ahmad akbari Articles List

List of Articles ahmad akbari

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1 - Using Context Dependent Information for Discriminative Spoken Term Detection
S. Tabibian Ahmad Akbari B. Nasersharif
20.1001.1.16823745.1394.13.2.3.0

Spoken Term Detection (STD) approaches can be divided into two main groups: Hidden Markov Model (HMM)-based and Discriminative STD (DSTD) approaches. One of the important advantages of HMM-based methods is that they can use context dependent (diphone or triphones) infor More

Spoken Term Detection (STD) approaches can be divided into two main groups: Hidden Markov Model (HMM)-based and Discriminative STD (DSTD) approaches. One of the important advantages of HMM-based methods is that they can use context dependent (diphone or triphones) information to improve the whole STD system performance. On the other hand, lack of triphones information is one of the significant drawbacks of DSTD methods. In this paper, we propose a solution to overcome this drawback of DSTD systems. To this end, we modify the feature extraction part of an Evolutionary DSTD (EDSTD) system to consider triphones information. At first, we propose a monophone-based feature extraction part for the EDSTD system. Then, we propose an approach for exploiting triphones information in the EDSTD system. The results on TIMIT database indicate that the true detection rate of the triphone-based EDSTD (Tph-EDSTD) system, in false alarm per keyword per hour greater than two, is about 3% higher than that of the monophone-based EDSTD (Mph-SDSTD) system. This improvement costs about 36% degradation of the system response speed which is neglected. Manuscript profile
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2 - Human Activity Recognition using Switching Structure Model
Mohammad Mahdi Arzani M. Fathy Ahmad Akbari
20.1001.1.16823745.1399.18.1.10.0

To communicate with people interactive systems often need to understand human activities in advance. However, recognizing activities in advance is a very challenging task, because people perform their activities in different ways, also, some activities are simple while More

To communicate with people interactive systems often need to understand human activities in advance. However, recognizing activities in advance is a very challenging task, because people perform their activities in different ways, also, some activities are simple while others are complex and comprised of several smaller atomic sub-activities. In this paper, we use skeletons captured from low-cost depth RGB-D sensors as high-level descriptions of the human body. We propose a method capable of recognizing simple and complex human activities by formulating it as a structured prediction task using probabilistic graphical models (PGM). We test our method on three popular datasets: CAD-60, UT-Kinect, and Florence 3D. These datasets cover both simple and complex activities. Also, our method is sensitive to clustering methods that are used to determine the middle states, we evaluate test different clustering, methods. Manuscript profile
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3 - SAHAR: An Architecture to Strengthen the Control Plane of the Software-Defined Network Against Denial of Service Attacks
mehran shetabi Ahmad Akbari
20.1001.1.16823745.1399.18.4.12.8

Software-defined network (SDN) is the next generation of network architecture thatby separating the data plane and the control plane enables centralized control with the aim of improving network management and compatibility. However, due to the centralized control polic More

Software-defined network (SDN) is the next generation of network architecture thatby separating the data plane and the control plane enables centralized control with the aim of improving network management and compatibility. However, due to the centralized control policy, this type of network is prone to Inaccessibility of control plane against a denial of service (DoS) attack. In the reactive mode, a significant increase in events due to the entry of new flows into the network puts a lot of pressure on the control plane. Also, the presence of recurring events such as the collection of statistical information from the network, which severely interferes with the basic functionality of the control plane, can greatly affect the efficiency of the control plane. To resist attack and prevent network paralysis, this paper introduces a new architecture called SAHAR, which consists of a control box consisting of a coordinator controller, a primary flow setup controller, and one or more (as needed) secondary flow setup controller(s). Assigning monitoring and managing tasks to the coordinator controller reduces the load of flow setup controllers. In addition, dividing the incoming traffic between the flow setup controllers by the coordinator controller distributes the load at the control plane. Thus, by assigning the traffic load resulting from a denial-of-service attack to one or more secondary flow setup controller(s), the SAHAR architecture can prevent the primary flow setup controller from impairment and resist DoS attacks. Tests show that SAHAR performs better in the face of a DoS attack than existing solutions. Manuscript profile
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4 - Outlier Detection in High Dimensional Data Using Entropy-Based Locally Relevant Subspace Selection
Mahboobeh Riahi Madvar Ahmad Akbari B. Nasersharif
20.1001.1.16823745.1400.19.4.16.9

One of the challenges of high dimensional outlier detection problem is the curse of dimensionality which irrelevant dimensions (features) lead to hidden outliers. To solve this problem, some dimensions that contain valuable information to detect outliers are searched to More

One of the challenges of high dimensional outlier detection problem is the curse of dimensionality which irrelevant dimensions (features) lead to hidden outliers. To solve this problem, some dimensions that contain valuable information to detect outliers are searched to make outliers more prominent and detectable by mapping the dataset into the subspace which is constituted of these relevant dimensions/features. This paper proposes an outlier detection method in high dimensional data by introducing a new locally relevant subspace selection and developing a local density-based outlier scoring. First, we present a locally relevant subspace selection method based on local entropy to select a relevant subspace for each data point due to its neighbors. Then, each data point is scored in its relevant subspace using a density-based local outlier scoring method. Our adaptive-bandwidth kernel density estimation method eliminates the slight difference between the density of a normal data point and its neighbors. Thus, normal data are not wrongly detected as outliers. At the same time, our method underestimates the actual density of outlier data points to make them more prominent. The experimental results on several real datasets show that our local entropy-based subspace selection algorithm and the proposed outlier scoring can achieve a high accuracy detection rate for the outlier data. Manuscript profile
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5 - Improving IoT Botnet Anomaly Detection Based on Dynamic Feature Selection and Hybrid Processing
Boshra Pishgoo Ahmad akbari azirani
20.1001.1.16823745.1401.20.2.8.4

The complexity of real-world applications, especially in the field of the Internet of Things, has brought with it a variety of security risks. IoT Botnets are known as a type of complex security attacks that can be detected using machine learning tools. Detection of the More

The complexity of real-world applications, especially in the field of the Internet of Things, has brought with it a variety of security risks. IoT Botnets are known as a type of complex security attacks that can be detected using machine learning tools. Detection of these attacks, on the one hand, requires the discovery of their behavior patterns using batch processing with high accuracy, and on the other hand, must be operated in real time and adaptive like stream processing. This highlights the importance of using batch/stream hybrid processing techniques for botnet detection. Among the important challenges of these processes, we can mention the selection of appropriate features to build basic models and also the intelligent selection of basic models to combine and present the final result. In this paper, we present a solution based on a combination of stream and batch learning methods with the aim of botnet anomaly detection. This approach uses a dynamic feature selection method that is based on a genetic algorithm and is fully compatible with the nature of hybrid processing. The experimental results in a data set consisting of two known types of botnets indicate that on the one hand, the proposed approach increases the speed of hybrid processing and reduces the detection time of the botnets by reducing the number of features and removing inappropriate features, and on the other hand, increases accuracy by selecting appropriate models for combination. Manuscript profile

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List of Articles ahmad akbari