Cardiovascular diseases (CVDs) are the primary cause of mortality worldwide. The healthcare sector in India currently shows promise for substantial changes, specifically in the utilization and importance of the Internet of Medical Things (IoMT). Edge computing is necessary to make the IoMT more scalable, portable, reliable, and responsive. Security and privacy concerns impede the development and deployment of IoMT devices. The technology of blockchain can resolve security and privacy concerns. In this work, we implement a lightweight binary neural network (BNN) in a Cortex-M4 microcontroller (MCU) to enable the detection of four different types of heart illnesses present in a single-lead electrocardiogram (ECG) signal, in addition to proposing a blockchain-enabled HeartCare framework. The end-user can identify ailments and subsequently disseminate ECG results to medical professionals via a privacy-preserving blockchain-enabled framework. To acquire the ECG signal, a reusable fabric electrode was proposed and successfully fabricated. Finally, the BNN model is being trained utilising ECG databases of patients from the Indian continent, in addition to other state-of-the-art databases. The post-deployment validation of the proposed framework was conducted rigorously in alignment with the ACC/AHA Guidelines, resulting in an overall accuracy of 95.93% and a sensitivity of 95.90% for our BNN model.

BACKGROUND: A limitation of traditional differential expression analysis on small datasets involves the possibility of false positives and false negatives due to sample variation. Considering the recent advances in deep learning (DL) based models, we wanted to expand the state-of-the-art in disease biomarker prediction from RNA-seq data using DL. However, application of DL to RNA-seq data is challenging due to absence of appropriate labels and smaller sample size as compared to number of genes. Deep learning coupled with transfer learning can improve prediction performance on novel data by incorporating patterns learned from other related data. With the emergence of new disease datasets, biomarker prediction would be facilitated by having a generalized model that can transfer the knowledge of trained feature maps to the new dataset. To the best of our knowledge, there is no Convolutional Neural Network (CNN)-based model coupled with transfer learning to predict the significant upregulating (UR) and downregulating (DR) genes from both trained and untrained datasets. RESULTS: We implemented a CNN model, DEGnext, to predict UR and DR genes from gene expression data obtained from The Cancer Genome Atlas database. DEGnext uses biologically validated data along with logarithmic fold change values to classify differentially expressed genes (DEGs) as UR and DR genes. We applied transfer learning to our model to leverage the knowledge of trained feature maps to untrained cancer datasets. DEGnext's results were competitive (ROC scores between 88 and 99[Formula: see text]) with those of five traditional machine learning methods: Decision Tree, K-Nearest Neighbors, Random Forest, Support Vector Machine, and XGBoost. DEGnext was robust and effective in terms of transferring learned feature maps to facilitate classification of unseen datasets. Additionally, we validated that the predicted DEGs from DEGnext were mapped to significant Gene Ontology terms and pathways related to cancer. CONCLUSIONS: DEGnext can classify DEGs into UR and DR genes from RNA-seq cancer datasets with high performance. This type of analysis, using biologically relevant fine-tuning data, may aid in the exploration of potential biomarkers and can be adapted for other disease datasets.

To understand the underlying biological mechanisms of gene expression data, it is important to discover the groups of genes that have similar expression patterns under certain subsets of conditions. Biclustering algorithms have been effective in analyzing large-scale gene expression data. Recently, traditional biclustering has been improved by introducing biological knowledge along with the expression data during the biclustering process. In this paper, we propose the Pathway-based Order Preserving Biclustering (POPBic) algorithm by incorporating Kyoto Encyclopedia of Genes and Genomes (KEGG) based on the hypothesis that two genes sharing similar pathways are likely to be similar. The basic principle of the POPBic approach is to apply the concept of Longest Common Subsequence between a pair of genes which have a high number of common pathways. The algorithm identifies the expression patterns from data using two major steps: (i) selection of significant seed genes and (ii) extraction of biclusters. We performe exhaustive experimentation with the POPBic algorithm using synthetic dataset to evaluate the bicluster model, finding its robustness in the presence of noise and identifying overlapping biclusters. We demonstrate that POPBic is able to discover biologically significant biclusters for four cancer microarray gene expression datasets. POPBic has been found to perform consistently well in comparison to its closest competitors.

