Enhancing ERS-A Algorithm for Pattern Matching (EERS-A)

The malicious code on the network is increasingly rampant that the traditional detection method of characteristic code has been difficult to deal with malicious code, with features of various variants, deformations and other problems. In this paper we present a new static analysis model based on software fingerprint to distinguish malicious codes. Through obtaining the software call graph by disassembling the binary file and mapping it as an image, shape moments can be obtained as the software fingerprint based on the retrieval theory of content image, combined with moment theory and the image's color, texture and shape features. The idea of pattern matching is used to measure the extracted software fingerprint similarity to determine whether it is malicious code or not. Then, we analyze the collected program samples. Test and verify whether the program has good performance in uniqueness, invariability and sensibility. With the rapid popularization of Internet and corporate information technology acceleration, the computer is a great convenience to people's lives, whether it is shopping, leisure or work and more obvious the importance of the Internet, but due to the openness of the Internet and flexible application and operating system vulnerability so that people can enjoy the benefits brought by the computer at the same time, also is experiencing distress and abuse of all kinds of malicious code threats to network security events increased year by year. Network security incidents, the most serious harm caused by malicious code, causing huge economic losses to the country as a whole, society and the individual, information security has become a major challenge facing. Domestic and foreign researchers turned to the semantics of malicious code, trying to judge the signatures of the two deformation malicious code through the instruction-level semantics rather than program syntax, further can determine whether the deformation of the malicious code. Trying to evade detection of malicious code, malicious codes are disassembled and for standardization or stack analysis system call for the use of fuzzy transformation technology to discriminate. In recent years, the researchers also uses malicious code detection technology engineering methodology, based on feature detection based on the application of malicious code detection technology based on data mining and machine learning. However, while foreign research scholars during the malicious code detection and anti-virus software R & D, the attacker malicious code using anti-debugging techniques, anti-Hook technology, to detect whether the code being debugged, the to find themselves debugger or analysis environment, malicious code using fuzzy transform technique and a series of anti-debugging measures show some non-anomalous behavior of the code, so as to protect their own purposes. In this process, we need manually assistive technology. Most automated virus analysis software only to capture some of the behavior, security experts need further analysis and screening of experimental results, and finally determine the extent of the harm of malicious behavior. In summary, the malicious code on the network more and more rampant code detection method for the traditional characteristics of difficult to deal with malicious code variants, deformation problem, solve the problem of the detection of unstable deformation caused due to malicious code upgrade has become a research focus and difficulty, but also of the issues that must be resolved key issues.

The C* Conference on Computer Science and Software Engineering (C3S2E) was established in 2008. C3S2E 2010 is the third annual meeting in the C3S2E conference series and is held in Montréal, Québec, Canada, during May 19-20, 2010. The conference aims to address the need of the academic community in computer science and software engineering. It brings together academics, government and industry professionals to discuss recent progress and challenges in a broad range of areas in the field of computer science and software engineering such as algorithms and theory, artificial intelligence, bioinformatics, computer architecture, computer graphics, computer security, computer vision, data mining, databases, image processing, information retrieval, machine learning, as well as software engineering. C3S2E 2010 also serves as a platform for theoreticians and practitioners to exchange their original research ideas on academic or application aspects of computing and engineering of software systems, present their new findings on theoretical or practical aspects of computer science and software engineering, share their experiences on integrating new technologies into products and applications, discuss their work on applying computing and software engineering to real-life situations, describe their development and operations of challenging computing related systems, and identify unsolved challenges. Although it is the third time C3S2E is held, this comprehensive conference has grown from a Canadian conference into an international conference. For C3S2E 2010, we continue to keep our tradition for inviting international experts in various areas of computer science and software engineering to join our team of international program committee. As a result, our Program Committee consists of professionals from different parts of the world including Australia, Canada, Germany, Hong Kong, Italy, Japan, Portugal, South Korea, Turkey, UAE, and UK. This committee has done an excellent job in finishing the single-blind review and on-line double-blind debate processes in record time. On average, about 90% of reviews were submitted by the due date and about 3.67 reviews were received for each paper. In terms of submissions, we have attracted many high-quality papers submitted by authors globally. The paper selection process was thorough and competitive. On average, each paper was refereed by at least three reviewers, and about 18% of submissions were accepted as full research papers. This year, we have an intensive program that spans over two days. In addition to research papers and student posters, we also include position papers, which allow authors to give their opinions and vision on exploring new concepts and research directions.

