Analyzing CNN Based Behavioural Malware Detection Techniques on Cloud IaaS

Cloud Infrastructure as a Service (IaaS) is vulnerable to malware due to its exposure to external adversaries, making it a lucrative attack vector for malicious actors. A datacenter infected with malware can cause data loss and/or major disruptions to service for its users. This paper analyzes and compares various Convolutional Neural Networks (CNNs) for online detection of malware in cloud IaaS. The detection is performed based on behavioural data using process level performance metrics including cpu usage, memory usage, disk usage etc. We have used the state of the art DenseNets and ResNets in effectively detecting malware in online cloud system. CNN are designed to extract features from data gathered from a live malware running on a real cloud environment. Experiments are performed on OpenStack (a cloud IaaS software) testbed designed to replicate a typical 3-tier web architecture. Comparative analysis is performed for different metrics for different CNN models used in this research

'Then look!' Unborn attachments and the half-moving image

This article explores the emotional impact on the viewer of disturbing and disorienting images of infant-caregiver relationality in four “melo-horror” films: Imitation of Life (Stahl, 1934), Imitation of Life (Sirk, 1959), The Brood (Cronenberg, 1979), and Beloved (Demme, 1998). Comparing some of these filmic images with the infant performances of “disorganized” attachment styles captured on videotape by attachment researchers such as Mary Main, the author argues that the filmed audiovisual enactment of relational trauma, whether in the context of scientific research or cinematic art, offers the spectator an opportunity to work consciously and unconsciously with representations of unbearable psychic and psychosocial experience—both her own and that of others—that may hitherto have been thought unrepresentable or simply not thought at all