Analysis of RNA-sequence (RNA-seq) data is widely used in transcriptomic studies and it has many applications. We review RNA-seq data analysis from RNA-seq reads to the results of differential expression analysis. In addition, we perform a descriptive comparison of tools used in each step of RNA-seq data analysis along with a discussion of important characteristics of these tools. A taxonomy of tools is also provided. A discussion of issues in quality control and visualization of RNA-seq data is also included along with useful tools. Finally, we provide some guidelines for the RNA-seq data analyst, along with research issues and challenges which should be addressed.

Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.

This paper presents an exhaustive empirical study to identify biomarkers using two approaches: frequency-based and network-based, over seventeen different biclustering algorithms and six different cancer expression datasets. To systematically analyze the biclustering algorithms, we perform enrichment analysis, subtype identification and biomarker identification. Biclustering algorithms such as C&C, SAMBA and Plaid are useful to detect biomarkers by both approaches for all datasets except prostate cancer. We detect a total of 102 gene biomarkers using frequency-based method out of which 19 are for blood cancer, 36 for lung cancer, 25 for colon cancer, 13 for multi-tissue cancer and 9 for prostate cancer. Using the network-based approach we detect a total of 41 gene biomarkers of which 15 are from blood cancer, 12 from lung cancer, 6 from colon cancer, 7 from multi-tissue cancer and 1 from prostate cancer dataset. We further extend our network analysis over some biclusters and detect some gene biomarkers not detected earlier by both frequency-based or network-based approach. We expand our work on breast cancer miRNA expression data to evaluate the performance of the biclustering algorithms. We detect 19 breast cancer biomarkers by frequency-based method and 5 by network-based method for the miRNA dataset.

Distributed denial-of-service DDoS attacks cause havoc by exploiting threats to Internet services. In this paper, we propose E-LDAT, a lightweight extended-entropy metric-based system for both DDoS flooding attack detection and IP Internet Protocol traceback. It aims to identify DDoS attacks effectively by measuring the metric difference between legitimate traffic and attack traffic. IP traceback is performed using the metric values for an attack sample detected by the detection scheme. The method uses a generalized entropy metric with packet intensity computation on the sampled network traffic with respect to time. The E-LDAT system has been evaluated using several real-world DDoS datasets and outperforms competing methods when detecting four classes of DDoS flooding attacks, including constant rate, pulsing rate, increasing rate and subgroup attacks. The IP traceback model is also evaluated using NetFlow data in near real-time and performs well in large-scale attack networks with zombies. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Distributed denial‐of‐service attack is a serious concern in this era of voluminous Internet world. The challenge is to find difference between distributed denial‐of‐service attack traffic of all types and legitimate traffic at the earliest, as we can see high‐rate attack traffic and low rate attack traffic resembles with bursty legitimate traffic and normal legitimate traffic, respectively. Self‐similarity exists in ethernet traffic. Using Hurst parameter, we have differentiated legitimacy of any traffic irrespective of network traffic protocol. We have validated our method using both benchmark and real‐life datasets, and the results are highly satisfactory. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract A Distributed Denial of Service (DDoS) attack is a major security threat for networks and Internet services. Attackers can generate attack traffic similar to normal network traffic using sophisticated attacking tools. In such a situation, many intrusion detection systems fail to identify DDoS attack in real time. However, DDoS attack traffic behaves differently from legitimate network traffic in terms of traffic features. Statistical properties of various features can be analyzed to distinguish the attack traffic from legitimate traffic. In this paper, we introduce a statistical measure called Feature Feature score for multivariate data analysis to distinguish DDoS attack traffic from normal traffic. We extract three basic parameters of network traffic, namely, entropy of source IPs, variation of source IPs, and packet rate to analyze the behavior of network traffic for attack detection. The method is validated using CAIDA DDoS 2007 and MIT DARPA datasets. Copyright © 2016 John Wiley & Sons, Ltd.