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

The field of pattern recognition has emerged as one of the most challenging and important endeavours in the area of information technology research. Research in the area of pattern recognition has benefits for improving many areas of human endeavour, including medicine, the economy, the environment, and security. This book presents some of the latest advances in the area of pattern recognition theory and applications. The first half of the book discusses novel pattern classification and matching schemes, and the second half describes the application of novel tools in biometrics and digital multimedia. The applications included, such as face/iris recognition, handwriting recognition, surveillance, human dynamics, sensor fusion, etc., provide a detailed insight into how to build real pattern recognition systems and how to evaluate them. Given the dynamic nature of technology evolution in this area, this book provides the latest algorithms and concepts that can be used to build real systems. Features and topics: Provides state-of-the art algorithms, as well as presents cutting-edge applications within the field Introduces achievements in theoretical pattern recognition, including statistical and Bayesian pattern recognition, structural pattern recognition, neural networks, classification and data mining, evolutionary approaches to optimisation, and knowledge based systems Offers insights and support to practitioners concerned with the state-of-the art technology in the area Progress in Pattern Recognition addresses the needs of postgraduate students, researchers, and practitioners in the areas of computer science, engineering and mathematics where pattern recognition techniques are widely used. Professor Sameer Singh is Director of the Research School of Informatics, Loughborough University, UK, and serves as Editor-in-Chief of the Springer journal, Pattern Analysis and Applications

With the rapid development of information technology and the rapid popularization of the Internet, while people enjoy the convenience and efficiency brought about by new technologies, they are also suffering from the harm caused by cyber attacks. In addition to efficiently thwarting network assaults, a high volume of complicated security event data might unintentionally increase the strain of policy makers. At present, NS threats mainly include network viruses, trojans, DOS (Denial-Of-Service), etc. For the increasingly complex Network Security (NS) problems, the traditional rule-based network monitoring technology is difficult to predict the unknown attack behavior. Environment-based, dynamic and integrated data fusion can integrate data from a macro perspective. In recent years, Machine Learning (ML) technology has developed rapidly, which could easily train, test and predict existing third-party models. It uses ML algorithms to find out the association between data rather than manually sets rules. Support vector machine is a common ML method, which can predict the security of the network well after training and testing. In order to monitor the overall security status of the entire network, NS situation awareness refers to the real-time and accurate reproduction of network attacks using the reconstruction approach. Situation awareness technology is a powerful network monitoring and security technology, but there are many problems in the existing NS technology. For example, the state of the network cannot be accurately detected, and its change rule cannot be understood. In order to effectively predict network attacks, this paper adopted a technology based on ML and data analysis, and constructed a NS situational awareness model. The results showed that the detection efficiency of the model based on ML and data analysis was 7.18% higher than that of the traditional NS state awareness model.

Humans have used electronic information retrieval (IR) systems for more than 50 years as they evolved from experimental systems to full-scale Web search engines and digital libraries. The fields of library and information science (LIS), cognitive science, human factors and computer science have historically been the leading disciplines in conducting research that seeks to model human interaction with IR systems for all kinds of information related behaviors. As technology problems have been mastered, the theoretical and applied framework for studying human interaction with IR systems has evolved from systems-centered to more user-centered, or cognitive-centered approaches. However, cognitive information retrieval (CIR) research that focuses on user interaction with IR systems is still largely under-funded and is often not included at computing and systems design oriented conferences. But CIR-focused research continues, and there are signs that some IR systems designers in academia and the Web search business are realizing that user behavior research can provide valuable insights into systems design and evaluation. The goal of our book is to provide an overview of new CIR research directions. This book does not provide a history of the research field of CIR. Instead, the book confronts new ways of looking at the human information condition with regard to our increasing need to interact with IR systems. The need has grown due to a number of factors, including the increased importance of information to more people in this information age. Also, IR was once considered document-oriented, but has now evolved to include multimedia, text, and other information objects. As a result, IR systems and their complexity have proliferated as users and user purposes for using them have also proliferated. Human interaction with IR systems can often be frustrating as people often lack an understanding of IR system functionality. New more holistic directions in CIR are emerging that conceptualize human-IR system interaction at the human-computer interaction level (taking into account interface issues), the information behavior level (taking into account the role of IR system interaction in the total range of people’s information behaviors), and the

