Cyber Attacks

Advanced Metering Infrastructure is prone to cyber threats and Distributed Denial of Service is renowned attack that threatens critical infrastructure. Intrusion detection schemes proposed in earlier works consider the network specific parameters for intrusion detection rather than physical layer parameters. Hence, the proposal considers the physical layer parameters in detecting distributed denial of service attack through abnormal energy consumption pattern of the Data concentrator in the advanced metering infrastructure.

This dataset comprises qualitative and quantitative data collected from a comprehensive study evaluating the prevalence and types of social engineering vulnerabilities within Tanzanian higher learning institutions. The data was gathered through surveys and structured interviews with 395 participants, including students, academic staff, and administrative staff.

In deep learning, images are utilized due to their rich information content, spatial hierarchies, and translation invariance, rendering them ideal for tasks such as object recognition and classification. The classification of malware using images is an important field for deep learning, especially in cybersecurity. Within this context, the Classified Advanced Persistent Threat Dataset is a thorough collection that has been carefully selected to further this field's study and innovation.

The efficient operation of interconnected power systems relies on Load Frequency Control (LFC) to maintain stable frequency and regulate tie-line power flow between distinct areas. In modern setups, LFC parameters are monitored and processed in real-time through cloud-based platforms, enabling centralized control and optimization. However, this introduces vulnerabilities, as demonstrated by the susceptibility to attacks such as scaling, ramp, and random manipulations of data transmitted between areas and the cloud server.

The real system in our experiment comprises four production stations: Pick and place, assembly, muscle compressing and sorting. These modular stations are controlled by Siemens PLC. This is the data gathered from a real manufacturing system and its Digital Twin data when under the denial of service attacks.

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).

66% of Prestashop websites are at high risk from cyber criminals.

Common Hacks in Prestashop