Ensemble Learning for Low-Level Hardware-Supported Malware Detection

Abstract. Recent work demonstrated hardware-based online malware detection using only low-level features. This detector is envisioned as a first line of defense that prioritizes the application of more expensive and more accurate software detectors. Critical to such a framework is the detection performance of the hardware detector. In this paper, we explore the use of both specialized detectors and ensemble learning tech-niques to improve performance of the hardware detector. The proposed detectors reduce the false positive rate by more than half compared to a single detector, while increasing the detection rate. We also contribute approximate metrics to quantify the detection overhead, and show that the proposed detectors achieve more than 11x reduction in overhead compared to a software only detector (1.87x compared to prior work), while improving detection time. Finally, we characterize the hardware complexity by extending an open core and synthesizing it on an FPGA platform, showing that the overhead is minimal.

Processes Fmea On Screwing Of Terminals

This paper provides the use of Failure Mode and Effects Analysis (FMEA) for improving the reliability of sub systems in order to improve the productivity which in turn improves the bottom line of a manufacturing industry. Thus the various possible causes of failure and their effects with the prevention are discussed in this work. Severity values, Occurrence number, Detection and Risk Priority Number (RPN) are some parameters, which need to be determined. These are the steps taken during the design phase of the equipment life cycle to ensure that reliability requirements have been properly allocated and that a process for continuous improvement exists. The FMEA technique is applied a testing bench for the controllers/ contactors to avoid the failures. The prevention suggested in this paper can considerably decrease the time for understanding, operation & failures

Function Call Graphs Versus Machine Learning for Malware Detection

Recent work has shown that a function call graph technique can perform well on some challenging malware detection problems. In this chapter, we compare this function call graph approach to elementary machine learning techniques that are trained on simpler features. We find that the machine learning techniques are generally more robust than the function call graphs, in the sense that the malware must be modified to a far greater extent before the machine learning techniques are significantly degraded. This work provides evidence that machine learning is likely to perform better than ad hoc approaches, particularly when faced with intelligent attackers who can attempt to exploit the inherent weaknesses in a given detection strategy