Genetic heterogeneity analysis using genetic algorithm and network science

Through genome-wide association studies (GWAS), disease susceptible genetic variables can be identified by comparing the genetic data of individuals with and without a specific disease. However, the discovery of these associations poses a significant challenge due to genetic heterogeneity and feature interactions. Genetic variables intertwined with these effects often exhibit lower effect-size, and thus can be difficult to be detected using machine learning feature selection methods. To address these challenges, this paper introduces a novel feature selection mechanism for GWAS, named Feature Co-selection Network (FCSNet). FCS-Net is designed to extract heterogeneous subsets of genetic variables from a network constructed from multiple independent feature selection runs based on a genetic algorithm (GA), an evolutionary learning algorithm. We employ a non-linear machine learning algorithm to detect feature interaction. We introduce the Community Risk Score (CRS), a synthetic feature designed to quantify the collective disease association of each variable subset. Our experiment showcases the effectiveness of the utilized GA-based feature selection method in identifying feature interactions through synthetic data analysis. Furthermore, we apply our novel approach to a case-control colorectal cancer GWAS dataset. The resulting synthetic features are then used to explain the genetic heterogeneity in an additional case-only GWAS dataset

SnapCatch: Automatic Detection of Covert Timing Channels Using Image Processing and Machine Learning

With the rapid growth of data exfiltration carried out by cyber attacks, Covert Timing Channels (CTC) have become an imminent network security risk that continues to grow in both sophistication and utilization. These types of channels utilize inter-arrival times to steal sensitive data from the targeted networks. CTC detection relies increasingly on machine learning techniques, which utilize statistical-based metrics to separate malicious (covert) traffic flows from the legitimate (overt) ones. However, given the efforts of cyber attacks to evade detection and the growing column of CTC, covert channels detection needs to improve in both performance and precision to detect and prevent CTCs and mitigate the reduction of the quality of service caused by the detection process. In this article, we present an innovative image-based solution for fully automated CTC detection and localization. Our approach is based on the observation that the covert channels generate traffic that can be converted to colored images. Leveraging this observation, our solution is designed to automatically detect and locate the malicious part (i.e., set of packets) within a traffic flow. By locating the covert parts within traffic flows, our approach reduces the drop of the quality of service caused by blocking the entire traffic flows in which covert channels are detected. We first convert traffic flows into colored images, and then we extract image-based features for detection covert traffic. We train a classifier using these features on a large data set of covert and overt traffic. This approach demonstrates a remarkable performance achieving a detection accuracy of 95.83% for cautious CTCs and a covert traffic accuracy of 97.83% for 8 bit covert messages, which is way beyond what the popular statistical-based solutions can achieve

Distributed under Creative Commons CC-BY 4.0 OPEN ACCESS TCP adaptation with network coding and opportunistic data forwarding in multi-hop wireless networks

ABSTRACT Opportunistic data forwarding significantly increases the throughput in multi-hop wireless mesh networks by utilizing the broadcast nature of wireless transmissions and the fluctuation of link qualities. Network coding strengthens the robustness of data transmissions over unreliable wireless links. However, opportunistic data forwarding and network coding are rarely incorporated with TCP because the frequent occurrences of out-of-order packets in opportunistic data forwarding and long decoding delay in network coding overthrow TCP's congestion control. In this paper, we propose a solution dubbed TCPFender, which supports opportunistic data forwarding and network coding in TCP. Our solution adds an adaptation layer to mask the packet loss caused by wireless link errors and provides early positive feedbacks to trigger a larger congestion window for TCP. This adaptation layer functions over the network layer and reduces the delay of ACKs for each coded packet. The simulation results show that TCPFender significantly outperforms TCP/IP in terms of the network throughput in different topologies of wireless networks

A framework for evaluating the performance of sustainable service supply chain management under uncertainty

Developing and accessing a measure of sustainable service supply chain management (SSSCM) performance is currently a key challenge. The main contributions of this study are two-fold. First, this paper provides valuable support for SSSCM regarding the nature of network hierarchical relations with qualitative and quantitative scales. Second, this study indicates the practical implementation and enhances management effectiveness for SSSCM. The literature on SSSCM is very limited and performance measures need to have a systematic framework. The purpose of this study is to develop and evaluate the SSSCM importance based on aspects i.e., environmentally conscious design, environmental service operations design and environmentally sustainable design. This paper developed a hierarchical network for SSSCM in a closed-loop hierarchical structure. A generalized quantitative evaluation model based on the Fuzzy Delphi Method and Analytical Network Process were then used to consider both the interdependence among measures and the fuzziness of subjective measures in SSSCM. The results indicate that the top-ranking aspect to consider is that of environmental service operation design, and the top criteria is reverse logistics integrated into service packag