Due to the development of technology and computer science, especially in the field of artificial intelligence and machine learning, today information technologies have become the most important element in most fields. The developments in these technologies included building models and systems that depend mainly on machine learning. One of the most important areas that we need to develop is pattern recognition and classification. The term patterns include giving the computer the ability to recognize and classify images, shapes, sounds, and digital and textual data into different classes. These systems depend on building a model based on artificial intelligence and a trained neural network to analyze and classify patterns. In this article, we develop neural network algorithms based on five different datasets to build an intelligent pattern classification and recognition system by providing a neural network method for pattern recognition (NNM - PR). Using these algorithms, a high accuracy scale was obtained compared to previous studies.

The graph is an efficient data structure to represent multi-dimensional data and their complex relations. Pattern matching and data mining are the two important fields of computer science. Pattern matching finds a particular pattern in the given input where as data mining deals with selecting specific data from the huge databases. This work contributes towards the combination of graph theory, pattern recognition and graph based databases. A variety of graph based techniques have been proposed as a powerful tool for pattern representation and classification in the past years. For a longer time graphs remained computationally expensive tool. But recently the graph based structural pattern recognition and image processing is becoming popular. The computational complexity of the graph based methods is becoming feasible due to high end new generations of the computers and the research advancements. In this work we have implemented graph based fingerprint recognition algorithm. The fingerprints are represented as attributed relational graphs. In the pattern recognition phase graph matching is applied. This study focuses on the clustering of graph databases prior to graph matching. When the structural feature set size of the data grows longer, graph matching becomes expensive. The clustering of graph databases drastically reduce the graph matching candidates.

Clustering of data points in metric space is among the most fundamental problems in computer science with plenty of applications in data mining, information retrieval and machine learning. Many of these applications deal with large datasets, and hence researchers focused on designing algorithms for these problems in large scale settings such as the streaming setting. One of the sweet versions of clustering problems is balanced clustering (or more generally capacitated clustering), where we do not desire to have some giant and several small clusters. Despite the importance of the context, the best known streaming algorithm for capacitated clustering is far from optimal. The state-of-the-art streaming algorithm for capacitated clustering gives an O(1)-approximate solution, requires three passes and only handles insertions (Bateni et al. NeurIPS'14).

He significantly contributed to research and development of the image processing, classification, and retrieval methods extensively used in the NLM's Open-i Search Engine for biomedical literature. Dr. Rahman has good expertise in the fields of Computer Vision, Image Processing, Information Retrieval, Machine Learning, and Data Mining and their application to retrieval of biomedical images from large collections. Since joining Morgan, Dr. Rahman also has

Neural network-based machine learning is capable of approximating functions\nin very high dimension with unprecedented efficiency and accuracy. This has\nopened up many exciting new possibilities, not just in traditional areas of\nartificial intelligence, but also in scientific computing and computational\nscience. At the same time, machine learning has also acquired the reputation of\nbeing a set of "black box" type of tricks, without fundamental principles. This\nhas been a real obstacle for making further progress in machine learning. In\nthis article, we try to address the following two very important questions: (1)\nHow machine learning has already impacted and will further impact computational\nmathematics, scientific computing and computational science? (2) How\ncomputational mathematics, particularly numerical analysis, {can} impact\nmachine learning? We describe some of the most important progress that has been\nmade on these issues. Our hope is to put things into a perspective that will\nhelp to integrate machine learning with computational mathematics.\n

Recently, many fields have widely used cluster analysis: psychology, biology, statistics, pattern recognition, information retrieval, machine learning, and data mining. Diagnosis of histopathological images of prostate cancer is one of the routine tasks for pathologists and it is challenging for pathologists to analyze the formation of glands and tumors based on the Gleason grading system. In this study, unsupervised classification has been performed for differentiating malignant (cancerous) from benign (non-cancerous) tumors. Therefore, the unsupervised-based computer-aided diagnosis (CAD) technique would be of great benefit in easing the workloads of pathologists. This technique is used to find meaningful clustering objects (i.e., individuals, entities, patterns, or cases) and identify useful patterns. Radiomic-based features were extracted for cluster analysis using the gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and gray-level size zone matrix (GLSZM) techniques. Multi-clustering techniques used for the unsupervised classification are K-means clustering, K-medoids clustering, Agglomerative Hierarchical (AH) clustering, Gaussian mixture model (GMM) clustering, and Spectral clustering. The quality of the clustering algorithms was determined using Purity, Silhouettes, Adjusted Rand, Fowlkes Mallows, and Calinski Harabasz (CH) scores. However, the best-performing algorithm (i.e., K-means) has been applied to predict and annotate the cancerous regions in the whole slide image (WSI) to compare with the pathologist annotation.

