Biblio

Over the last few years, there has been an increasing number of studies about facial emotion recognition because of the importance and the impact that it has in the interaction of humans with computers. With the growing number of challenging datasets, the application of deep learning techniques have all become necessary. In this paper, we study the challenges of Emotion Recognition Datasets and we also try different parameters and architectures of the Conventional Neural Networks (CNNs) in order to detect the seven emotions in human faces, such as: anger, fear, disgust, contempt, happiness, sadness and surprise. We have chosen iCV MEFED (Multi-Emotion Facial Expression Dataset) as the main dataset for our study, which is relatively new, interesting and very challenging.

Similarity digests have gained popularity for many security applications like blacklisting/whitelisting, and finding similar variants of malware. TLSH has been shown to be particularly good at hunting similar malware, and is resistant to evasion as compared to other similarity digests like ssdeep and sdhash. Searching and clustering are fundamental tools which help the security analysts and security operations center (SOC) operators in hunting and analyzing malware. Current approaches which aim to cluster malware are not scalable enough to keep up with the vast amount of malware and goodware available in the wild. In this paper, we present techniques which allow for fast search and clustering of TLSH hash digests which can aid analysts to inspect large amounts of malware/goodware. Our approach builds on fast nearest neighbor search techniques to build a tree-based index which performs fast search based on TLSH hash digests. The tree-based index is used in our threshold based Hierarchical Agglomerative Clustering (HAC-T) algorithm which is able to cluster digests in a scalable manner. Our clustering technique can cluster digests in O (n logn) time on average. We performed an empirical evaluation by comparing our approach with many standard and recent clustering techniques. We demonstrate that our approach is much more scalable and still is able to produce good cluster quality. We measured cluster quality using purity on 10 million samples obtained from VirusTotal. We obtained a high purity score in the range from 0.97 to 0.98 using labels from five major anti-virus vendors (Kaspersky, Microsoft, Symantec, Sophos, and McAfee) which demonstrates the effectiveness of the proposed method.

With the developing understanding of Information Security and digital assets, IT technology has put on tremendous importance of network admission control (NAC). In NAC architecture, admission decisions and resource reservations are taken at edge devices, rather than resources or individual routers within the network. The NAC architecture enables resilient resource reservation, maintaining reservations even after failures and intra-domain rerouting. Admission Control Networks destiny is based on IP networks through its Security and Quality of Service (QoS) demands for real time multimedia application via advance resource reservation techniques. To achieve Security & QoS demands, in real time performance networks, admission control algorithm decides whether the new traffic flow can be admitted to the network or not. Secure allocation of Peer for multimedia traffic flows with required performance is a great challenge in resource reservation schemes. In this paper, we have proposed our model for VoIP networks in order to achieve security services along with QoS, where admission control decisions are taken place at edge routers. We have analyzed and argued that the measurement based admission control should be done at edge routers which employs on-demand probing parallel from both edge routers to secure the source and destination nodes respectively. In order to achieve Security and QoS for a new call, we choose various probe packet sizes for voice and video calls respectively. Similarly a technique is adopted to attain a security allocation approach for selecting an admission control threshold by proposing our admission control algorithm. All results are tested on NS2 based simulation to evalualate the network performance of edge router based upon network admission control in VoIP traffic.