC UA message transport size;
    • min_msg_size: minimum OPC UA message size;
    • flowStart: Timestamp of flow start;
    • flowEnd: Timestamp of flow end;
    • flowDuration: Flow duration in seconds;
    • avg_flowDuration: Average flow duration in seconds;
    • flowInterval: Time interval between flows in seconds;
    • count: Number of connections to the same destination host as the current connection in the past two seconds;
    • srv_count: Number of connections to the same port number as the current connection in the past two seconds;
    • same_srv_rate: The percentage of connections that were to the same port number, among the connections aggregated in Count;
    • dst_host_same_src_port_rate: The percentage of connections that were to the same source port, among the connections having the same port number;
    • f_pktTotalCount: Total forward packets count;
    • f_octetTotalCount: Total forward packets size;
    • f_flowStart: Timestamp of first forward packet start;
    • f_rate: Rate at which forward packets are transmitted;
    • b_pktTotalCount: Total backwards packets count;
    • b_octetTotalCount: Total backwards packets size;
    • b_flowStart: Timestamp of first backwards packet start;
    • label: Binary label classification;
    • multi_label: Multi classification labeling.

 

The generated dataset has 33.567 normal instances, 74.013 DoS attack instances, 50 impersonation attack instances, and 7 MITM attack instances. This gives a total of 107.634 instances. Also, all attacks were grouped into one class (anomaly - 1) and the rest of the instances belong to the normal class (0).

For more information, please contact the author: Rui Pinto (rpinto@fe.up.pt).

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Data

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50 Views

ABSTRACT

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A simple dataset that gives the processing cost (in cycles) for verifying multiple messages signed with ECDSA and implicitly certified public keys. It considers two implicit certification models: ECQV and SIMPL. 

 

This dataset is used in article "Schnorr-based implicit certification: improving the security and efficiency of vehicular communications", submitted to IEEE Transactions on Computers. Namely, it is used as basis for building that article's Figure 2.

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Master data has played a significant role in improving operational efficiencies and has attracted the attention of many large businesses over the decade. Recent professional searches have also proved a significant growth in the practice and research of managing these master data assets.

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Development of Industrial IoT System for Anomaly Detection in Smart Factory

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905 Views

This data set is the capture of the Radio Frequency emissions from 9 IoT devices using an USRP Software Defined Radio. The data set is in MATLAB format and it stores the IQ samples of the signals in space. The data set can be used for experimental and analysis on Radio Frequency identification and authentication.

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1149 Views

A Traffic Light Controller PETRI_NET (Finite State Machine) Implementation.

 

An implementation of FSM approach can be followed in systems whose tasks constitute a well-structured list so all states can be easily enumerated. A Traffic light controller represents a relatively complex control function

Instructions: 

This file would need to be unzipped for access

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