Review:Botnet detection techniques: review,future trends,and issue

In recent years, the Internet has enabled access to widespread remote services in the distributed computing environment; however, integrity of data transmission in the distributed computing platform is hindered by a number of security issues. For instance, the botnet phenomenon is a prominent threat to Internet security, including the threat of malicious codes. The botnet phenomenon supports a wide range of criminal activities, including distributed denial of service(DDoS) attacks, click fraud, phishing, malware distribution, spam emails, and building machines for illegitimate exchange of information/materials. Therefore, it is imperative to design and develop a robust mechanism for improving the botnet detection, analysis, and removal process. Currently, botnet detection techniques have been reviewed in different ways; however, such studies are limited in scope and lack discussions on the latest botnet detection techniques. This paper presents a comprehensive review of the latest state-of-the-art techniques for botnet detection and figures out the trends of previous and current research. It provides a thematic taxonomy for the classification of botnet detection techniques and highlights the implications and critical aspects by qualitatively analyzing such techniques. Related to our comprehensive review, we highlight future directions for improving the schemes that broadly span the entire botnet detection research field and identify the persistent and prominent research challenges that remain open.     

Cyber-physical systems security: Limitations,issues and future trends

Typically, Cyber-Physical Systems (CPS) involve various interconnected systems, which can monitor and manipulate real objects and processes. They are closely related to Internet of Things (IoT) systems, except that CPS focuses on the interaction between physical, networking and computation processes. Their integration with IoT led to a new CPS aspect, the Internet of Cyber-Physical Things (IoCPT). The fast and significant evolution of CPS affects various aspects in people’s way of life and enables a wider range of services and applications including e-Health, smart homes, e-Commerce, etc. However, interconnecting the cyber and physical worlds gives rise to new dangerous security challenges. Consequently, CPS security has attracted the attention of both researchers and industries. This paper surveys the main aspects of CPS and the corresponding applications, technologies, and standards. Moreover, CPS security vulnerabilities, threats and attacks are reviewed, while the key issues and challenges are identified. Additionally, the existing security measures are presented and analyzed while identifying their main limitations. Finally, several suggestions and recommendations are proposed benefiting from the lessons learned throughout this comprehensive review.     

A review of retinal blood vessels extraction techniques: challenges,taxonomy, and future trends

Pattern Analysis and Applications - The visual exploration of retinal blood vessels assists ophthalmologists in the diagnoses of different abnormalities of the eyes such as diabetic retinopathy,...     

Simulation of the smart grid communications: Challenges,techniques, and future trends

This paper gives an overview of available simulation techniques to support the analysis of smart grid communications. Intelligent power grids, known as smart grids, are required as the demand for energy continues to grow and more emphasis is being placed on the supply of renewable energy. The main ingredient of smart grid is the integration of information and communication technology into the grids to monitor and regulate power generation and the demand. The impact of the communication networks on the performance of power system dynamics can only be understood through numerical simulations. This paper provides an overview of available simulation techniques for smart grid communications with a particular focus on the co-simulation frameworks and their enabling technologies. Moreover, a case study with VPNET for analyzing agent based shipboard smart grid protections considering communication impacts is provided.     

Ensembles for feature selection: A review and future trends

Ensemble learning is a prolific field in Machine Learning since it is based on the assumption that combining the output of multiple models is better than using a single model, and it usually provides good results. Normally, it has been commonly employed for classification, but it can be used to improve other disciplines such as feature selection. Feature selection consists of selecting the relevant features for a problem and discard those irrelevant or redundant, with the main goal of improving classification accuracy. In this work, we provide the reader with the basic concepts necessary to build an ensemble for feature selection, as well as reviewing the up-to-date advances and commenting on the future trends that are still to be faced.     

Scene text detection and recognition: recent advances and future trends

Text, as one of the most influential inventions of humanity, has played an important role in human life, so far from ancient times. The rich and precise information embodied in text is very useful in a wide range of vision-based applications, therefore text detection and recognition in natural scenes have become important and active research topics in computer vision and document analysis. Especially in recent years, the community has seen a surge of research efforts and substantial progresses in these fields, though a variety of challenges (e.g. noise, blur, distortion, occlusion and variation) still remain. The purposes of this survey are three-fold: 1) introduce up-to-date works, 2) identify state-of-the-art algorithms, and 3) predict potential research directions in the future. Moreover, this paper provides comprehensive links to publicly available resources, including benchmark datasets, source codes, and online demos. In summary, this literature review can serve as a good reference for researchers in the areas of scene text detection and recognition.     