Web-application attacks are considered to be one of the major security concerns of a large number of applications, especially those deployed in health care, banking and e-business operations. These applications must involve high security in addition to other application requirements such as friendliness, effectiveness and efficiency in service provided to the end users. In this paper, we focus on security vulnerabilities resulting from generic input validation problems that may cause cross-site scripting (XSS) attacks. We take a look at the types of XSS attacks and current practices for their detection and identify the research issues and challenges. We propose a method for the detection of XSS attacks. The detection method identifies a malicious execution sequence based on the initialized list of—legitimate execution sequences and malicious strings or malicious literals generated during a training phase. The initialized lists are stored into four different Web-Application Execution Profiles (WAEPs) corresponding to four different attack scenarios. The detection module searches the run-time sequence in the respective WAEPs. We test our method for the detection of three different categories of XSS attacks under four different attack scenarios—two in the client side and two in the web-application server. Satisfactory results have been found under all the four scenarios.

BACKGROUND: Biological networks connect genes, gene products to one another. A network of co-regulated genes may form gene clusters that can encode proteins and take part in common biological processes. A gene co-expression network describes inter-relationships among genes. Existing techniques generally depend on proximity measures based on global similarity to draw the relationship between genes. It has been observed that expression profiles are sharing local similarity rather than global similarity. We propose an expression pattern based method called GeCON to extract Gene CO-expression Network from microarray data. Pair-wise supports are computed for each pair of genes based on changing tendencies and regulation patterns of the gene expression. Gene pairs showing negative or positive co-regulation under a given number of conditions are used to construct such gene co-expression network. We construct co-expression network with signed edges to reflect up- and down-regulation between pairs of genes. Most existing techniques do not emphasize computational efficiency. We exploit a fast correlogram matrix based technique for capturing the support of each gene pair to construct the network. RESULTS: We apply GeCON to both real and synthetic gene expression data. We compare our results using the DREAM (Dialogue for Reverse Engineering Assessments and Methods) Challenge data with three well known algorithms, viz., ARACNE, CLR and MRNET. Our method outperforms other algorithms based on in silico regulatory network reconstruction. Experimental results show that GeCON can extract functionally enriched network modules from real expression data. CONCLUSIONS: In view of the results over several in-silico and real expression datasets, the proposed GeCON shows satisfactory performance in predicting co-expression network in a computationally inexpensive way. We further establish that a simple expression pattern matching is helpful in finding biologically relevant gene network. In future, we aim to introduce an enhanced GeCON to identify Protein-Protein interaction network complexes by incorporating variable density concept.

Distributed denial of service (DDoS) attack is a coordinated attack, generally performed on a massive scale on the availability of services of a target system or network resources. Owing to the continuous evolution of new attacks and ever-increasing number of vulnerable hosts on the Internet, many DDoS attack detection or prevention mechanisms have been proposed. In this paper, we present a comprehensive survey of DDoS attacks, detection methods and tools used in wired networks. The paper also highlights open issues, research challenges and possible solutions in this area.

With the growth of networked computers and associated applications, intrusion detection has become essential to keeping networks secure. A number of intrusion detection methods have been developed for protecting computers and networks using conventional statistical methods as well as data mining methods. Data mining methods for misuse and anomaly-based intrusion detection, usually encompass supervised, unsupervised and outlier methods. It is necessary that the capabilities of intrusion detection methods be updated with the creation of new attacks. This paper proposes a multi-level hybrid intrusion detection method that uses a combination of supervised, unsupervised and outlier-based methods for improving the efficiency of detection of new and old attacks. The method is evaluated with a captured real-time flow and packet dataset called the Tezpur University intrusion detection system (TUIDS) dataset, a distributed denial of service dataset, and the benchmark intrusion dataset called the knowledge discovery and data mining Cup 1999 dataset and the new version of KDD (NSL-KDD) dataset. Experimental results are compared with existing multi-level intrusion detection methods and other classifiers. The performance of our method is very good.

