运行时含木马电路IP核的定位与替换

近年来，由集成电路（IC）供应链的全球化带来的硬件木马电路威胁引发了广泛的关注。现有运行阶段针对不可信第三方知识产权核（3PIP）的木马电路检测技术没有考虑到检测到木马电路时被感染IP核的定位和替换问题，使得系统时间性能开销增加、可靠性下降。为了解决该问题，基于IP核多样性和现有的木马电路检测和出错恢复架构，提出了一个面向开销优化的被感染IP核定位和替换的解决方案。定位技术通过检测阶段存储结果和恢复阶段运行结果的实时比较实现，并在理论上证明了定位的准确性；替换技术通过可编程逻辑实现，能够最小程度地影响系统下次的正常运行。实验表明，在检测到木马电路时，被感染IP核的定位技术相比于已有的木马电路检测和恢复技术能够平均提升25%的系统时间开销。提出的定位和替换方案对于使用不可信单元建立可信计算系统，以及加强系统的安全性具有一定的指导意义。     

Modelling of content-aware indicators for effective determination of shot boundaries in compressed MPEG videos

In this paper, a content-aware approach is proposed to design multiple test conditions for shot cut detection, which are organized into a multiple phase decision tree for abrupt cut detection and a finite state machine for dissolve detection. In comparison with existing approaches, our algorithm is characterized with two categories of content difference indicators and testing. While the first category indicates the content changes that are directly used for shot cut detection, the second category indicates the contexts under which the content change occurs. As a result, indications of frame differences are tested with context awareness to make the detection of shot cuts adaptive to both content and context changes. Evaluations announced by TRECVID 2007 indicate that our proposed algorithm achieved comparable performance to those using machine learning approaches, yet using a simpler feature set and straightforward design strategies. This has validated the effectiveness of modelling of content-aware indicators for decision making, which also provides a good alternative to conventional approaches in this topic.     

基于RNN的Webshell检测研究

近年来,互联网行业发展迅速,网络安全的重要性与日俱增。网络安全领域涉及到各种问题,比如恶意代码检测、攻击溯源等,而Webshell作为一种恶意代码,也得到了学术界和业界的关注。Webshell的检测方法除了简单低效的关键词匹配之外,还有各种机器学习算法。Webshell代码经过逃逸技术处理之后,基于关键词匹配的检测算法无法有效检测出Webshell,常规的机器学习算法不能提取深层特征,检测准确率不高。因此,提出基于RNN的Webshell检测方法。实验结果表明,该方法在准确率、漏报率、误报率等指标上优于传统的机器学习算法。     

Efficient hardware trojan diagnosis in SRAM based on FPGA processors using inject detect masking algorithm for multimedia signal Processors

The multimedia processor is the most powerful and challenging application in real-time world where Hardware Trojans (HTs) is a significant threat in most of the electronic devices which use Integrated Circuit (IC) as a crucial component. Since IC is manufactured by most of the untrusted designers, there is a possibility of inserting malicious attacks in any stages of fabrication. It is mainly added by an antagonist into the storage cell to make a detection process is a complex task, which creates an impact in the function of the device. To mitigate these issues, an IDM (Inject Detect Masking) algorithm is proposed, and it is implemented in a Look-Up Table (LUT) design, which exploited Stability Enhancing Static Random Access Memory (SESRAM) cell for storing the data bits. HT is injected at the output of the SESRAM cell, and then masking is applied to mitigate the HT. The proposed Inject Detect Masking (IDM) algorithm is designed and simulated in Tanner EDA with 125 nm technology. It is used to multimedia signal processors world in real-time applications to achieve better response in processor end solutions. It increases the detection rate by 8.88%, 8.88%, 5.37%, 4.25% and correction coverage by 5.26%, 28.20%, 21.95%, 13.63%, 11.11%, 7.52% when compared with Online Checking Technique, Simultaneous Orthogonal Matching Pursuit (SOMP) algorithm, Multiple Excitation of Rare Switching (MERS), LMDet and Clustering Ensemble-based Detection respectively.     

Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system

Intrusion detection has become essential to network security because of the increasing connectivity between computers. Several intrusion detection systems have been developed to protect networks using different statistical methods and machine learning techniques. This study aims to design a model that deals with real intrusion detection problems in data analysis and classify network data into normal and abnormal behaviors. This study proposes a multi-level hybrid intrusion detection model that uses support vector machine and extreme learning machine to improve the efficiency of detecting known and unknown attacks. A modified K-means algorithm is also proposed to build a high-quality training dataset that contributes significantly to improving the performance of classifiers. The modified K-means is used to build new small training datasets representing the entire original training dataset, significantly reduce the training time of classifiers, and improve the performance of intrusion detection system. The popular KDD Cup 1999 dataset is used to evaluate the proposed model. Compared with other methods based on the same dataset, the proposed model shows high efficiency in attack detection, and its accuracy (95.75%) is the best performance thus far.     

Twin support vector machines and subspace learning methods for microcalcification clusters detection

This paper presents a novel framework for microcalcification clusters (MCs) detection in mammograms. The proposed framework has three main parts: (1) first, MCs are enhanced by using a simple-but-effective artifact removal filter and a well-designed high-pass filter; (2) thereafter, subspace learning algorithms can be embedded into this framework for subspace (feature) selection of each image block to be handled; and (3) finally, in the resulted subspaces, the MCs detection procedure is formulated as a supervised learning and classification problem, and in this work, the twin support vector machine (TWSVM) is developed in decision-making of MCs detection. A large number of experiments are carried out to evaluate and compare the MCs detection approaches, and the effectiveness of the proposed framework is well demonstrated.     

Application of Multiobjective Genetic Programming to the Design of Robot Failure Recognition Systems

We present an evolutionary approach using multiobjective genetic programming (MOGP) to derive optimal feature extraction preprocessing stages for robot failure detection. This data-driven machine learning method is compared both with conventional (nonevolutionary) classifiers and a set of domain-dependent feature extraction methods. We conclude MOGP is an effective and practical design method for failure recognition systems with enhanced recognition accuracy over conventional classifiers, independent of domain knowledge.     

Forecasting Bank Failure: Base Learners,Ensembles and Hybrid Ensembles

The prediction of bankruptcy for financial companies, especially banks, has been extensively researched area and creditors, auditors, stockholders and senior managers are all interested in bank bankruptcy prediction. In this paper, three common machine learning models namely Logistic, J48 and Voted Perceptron are used as the base learners. In addition, an attribute-base ensemble learning method namely Random Subspaces and two instance-base ensemble learning methods namely Bagging and Multi-Boosting are employed to enhance the prediction accuracy of conventional machine learning models for bank failure prediction. The models are grouped in the following families of approaches: (i) conventional machine learning models, (ii) ensemble learning models and (iii) hybrid ensemble learning models. Experimental results indicate a clear outperformance of hybrid ensemble machine learning models over conventional base and ensemble models. These results indicate that hybrid ensemble learning models can be used as a reliable predicting model for bank failures.     

3D Lip-Synch Generation with Data-Faithful Machine Learning

This paper proposes a new technique for generating three-dimensional speech animation. The proposed technique takes advantage of both data-driven and machine learning approaches. It seeks to utilize the most relevant part of the captured utterances for the synthesis of input phoneme sequences. If highly relevant data are missing or lacking, then it utilizes less relevant (but more abundant) data and relies more heavily on machine learning for the lip-synch generation. This hybrid approach produces results that are more faithful to real data than conventional machine learning approaches, while being better able to handle incompleteness or redundancy in the database than conventional data-driven approaches. Experimental results, obtained by applying the proposed technique to the utterance of various words and phrases, show that (1) the proposed technique generates lip-synchs of different qualities depending on the availability of the data, and (2) the new technique produces more realistic results than conventional machine learning approaches.     

Practical real-time intrusion detection using machine learning approaches

The growing prevalence of network attacks is a well-known problem which can impact the availability, confidentiality, and integrity of critical information for both individuals and enterprises. In this paper, we propose a real-time intrusion detection approach using a supervised machine learning technique. Our approach is simple and efficient, and can be used with many machine learning techniques. We applied different well-known machine learning techniques to evaluate the performance of our IDS approach. Our experimental results show that the Decision Tree technique can outperform the other techniques. Therefore, we further developed a real-time intrusion detection system (RT-IDS) using the Decision Tree technique to classify on-line network data as normal or attack data. We also identified 12 essential features of network data which are relevant to detecting network attacks using the information gain as our feature selection criterions. Our RT-IDS can distinguish normal network activities from main attack types (Probe and Denial of Service (DoS)) with a detection rate higher than 98% within 2 s. We also developed a new post-processing procedure to reduce the false-alarm rate as well as increase the reliability and detection accuracy of the intrusion detection system.     

