Biblio

With the great development of the information communication technology, power systems have been typical Cyber Physical Systems (CPSs). Although the control function of the grid side is becoming more intelligent, Grid Cyber Physical System (GCPS) brings the risk of potential cyberattacks. In this paper, the impacts of cyber-attacks against GCPS are analyzed based on confusion matrix model firstly, then a double-layer cyber physical collaboration control strategy adjustment methods is proposed considering the status of cyber modules and physical devices infected by cyber-attacks. Finally, the feasibility and effectiveness of the proposed method are verified on the IEEE standard system.

As a TEMPEST information security problem, electromagnetic emissions from the computer displays can be captured, and reconstructed using signal processing techniques. It is necessary to identify the display type to intercept the image of the display. To determine the display type not only significant for attackers but also for protectors to prevent display compromising emanations. This study relates to the identification of the display type using Support Vector Machines (SVM) from electromagnetic emissions emitted from computer displays. After measuring the emissions using receiver measurement system, the signals were processed and training/test data sets were formed and the classification performance of the displays was examined with the SVM. Moreover, solutions for a better classification under real conditions have been proposed. Thus, one of the important step of the display image capture can accomplished by automatically identification the display types. The performance of the proposed method was evaluated in terms of confusion matrix and accuracy, precision, F1-score, recall performance measures.

Malware classification is the process of categorizing the families of malware on the basis of their signatures. This work focuses on classifying the emerging malwares on the basis of comparable features of similar malwares. This paper proposes a novel framework that categorizes malware samples into their families and can identify new malware samples for analysis. For this six diverse classification techniques of machine learning are used. To get more comparative and thus accurate classification results, analysis is done using two different tools, named as Knime and Orange. The work proposed can help in identifying and thus cleaning new malwares and classifying malware into their families. The correctness of family classification of malwares is investigated in terms of confusion matrix, accuracy and Cohen's Kappa. After evaluation it is analyzed that Random Forest gives the highest accuracy.

Despite the popularity of wireless sensor networks (WSNs) in a wide range of applications, security problems associated with them have not been completely resolved. Middleware is generally introduced as an intermediate layer between WSNs and the end user to resolve some limitations, but most of the existing middleware is unable to protect data from malicious and unknown attacks during transmission. This paper introduces an intelligent middleware based on an unsupervised learning technique called Generative Adversarial Networks (GANs) algorithm. GANs contain two networks: a generator (G) network and a detector (D) network. The G creates fake data similar to the real samples and combines it with real data from the sensors to confuse the attacker. The D contains multi-layers that have the ability to differentiate between real and fake data. The output intended for this algorithm shows an actual interpretation of the data that is securely communicated through the WSN. The framework is implemented in Python with experiments performed using Keras. Results illustrate that the suggested algorithm not only improves the accuracy of the data but also enhances its security by protecting data from adversaries. Data transmission from the WSN to the end user then becomes much more secure and accurate compared to conventional techniques.

Facial emotion recognition is an essential aspect in human-machine interaction. In the real-world conditions, it faces many challenges, i.e., illumination changes, large pose variations and partial or full occlusions, which cause different facial areas with different sharpness and completeness. Inspired by this fact, we focus on facial expression recognition based on partial faces in this paper. We compare contribution of seven facial areas of low-resolution images, including nose areas, mouse areas, eyes areas, nose to mouse areas, nose to eyes areas, mouth to eyes areas and the whole face areas. Through analysis on the confusion matrix and the class activation map, we find that mouth regions contain much emotional information compared with nose areas and eyes areas. In the meantime, considering larger facial areas is helpful to judge the expression more precisely. To sum up, contributions of this paper are two-fold: (1) We reveal concerned areas of human in emotion recognition. (2) We quantify the contribution of different facial parts.