Cluster analysis (Jain & Dubes, 1988) provides insight into the data by dividing the objects into groups (clusters), such that objects in a cluster are more similar to each other than objects in other clusters. Cluster analysis has long played an important role in a wide variety of fields, such as psychology, bioinformatics, pattern recognition, information retrieval, machine learning, and data mining. Many clustering algorithms, such as K-means and Unweighted Pair Group Method with Arithmetic Mean (UPGMA), have been wellestablished. A recent research focus on clustering analysis is to understand the strength and weakness of various clustering algorithms with respect to data factors. Indeed, people have identified some data characteristics that may strongly affect clustering analysis including high dimensionality and sparseness, the large size, noise, types of attributes and data sets, and scales of attributes (Tan, Steinbach, & Kumar, 2005). However, further investigation is expected to reveal whether and how the data distributions can have the impact on the performance of clustering algorithms. Along this line, we study clustering algorithms by answering three questions: 1. What are the systematic differences between the distributions of the resultant clusters by different clustering algorithms? 2. How can the distribution of the “true” cluster sizes make impact on the performances of clustering algorithms? 3. How to choose an appropriate clustering algorithm in practice? The answers to these questions can guide us for the better understanding and the use of clustering methods. This is noteworthy, since 1) in theory, people seldom realized that there are strong relationships between the clustering algorithms and the cluster size distributions, and 2) in practice, how to choose an appropriate clustering algorithm is still a challenging task, especially after an algorithm boom in data mining area. This chapter thus tries to fill this void initially. To this end, we carefully select two widely used categories of clustering algorithms, i.e., K-means and Agglomerative Hierarchical Clustering (AHC), as the representative algorithms for illustration. In the chapter, we first show that K-means tends to generate the clusters with a relatively uniform distribution on the cluster sizes. Then we demonstrate that UPGMA, one of the robust AHC methods, acts in an opposite way to K-means; that is, UPGMA tends to generate the clusters with high variation on the cluster sizes. Indeed, the experimental results indicate that the variations of the resultant cluster sizes by K-means and UPGMA, measured by the Coefficient of Variation (CV), are in the specific intervals, say [0.3, 1.0] and [1.0, 2.5] respectively. Finally, we put together K-means and UPGMA for a further comparison, and propose some rules for the better choice of the clustering schemes from the data distribution point of view.

Machine learning (ML) and pattern matching (PM) are powerful computer science techniques which can derive knowledge from big data, and provide prediction and matching. Since nanometer VLSI design and manufacturing have extremely high complexity and gigantic data, there has been a surge recently in applying and adapting machine learning and pattern matching techniques in VLSI physical design (including physical verification), e.g., lithography hotspot detection and data/pattern-driven physical design, as ML and PM can raise the level of abstraction from detailed physics-based simulations and provide reasonably good quality-of-result. In this paper, we will discuss key techniques and recent results of machine learning and pattern matching, with their applications in physical design.

Pattern matching is essential to a wide range of applications such as network intrusion detection, virus scanning, etc. Pattern matching algorithms normally rely on state machines to detect predefined patterns. Recently, parallel pattern matching engines, based on ASICs, FPGAs or network processors, perform matching with multiple state machines. The state migration in the matching procedure incurs intensive memory accesses. Thus, it is critical to minimize the storage of state machines such that they can be fit in on-chip or other fast memory modules to achieve high-speed pattern matching. This paper proposes novel optimization techniques, namely state re-labeling and memory partition, to reduce state machine storage. The paper also presents architectural designs based on the optimization strategy. We evaluate our design using realistic pattern sets, and the results show state machine memory reduction up to 80.1%.