Automatic network intrusion detection: Current techniques and open issues

Automatic network intrusion detection has been an important research topic for the last 20 years. In that time, approaches based on signatures describing intrusive behavior have become the de-facto industry standard. Alternatively, other novel techniques have been used for improving automation of the intrusion detection process. In this regard, statistical methods, machine learning and data mining techniques have been proposed arguing higher automation capabilities than signature-based approaches. However, the majority of these novel techniques have never been deployed on real-life scenarios. The fact is that signature-based still is the most widely used strategy for automatic intrusion detection. In the present article we survey the most relevant works in the field of automatic network intrusion detection. In contrast to previous surveys, our analysis considers several features required for truly deploying each one of the reviewed approaches. This wider perspective can help us to identify the possible causes behind the lack of acceptance of novel techniques by network security experts.     

Privacy issues in intrusion detection systems: A taxonomy,survey and future directions

Intrusion Detection Systems (IDSs) detect potential attacks by monitoring activities in computers and networks. This monitoring is carried out by collecting and analyzing data pertaining to users and organizations. The data is collected from various sources – such as system log files or network traffic–and may contain private information. Therefore, analysis of the data by an IDS can raise multiple privacy concerns. Recently, building IDSs that consider privacy issues in their design criteria in addition to classic design objectives (such as IDS’ performance and precision) has become a priority. This article proposes a taxonomy of privacy issues in IDSs which is then utilized to identify new challenges and problems in the field. In this taxonomy, we classify privacy-sensitive IDS data as input, built-in and generated data. Research prototypes are then surveyed and compared using the taxonomy. The privacy techniques used in the surveyed systems are discussed and compared based on their effects on the performance and precision of the IDS. Finally, the taxonomy and the survey are used to point out a number of areas for future research.     

Technology-supported learning:status, issues and trends

Abstract  Higher education is facing a broad range of challenges upon entering the new millennium with shifts in technology, paradigms and resources for learning. In response to these challenges, teachers are broadening their range of instructional methods. One area that is seeing increased attention is the provision of instructional support for 'self-accessed' learning. This paper examines the status, issues and trends of multimedia instructional support. Examples associated with the development of CD-ROMs and interactive web sites are presented to illustrate important considerations for such development. It is hoped that these considerations will result in greater educational support for individualised learning.     

Deployment techniques in wireless sensor networks: a survey,classification, challenges,and future research issues

The Journal of Supercomputing - Wireless sensor networks (WSNs) have been considered as one of the fine research areas in recent years because of vital role in numerous applications. To process the...     

Requirement management for product-service systems: Status review and future trends

Many manufacturers today are striving to offer high value-added product-service systems (PSS) to their customers. Since the success of the PSS largely depends on the understanding and satisfying of different requirements, requirements management (RM) has become a critical factor in PSS development. However, PSS are mixed product-service offerings with features of heterogeneity, interaction, stakeholder participation and customization, which makes the PSS requirement difficult to be captured, analyzed, concretized and forecasted. Thus, this research analyzes the state of the art of requirements management for PSS by reviewing extensive literature of requirement identification, analysis, specification, and forecast. Based on the review, key challenges and future research directions are identified, and they are requirement elicitation considering interactions, hybrid PSS conflict detection and resolution, dynamic requirement forecasting with big data, smart requirement management and proactive response. This literature review lays the foundation for future research of PSS development.     

Knowledge sharing mechanisms and techniques in project teams: Literature review,classification, and current trends

Knowledge management is the process of capturing, sharing, developing, and using the knowledge efficiently. Knowledge sharing as one of the important parts of the knowledge management system means that an individual, team, and the organization share the knowledge with other members in the form of activities through the various ways. On the other hand, a project team is a team whose members usually belongs to different departments and are assigned to join the same project. In a project team, knowledge sharing is very important because it provides a link between the member and the project team by sharing knowledge to reduce cost and increase the performance. However, despite the importance of the knowledge sharing mechanisms and techniques in the project teams, to the best of our knowledge, the comprehensive and systematic research and study about the antecedents and backgrounds of knowledge sharing mechanisms between project teams is rare. Therefore, the main aim of this paper is to provide the comprehensive and detailed review of the state-of-the-art knowledge sharing mechanisms in the project team as well as directions for future research. Also, this paper presents a systematic literature review (SLR) on the knowledge sharing mechanisms in project teams up to the end of 2015. We identified 71 papers, which are reduced to 28 primary studies through our paper selection process. By providing the state-of-the-art information and the challenges issues, this survey will directly support academics, researchers and practicing professionals in their understanding of developments in knowledge sharing mechanisms and techniques in project teams.     

