ARAT is a complete vulnerabilities testing, penetration and data analytics platform full AI/ML support for anomaly and attack detection. ARAT was originally developed to meet our own requirements in vulnerabilities and penetration testing of individual (embedded) systems and complete, complex networks, and also for our work in hardware level and side channel attacks.
ARAT allows to stimulate systems in different ways. For example, ARAT controls, using rule-based or AI/ML-based orchestration, standard and self-developed exploration, vulnerabilities and penetration test penetration test tools, for network traffic and other interfaces, for example UART, CAN, I2C, field buses and so on. It collects response information, e.g. from network tapping (PCAP), interfaces and physical sensors like Digital Storage Oscilloscopes (DSO) or Software Define Radios (SDR). It especially supports an FPGA/SoC based integrated, high performance stimulation and observation device for Side Channel Analysis, which we developed inhouse at ObjectSecurity. Finally, ARAT supports result analytics-based rules, statistics, AI/ML and visual analytics.
The main advantage of ARAT is its high agility over the entire simulation, observation and analytics cycle. It fully integrates automation with interactive visual analytics.
ARAT has a wide range of features to support vulnerabilities testing, penetration and analytics:
Vulnerability testing, penetration, analytics
Standard toolkits integration, e.g. TensorFlow
Analytics can directly control simulation and acquisition
AI/ML control of tests/attacks
Full integration of stimulation, data acquisition & analytics
Side channel attacks
Scanning and attacks (OpenVAS, Metasploit, …)
Interfaces (UART, JTAG, CAN, I2C)
Network traffic (pcap)
Digital Storage Oscilloscope/timer/counter
Software Define Radio (SDR)
Here is a screenshot of the ARAT platform in action:
ARAT supports our patent-pending Differential Stimulus technology, which enables effective and efficient Artificial Intelligence and Machine Learning (AI/ML) based anomalies detection and analytics. Our technology greatly improves the probability to detect anomalies using AI/ML – even in the used training baseline data.