A unified perspective and new results on RHT computing, mixture based learning, and multi-learner based problem solving

On one hand, multiple object detection approaches of Hough transform (HT) type and randomized HT type have been extended into an evidence accumulation featured general framework for problem solving, with five key mechanisms elaborated and several extensions of HT and RHT presented. On the other hand, another framework is proposed to integrate typical multi-learner based approaches for problem solving, particularly on Gaussian mixture based data clustering and local subspace learning, multi-sets mixture based object detection and motion estimation, and multi-agent coordinated problem solving. Typical learning algorithms, especially those that base on rival penalized competitive learning (RPCL) and Bayesian Ying-Yang (BYY) learning, are summarized from a unified perspective with new extensions. Furthermore, the two different frameworks are not only examined with one viewed crossly from a perspective of the other, with new insights and extensions, but also further unified into a general problem solving paradigm that consists of five basic mechanisms in terms of acquisition, allocation, amalgamation, admission, and affirmation, or shortly A5 paradigm.     

BiLSTM在JavaScript恶意代码检测中的应用

传统的机器学习方法在检测JavaScript恶意代码时,存在提取特征过程复杂、计算量大、代码被恶意混淆导致难以检测的问题,不利于当前JavaScript恶意代码检测准确性和实时性的要求.基于此,提出一种基于双向长短时神经网络(BiLSTM)的JavaScript恶意代码检测方法.首先,将得到的样本数据经过代码反混淆,数据分词,代码向量化后得到适应于神经网络输入的标准化数据.其次,利用BiLSTM算法对向量化数据进行训练,学习JavaScript恶意代码的抽象特征.最后,利用学习到的特征对代码进行分类.将本文方法与深度学习方法和主流机器学习方法进行比较,结果表明该方法具有较高的准确率和较低的误报率.     

Non-Gaussian chemical process monitoring with adaptively weighted independent component analysis and its applications

Chemical process monitoring based on independent component analysis (ICA) is among the most widely used multivariate statistical process monitoring methods and has progressed very quickly in recent years. Generally, ICA methods initially employ several independent components (ICs) that are ordered according to certain criteria for process monitoring. However, fault information has no definite mapping relationship to a certain IC, and useful information might be submerged under the retained ICs. Thus, weighted independent component analysis (WICA) for fault detection and identification is proposed to process useful submerged information and reduce missed detection rates of I² statistics. The main idea of WICA is to initially build the conventional ICA model and then use the change rate of the I² statistic (RI²) to evaluate the importance of each IC. The important ICs tend to have higher RI²; thus, higher weighting values are then adaptively set for these ICs to highlight the useful fault information. Case studies on both simple simulated and Tennessee Eastman processes demonstrate the effectiveness of the WICA method. Monitoring results indicate that the performance of I² statistics improved significantly compared with principal component analysis and conventional ICA methods.     

胃镜下早期胃癌计算机辅助分析研究综述

胃癌是全世界癌症死亡的第三大主要原因,胃癌的早期检测会对胃癌患者的后期治疗起到至关重要的作用。随着人工智能的发展,可以利用计算机视觉领域的机器学习模型辅助检测早期胃癌,有研究发现一些计算机辅助诊断模型的筛查率接近甚至高于医生。利用计算机辅助诊断可以及早发现胃癌以减少胃癌患者的后期治疗成本。报告了基于机器学习在胃镜下早期胃癌辅助诊断的研究现状,介绍了胃镜下早期胃癌的临床诊断方式,并基于此提出了计算机辅助诊断该疾病的技术路线,分析了不同诊断技术路线的研究特点,为计算机辅助诊断早期胃癌提供不同的切入点。总结了用于早期胃癌检测的机器学习、深度学习、目标检测模型,讨论了其应用于计算机辅助诊断的问题及挑战。     

基于五轴数控机床的激光在线检测方法研究

在制造行业中,零件的质量检测通常采用手工检测和离线检测,随着激光技术的发展,激光在线检测逐渐进入零件的质量检测领域;文中针对传统的零件质量检测方法的不足,分析了目前国内外激光检测技术和数控机床在线检测技术的发展态势,探讨了基于五轴数控机床的激光在线检测方法及其在检测路径设计过程中的一些亟待解决的技术问题;该方法基于光学三角测量原理,将零件加工场所和检测场所统一于数控机床,从而提高了检测活动的效率和检测结果的准确性。     

