AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities

With the expansion of the digital world, the number of Internet of things (IoT) devices is evolving dramatically. IoT devices have limited computational power and a small memory. Consequently, existing and complex security methods are not suitable to detect unknown malware attacks in IoT networks. This has become a major concern in the advent of increasingly unpredictable and innovative cyberattacks. In this context, artificial immune systems (AISs) have emerged as an effective malware detection mechanism with low requirements for computation and memory. In this research, we first validate the malware detection results of a recent AIS solution using multiple datasets with different types of malware attacks. Next, we examine the potential gains and limitations of promising AIS solutions under realistic implementation scenarios. We design a realistic IoT framework mimicking real-life IoT system architectures. The objective is to evaluate the AIS solutions’ performance with regard to the system constraints. We demonstrate that AIS solutions succeed in detecting unknown malware in the most challenging conditions. Furthermore, the systemic results with different system architectures reveal the AIS solutions’ ability to transfer learning between IoT devices. Transfer learning is a pivotal feature in the presence of highly constrained devices in the network. More importantly, this work highlights that previously published AIS performance results, which were obtained in a simulation environment, cannot be taken at face value. In reality, AIS’s malware detection accuracy for IoT systems is 91% in the most restricted designed system compared to the 99% accuracy rate reported in the simulation experiment

Trace semantics for polymorphic references

Research supported by the Engineering and Physical Sciences Research Council (EP/L022478/1) and the Royal Academy of Engineering

Trace Properties from Separation Logic Specifications

We propose a formal approach for relating abstract separation logic library specifications with the trace properties they enforce on interactions between a client and a library. Separation logic with abstract predicates enforces a resource discipline that constrains when and how calls may be made between a client and a library. Intuitively, this can enforce a protocol on the interaction trace. This intuition is broadly used in the separation logic community but has not previously been formalised. We provide just such a formalisation. Our approach is based on using wrappers which instrument library code to induce execution traces for the properties under examination. By considering a separation logic extended with trace resources, we prove that when a library satisfies its separation logic specification then its wrapped version satisfies the same specification and, moreover, maintains the trace properties as an invariant. Consequently, any client and library implementation that are correct with respect to the separation logic specification will satisfy the trace properties

Robustness of Several Estimators of the ACF of AR(1) Process With Non-Gaussian Errors

The autocorrelation function (ACF) plays an important role in the context of ARMA modeling, especially for their identification and estimation. This study considers the robust estimation of the ACF of the AR(1) model if the white noise (WN) process is non- Gaussian. Three estimators including the ordinary moment estimator and two other (robust) estimators are considered. The impacts of the deviation from normality of the WN process on those estimators in terms of bias, MSE and distribution via Monte-Carlo simulation are examined. The empirical distribution of those estimators when the errors are normal, t, Cauchy and exponential are studied. Results show that the moment estimator is more affected by the change of the white noise distribution than other considered estimators

Relativistic Aharonov-Casher Phase in Spin One

The Aharonov-Casher (AC) phase is calculated in relativistic wave equations of spin one. The AC phase has previously been calculated from the Dirac-Pauli equation using a gauge-like technique \cite{MK1,MK2}. In the spin-one case, we use Kemmer theory (a Dirac-like particle theory) to calculate the phase in a similar manner. However the vector formalism, the Proca theory, is more widely known and used. In the presence of an electromagnetic field, the two theories are `equivalent' and may be transformed into one another. We adapt these transformations to show that the Kemmer theory results apply to the Proca theory. Then we calculate the Aharonov-Casher phase for spin-one particles directly in the Proca formalism.Comment: 12 page