A review on biomedical image processing and future trends

The last two decades have witnessed a revolutionary development in the field of biomedical and diagnostic imaging. Imaging procedures and modalities which were only in the experimental research phase in the early part of the last two decades, have now become universally accepted clinical procedures. They include computerized tomography (CT), magnetic resonance imaging, ultrasound imaging, nuclear medicine imaging, computerized hematological cell analysis, etc. In the past, the conventional and relatively simple image processing techniques such as image enhancement, gray-level mapping, spectral analysis, region extraction, etc. have been modified for biomedical images and successfully applied for processing and analysis. The role of image enhancement, gray-level mapping, and image reconstruction from projections algorithms in CT and other radiological imaging modalities is well evident. Recently, many advances in biomedical image processing, analysis, and understanding algorithms have shown a great potential for enhancing and interpreting useful diagnostic information from these images more accurately. This paper presents a review on the current state-of-the-art techniques in biomedical image processing and comments on future trends.     

Beamforming techniques for massive MIMO systems in 5G: overview,classification, and trends for future research

Massive multiple-input multiple-output (MIMO) systems combined with beamforming antenna array technologies are expected to play a key role in next-generation wireless communication systems (5G), which will be deployed in 2020 and beyond. The main objective of this review paper is to discuss the state-of-the-art research on the most favourable types of beamforming techniques that can be deployed in massive MIMO systems and to clarify the importance of beamforming techniques in massive MIMO systems for eliminating and resolving the many technical hitches that massive MIMO system implementation faces. Classifications of optimal beamforming techniques that are used in wireless communication systems are reviewed in detail to determine which techniques are more suitable for deployment in massive MIMO systems to improve system throughput and reduce intra- and inter-cell interference. To overcome the limitations in the literature, we have suggested an optimal beamforming technique that can provide the highest performance in massive MIMO systems, satisfying the requirements of next-generation wireless communication systems.     

An overview of anomaly detection techniques: Existing solutions and latest technological trends

As advances in networking technology help to connect the distant corners of the globe and as the Internet continues to expand its influence as a medium for communications and commerce, the threat from spammers, attackers and criminal enterprises has also grown accordingly. It is the prevalence of such threats that has made intrusion detection systems—the cyberspace’s equivalent to the burglar alarm—join ranks with firewalls as one of the fundamental technologies for network security. However, today’s commercially available intrusion detection systems are predominantly signature-based intrusion detection systems that are designed to detect known attacks by utilizing the signatures of those attacks. Such systems require frequent rule-base updates and signature updates, and are not capable of detecting unknown attacks. In contrast, anomaly detection systems, a subset of intrusion detection systems, model the normal system/network behavior which enables them to be extremely effective in finding and foiling both known as well as unknown or “zero day” attacks. While anomaly detection systems are attractive conceptually, a host of technological problems need to be overcome before they can be widely adopted. These problems include: high false alarm rate, failure to scale to gigabit speeds, etc. In this paper, we provide a comprehensive survey of anomaly detection systems and hybrid intrusion detection systems of the recent past and present. We also discuss recent technological trends in anomaly detection and identify open problems and challenges in this area.     

基于网页挂马的僵尸网络的机理、检测与防御

随着现代信息技术的不断发展,国家、团体、个人间利益的博弈,网络安全防护正日益成为人们关注的热点话题.网页木马以页面元素作为攻击向量,利用浏览器或插件中的漏洞进行Web的客户端攻击;僵尸网络通过网页挂马广泛传播,形成大量受攻击者控制的以恶意活动为目的的覆盖网络：这已经对当前的网络安全构成了严重的威胁.本文从网页木马和僵尸网络的机理入手,深入分析了危害形成的原理和作用机制,并据此阐述了相关的检测与防御手段和方法,最后总结分析了今后的安全防御思路.     

Computational methods for pigmented skin lesion classification in images: review and future trends

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given.

     

蜜罐先知型半分布式P2P Botnet的构建及检测方法

蜜罐技术在僵尸网络（botnet）的防御和检测中扮演着重要的角色。攻击者可能会利用已有的基于蜜罐防御技术的漏洞,即防御者配置蜜罐要担当一定的责任,不允许蜜罐参与真实的攻击,进而构建出可以躲避蜜罐的botnet。针对这一问题,提出了攻击者利用认证sensor组建的蜜罐先知型半分布式P2P botnet,针对此类botnet,提出了用高交互性蜜罐和低交互性蜜罐相结合的双重蜜罐检测技术,并与传统蜜罐技术做了比较。理论分析表明,该检测方法能够有效地弥补蜜罐防御技术的漏洞,提高了蜜罐先知型半分布式P2P botnet的检出率。