Machine vision system for automated spectroscopy

This paper describes a novel system based on the machine vision and machine learning techniques for fully automated, real-time identification of constituent elements in a sample specimen using laser-induced breakdown spectroscopy (LIBS) images. The proposed system is developed as a compact spectrum analyzer for rapid element detection using a commercially available video camera. We proposed a correlation-based pattern matching algorithm for analyzing single element spectra. However, the use of a high-speed laser and presence of numerous imperfections in the experimental setup require advanced techniques for analyzing multi-element spectra. We cast the element detection problem as a multi-label classification problem that uses support vector machines and artificial neural networks for multi-element classification. The proposed algorithms were evaluated using actual LIBS images. The machine learning approaches yielded correct identification of elements to an accuracy of 99%. Our system is useful in instances where a qualitative analysis is sufficient over a quantitative element analysis.     

CMOS电路瞬态电流测试方法研究

集成电路设计与测试是当今计算机技术研究的主要问题之一。集成电路测试技术是生产高性能集成电路和提高集成电路成品率的关键。基于固定型故障模型的测试方法已不能满足高性能集成电路,尤其是对CMOS电路的测试要求。CMOS电路的瞬态电流(IDDT)测试方法自80年代提出以来,已被工业界采用,作为高可靠芯片的测试手段。     

基于流形学习和支持向量机的太赫兹谱分类

太赫兹时域光谱技术是一门新兴光谱检测技术,广泛应用于安检及反恐、生物医学和食品质量检测等方面。太赫兹谱的分类识别技术是太赫兹光谱检测技术的一个重要环节。由于受到噪声的影响,太赫兹谱可能在高维空间中成复杂的非线性分布,传统的分类方法难以取得理想的分类效果。流形学习和支持向量机都是当前机器学习领域的研究热点,都采取了核方法来解决非线性问题,正因为两者之间有很多共通之处,将这两种方法充分结合提出了一种称之为ISOMAP-SVM的新算法。这种新算法拥有比传统的支持向量机算法更快的训练速度和更好的分类效果。实验结果表明利用新算法可以实现对不同种类药品的识别,为太赫兹光谱技术用于药品的检测和识别提供了一种新的有效方法。     

Quick energy prediction and comparison of options at the early design stage

The energy-efficient building design requires building performance simulation (BPS) to compare multiple design options for their energy performance. However, at the early stage, BPS is often ignored, due to uncertainty, lack of details, and computational time. This article studies probabilistic and deterministic approaches to treat uncertainty; detailed and simplified zoning for creating zones; and dynamic simulation and machine learning for making energy predictions. A state-of-the-art approach, such as dynamic simulation, provide a reliable estimate of energy demand, but computationally expensive. Reducing computational time requires the use of an alternative approach, such as a machine learning (ML) model. However, an alternative approach will cause a prediction gap, and its effect on comparing options needs to be investigated. A plugin for Building information modelling (BIM) modelling tool has been developed to perform BPS using various approaches. These approaches have been tested for an office building with five design options. A method using the probabilistic approach to treat uncertainty, detailed zoning to create zones, and EnergyPlus to predict energy is treated as the reference method. The deterministic or ML approach has a small prediction gap, and the comparison results are similar to the reference method. The simplified model approach has a large prediction gap and only makes only 40% comparison results are similar to the reference method. These findings are useful to develop a BIM integrated tool to compare options at the early design stage and ascertain which approach should be adopted in a time-constraint situation.     

Machine learning-based real-time monitoring system for smart connected worker to improve energy efficiency

Recent advances in machine learning and computer vision brought to light technologies and algorithms that serve as new opportunities for creating intelligent and efficient manufacturing systems. In this study, the real-time monitoring system of manufacturing workflow for the Smart Connected Worker (SCW) is developed for the small and medium-sized manufacturers (SMMs), which integrates state-of-the-art machine learning techniques with the workplace scenarios of advanced manufacturing systems. Specifically, object detection and text recognition models are investigated and adopted to ameliorate the labor-intensive machine state monitoring process, while artificial neural networks are introduced to enable real-time energy disaggregation for further optimization. The developed system achieved efficient supervision and accurate information analysis in real-time for prolonged working conditions, which could effectively reduce the cost related to human labor, as well as provide an affordable solution for SMMs. The competent experiment results also demonstrated the feasibility and effectiveness of integrating machine learning technologies into the realm of advanced manufacturing systems.