Destructive Attacks Detection and Response System for Physical Devices in Cyber-Physical Systems

Nowadays, physical health of equipment controlled by Cyber-Physical Systems (CPS) is a significant concern. This paper reports a work, in which, a hardware is placed between Programmable Logic Controller (PLC) and the actuator as a solution. The proposed hardware operates in two conditions, i.e. passive and active. Operation of the proposed solution is based on the repetitive operational profile of the actuators. The normal operational profile of the actuator is fed to the protective hardware and is considered as the normal operating condition. In the normal operating condition, the middleware operates in its passive mode and simply monitors electronic signals passing between PLC and Actuator. In case of any malicious operation, the proposed hardware operates in its active mode and both slowly stops the actuator and sends an alert to SCADA server initiating execution of the actuator’s emergency profile. Thus, the proposed hardware gains control over the actuator and prevents any physical damage on the operating devices. Two sample experiments are reported in which, results of implementing the proposed solution are reported and assessed. Results show that once the PLC sends incorrect data to actuator, the proposed hardware detects it as an anomaly. Therefore, it does not allow the PLC to send incorrect and unauthorized data pattern to its actuator. Significance of the paper is in introducing a solution to prevent destruction of physical devices apart from source or purpose of the encountered anomaly and apart from CPS functionality or PLC model and operation

Detection of repetitive and irregular hypercall attacks from guest virtual machines to Xen hypervisor

Virtualization is critical to the infrastructure of cloud computing environment and other online services. Hypercall interface is provided by hypervisor to offer privileged requests by the guest domains. Attackers may use this interface to send malicious hypercalls. In the reported work, repetitive hypercall attacks and sending hypercalls within irregular sequences to Xen hypervisor were analyzed, and finally, an intrusion detection system (IDS) is proposed to detect these attacks. The proposed system is placed in the host domain (Dom0). Monitoring hypercalls traffic the system operates based on the identification of irregular behaviors in hypercalls sent from guest domains to hypervisor. Later on, the association rule algorithm is applied on the collected data within a fixed time window, and a set of thresholds for maximum number of all types of the hypercalls is extracted. The results from the implementation of the proposed system show 91% true positive rate

A strategy for trust propagation along the more trusted paths

The main goal of social networks are sharing and exchanging information among users. With the rapid growth of social networks on the Web, the most of interactions are conducted among unknown individuals. On the other hand, with increasing the biased behaviors in online communities, ability to assess the level of trustworthiness of a person before interacting with him has an important influence on users' decisions. Trust inference is a method used for this purpose. This paper studies propagating trust values along trust relationships in order to estimate the reliability of an anonymous person from the point of view of the user who intends to trust him/her. It describes a new approach for predicting trust values in social networks. The proposed method selects the most reliable trust paths from a source node to a destination node. In order to select the optimal paths, a new relation for calculating trustable coefficient based on previous performance of users in the social network is proposed. In ciao dataset there is a column called helpfulness. Helpfulness values represent previous performance of users in the social network. Advantages of this algorithm is its simplicity in trust calculation, using a new entity in dataset and its improvement in accuracy. The results of the experiments on Ciao dataset indicate that accuracy of the proposed method in evaluating trust values is higher than well-known methods in this area including TidalTrust, MoleTrust methods

Camera pose estimation in unknown environments using a sequence of wide-baseline monocular images

In this paper, a feature-based technique for the camera pose estimation in a sequence of wide-baseline images has been proposed. Camera pose estimation is an important issue in many computer vision and robotics applications, such as, augmented reality and visual SLAM. The proposed method can track captured images taken by hand-held camera in room-sized workspaces with maximum scene depth of 3-4 meters. The system can be used in unknown environments with no additional information available from the outside world except in the first two images that are used for initialization. Pose estimation is performed using only natural feature points extracted and matched in successive images. In wide-baseline images unlike consecutive frames of a video stream, displacement of the feature points in consecutive images is notable and hence cannot be traced easily using patch-based methods. To handle this problem, a hybrid strategy is employed to obtain accurate feature correspondences. In this strategy, first initial feature correspondences are found using similarity of their descriptors and then outlier matchings are removed by applying RANSAC algorithm. Further, to provide a set of required feature matchings a mechanism based on sidelong result of robust estimator was employed. The proposed method is applied on indoor real data with images in VGA quality (640×480 pixels) and on average the translation error of camera pose is less than 2 cm which indicates the effectiveness and accuracy of the proposed approach

A Novel Non-Negative Matrix Factorization Method for Recommender Systems

Recommender systems collect various kinds of data to create their recommendations. Collaborative filtering is a common technique in this area. This technique gathers and analyzes information on users preferences, and then estimates what users will like based on their similarity to other users. However, most of current collaborative filtering approaches have faced two problems: sparsity and scalability. This paper proposes a novel method by applying non-negative matrix factorization, which alleviates these problems via matrix factorization and similarity. Non-negative matrix factorization attempts to find two non-negative matrices whose product can well approximate the original matrix. It also imposes non-negative constraints on the latent factors. The proposed method presents novel update rules to learn the latent factors for predicting unknown rating. Unlike most of collaborative filtering methods, the proposed method can predict all the unknown ratings. It is easily implemented and its computational complexity is very low. Empirical studies on MovieLens and Book-Crossing datasets display that the proposed method is more tolerant against the problems of sparsity and scalability, and obtains good results

A Seven-Valued Full Adder/Subtractor Architecture

Current generation of computers is based on binary logic. There are two types or operations executed in this generation i.e., mathematical and logical operations. Logical instructions use binary logic operations while the mathematical operations yet again use the mathematical operations based on binary logic. This article introduces a new idea based on Multi-Valued Logic (MVL) to build a full adder. Here mathematical and logical operations are considered separately. The reported work only considers mathematical operation and more specifically the full adder. The proposed full-adder circuit is based on Operational Amplifier (Op-Amp) and uses MVL with seven electrical levels for its design. This work is implemented in voltage mode and it is a step towards a new generation of computers. Schematic, layout, design and test results for the proposed full-adder are reported in the article

A Novel Seven-valued Buffer

In many-valued logic, voltage regulation for individual logic levels plays an important role in building stacking capability for the circuitry. The main problem is the noise immunity and is shows itself in cascading a number of components such as full adders. Multi-Valued Buffer (MVB) represents a solution to improve noise immunity and to solve the cascading problem of multi-valued based circuits e.g. seven level full adder. The proposed MVB is designed in voltage mode. This buffer is expandable and it is capable of being implemented using more levels with the same approach

A Novel Non-Negative Matrix Factorization Method for Recommender Systems

Recommender systems collect various kinds of data to create their recommendations. Collaborative filtering is a common technique in this area. This technique gathers and analyzes information on users preferences, and then estimates what users will like based on their similarity to other users. However, most of current collaborative filtering approaches have faced two problems: sparsity and scalability. This paper proposes a novel method by applying non-negative matrix factorization, which alleviates these problems via matrix factorization and similarity. Non-negative matrix factorization attempts to find two non-negative matrices whose product can well approximate the original matrix. It also imposes non-negative constraints on the latent factors. The proposed method presents novel update rules to learn the latent factors for predicting unknown rating. Unlike most of collaborative filtering methods, the proposed method can predict all the unknown ratings. It is easily implemented and its computational complexity is very low. Empirical studies on MovieLens and Book-Crossing datasets display that the proposed method is more tolerant against the problems of sparsity and scalability, and obtains good results