Lotfi ben Othmane

Nicholas Pecka, Lotfi Ben Othmane, Bharat Bhargava et al.

Traditional threat modeling occurs during design, but cloud deployments introduce unanticipated threats, especially multi-stage attacks chaining vulnerabilities across trust boundaries. Existing security tools analyze components in isolation, cannot detect architectural threats from system composition, and cannot validate runtime behavior against configured policies. This gap leaves organizations vulnerable to attacks exploiting architectural weaknesses. This paper addresses this gap through a key innovation: automatically inferring system architecture from runtime observations to enable continuous threat modeling. Our methodology combines static configuration analysis with observed network flows to construct architecture graphs reflecting actual operational behavior, then applies systematic threat detection using platform-agnostic abstractions (components, domains, interfaces, access policies, flows). This enables consistent threat identification across bare metal, Kubernetes, and cloud infrastructure without manual diagram maintenance. We validate the methodology using a supply-chain system with ML components deployed on all three platforms, injecting 17 infrastructure and ML threats. Results show detection of all 17 threat types across all platforms, while existing security tools detected only 6-47% with zero ML threat coverage, confirming the necessity of runtime aware, architecture-level threat analysis.

Mubark B Jedh, Jian Kai Lee, Lotfi ben Othmane

Attackers demonstrated the use of remote access to the in-vehicle network of connected vehicles to launch cyber-attacks and remotely take control of these vehicles. Machine-learning-based Intrusion Detection Systems (IDSs) techniques have been proposed for the detection of such attacks. The evaluation of some of these IDS demonstrated their efficacy in terms of accuracy in detecting message injections but was performed offline, which limits the confidence in their use for real-time protection scenarios. This paper evaluates four architecture designs for real-time IDS for connected vehicles using Controller Area Network (CAN) datasets collected from a moving vehicle under malicious speed reading message injections. The evaluation shows that a real-time IDS for a connected vehicle designed as two processes, a process for CAN Bus monitoring and another one for anomaly detection engine is reliable (no loss of messages) and could be used for real-time resilience mechanisms as a response to cyber-attacks.

Mubark Jedh, Lotfi ben Othmane, Noor Ahmed et al.

The smart features of modern cars are enabled by a number of Electronic Control Units (ECUs) components that communicate through an in-vehicle network, known as Controller Area Network (CAN) bus. The fundamental challenge is the security of the communication link where an attacker can inject messages (e.g., increase the speed) that may impact the safety of the driver. Developing an effective defensive security solution depends on the knowledge of the identity of the ECUs, which is proprietary information. This paper proposes a message injection attack detection mechanism that is independent of the IDs of the ECUs, which is achieved by capturing the patterns in the message sequences. First, we represent the sequencing ofther messages in a given time-interval as a direct graph and compute the similarities of the successive graphs using the cosine similarity and Pearson correlation. Then, we apply threshold, change point detection, and Long Short-Term Memory (LSTM)-Recurrent NeuralNetwork (RNN) to detect and predict malicious message injections into the CAN bus. The evaluation of the methods using a dataset collected from a moving vehicle under malicious RPM and speed reading message injections show a detection accuracy of 98.45% when using LSTM-RNN and 97.32% when using a threshold method. Further, the pace of detecting the change isfast for the case of injection of RPM reading messagesbut slow for the case of injection of speed readingsmessages.