The existence of various types of correlations among the expressions of a group of biologically significant genes poses challenges in developing effective methods of gene expression data analysis. The initial focus of computational biologists was to work with only absolute and shifting correlations. However, researchers have found that the ability to handle shifting-and-scaling correlation enables them to extract more biologically relevant and interesting patterns from gene microarray data. In this paper, we introduce an effective shifting-and-scaling correlation measure named Shifting and Scaling Similarity (SSSim), which can detect highly correlated gene pairs in any gene expression data. We also introduce a technique named Intensive Correlation Search (ICS) biclustering algorithm, which uses SSSim to extract biologically significant biclusters from a gene expression data set. The technique performs satisfactorily with a number of benchmarked gene expression data sets when evaluated in terms of functional categories in Gene Ontology database.

This paper presents an expression pattern based biclustering technique for grouping both positively and negatively regulated genes together as co-regulated genes from microarray expression data. Most interesting variants of this problem are NP-complete requiring either large computational effort or the use of lossy heuristics to short circuit the calculation. Our approach deterministically finds all biclusters using a non-greedy approach in polynomial time. Various real datasets have been used for experiments and results are excellent.

Anomaly Based Network Intrusion Detection Systems (ANIDS) mechanisms are largely based on machine learning algorithms and have been found effective in detecting known as well as novel attacks. However, often these algorithms in isolation cannot accurately detect all kinds of attacks and generate lot of false alarms. In this paper, we intend to show that if the power of each of the algorithms are combined and harnessed using an appropriate ensemble method, a significant improvement in detection rate can be achieved. The performance of our meta ensemble classifier was evaluated over several real life intrusion datasets and the benchmark KDD'99 dataset, and the results have been found excellent in comparison to its other competing algorithms.

BACKGROUND: The development of high-throughput Microarray technologies has provided various opportunities to systematically characterize diverse types of computational biological networks. Co-expression network have become popular in the analysis of microarray data, such as for detecting functional gene modules. RESULTS: This paper presents a method to build a co-expression network (CEN) and to detect network modules from the built network. We use an effective gene expression similarity measure called NMRS (Normalized mean residue similarity) to construct the CEN. We have tested our method on five publicly available benchmark microarray datasets. The network modules extracted by our algorithm have been biologically validated in terms of Q value and p value. CONCLUSIONS: Our results show that the technique is capable of detecting biologically significant network modules from the co-expression network. Biologist can use this technique to find groups of genes with similar functionality based on their expression information.

Measurement of gene expression using DNA micro arrays have revolutionized biological and medical research. This paper presents a divisive clustering algorithm that produces a tree of genes called GERC tree along with the generated clusters. Unlike a dendrogram, a GERC tree is a general tree and it is an ample resource for biological information about the genes in a data set. The leaves of the tree represent the desired clusters. The clustering method was tested with several real-life data sets and the proposed method has been found satisfactory.

Scanning of ports on a computer occurs frequently on the Internet. An attacker performs port scans of Internet protocol addresses to find vulnerable hosts to compromise. However, it is also useful for system administrators and other network defenders to detect port scans as possible preliminaries to more serious attacks. It is a very difficult task to recognize instances of malicious port scanning. In general, a port scan may be an instance of a scan by attackers or an instance of a scan by network defenders. In this survey, we present research and development trends in this area. Our presentation includes a discussion of common port scan attacks. We provide a comparison of port scan methods based on type, mode of detection, mechanism used for detection and other characteristics. This survey also reports on the available data sets and evaluation criteria for port scan detection approaches.

The detection of outliers has gained considerable interest in data mining with the realization that outliers can be the key discovery to be made from very large databases. Outliers arise due to various reasons such as mechanical faults, changes in system behavior, fraudulent behavior, human error and instrument error. Indeed, for many applications the discovery of outliers leads to more interesting and useful results than the discovery of inliers. Detection of outliers can lead to identification of system faults so that administrators can take preventive measures before they escalate. It is possible that anomaly detection may enable detection of new attacks. Outlier detection is an important anomaly detection approach. In this paper, we present a comprehensive survey of well-known distance-based, density-based and other techniques for outlier detection and compare them. We provide definitions of outliers and discuss their detection based on supervised and unsupervised learning in the context of network anomaly detection.