seyed amin hosseini seno

In the realm of machine learning, Generative Adversarial Networks (GANs) have revolutionized the generation of synthetic data, closely mirroring the distribution of real datasets. This paper delves into the privacy concerns associated with GANs, particularly focusing on Membership Inference Attacks (MIAs), which aim to determine if a specific record was used in training a model. Such attacks pose significant privacy risks, especially when sensitive data is involved. To combat this, we propose a novel detector model designed to identify and thwart MIAs within GANs. Our model, which operates as an additional layer of protection for Machine Learning as a Service (MLaaS) providers, leverages outputs from both the discriminator and generator to ascertain the membership status of data samples. We introduce two variants of the detector model—supervised and unsupervised—based on the availability of information from the discriminator. The supervised detector employs labeled data for training, while the unsupervised detector uses anomaly detection techniques. Our experimental evaluation spans various GAN architectures and datasets, ensuring the robustness and generalizability of our approach. The paper also analyzes the impact of dataset size on the detector's effectiveness. By integrating our detector, MLaaS providers can enhance privacy safeguards, striking a balance between model utility and data protection.

The Internet of Vehicles (IoV) is an emerging concept in Intelligent Transportation Systems (ITS) that aims to improve pedestrian and driver safety and traffic monitoring, But the IoV is vulnerable to various attacks. Therefore, security in the IoV is a serious issue because it directly affects the lives of the users. One of the most important attacks in the IoV is the Denial of Service (DoS) attack, which prevents access to the services of IoV and most importantly causes traffic and road accidents and the safety of users. endangers Therefore, a solution based on deep learning is proposed to detect DoS attacks in the IoV. The proposed model consists of a 10-layer convolutional neural network that can effectively detect different types of denial of service attacks. The performance of the proposed model is evaluated with real and new VDoS-LRS dataset. Experimental results show that the proposed intrusion detection system has reached a 100% accuracy rate.

For more comprehensive security of a computer network as well as the use of firewall and anti-virus security equipment, intrusion detection systems (IDSs) are needed to detect the malicious activity of intruders. Therefore, the introduction of a high-precision intrusion detection system is critical for the network. Generally, the general framework of the proposed intrusion detection models is the use of text classification, and today deep neural networks (DNNs) are one of the top classifiers. A variety of DNN-based intrusion detection models have been proposed for software-defined networks (SDNs); however, these methods often report performance metrics solely on one well-known dataset. In this paper, we present a DNN-based IDS model with a 12-layer arrangement which works well on three datasets, namely, NSL-KDD, KDD99, and UNSW-NB15. The layered layout of the proposed model is considered the same for all the three datasets, which is one of the strengths of the proposed model. To evaluate the proposed solution, six other DNN-based IDS models have been designed. The values of the evaluation metrics, including accuracy, precision, recall, F-measure, and loss function, show the superiority of the proposed model over these six models. In addition, the proposed model is compared with several recent articles in this field, and the superiority of the proposed solution is shown.

Software defined network is a new computer network architecture who separates controller and data layer in network devices such as switches and routers. Separation of the control plane and the data plane has many benefits, such as more programmability of the control plane, the possibility of network virtualization, reduction of operating costs, greater independence of network equipment manufacturers, and so on. By the emerge of software defined networks, a class of location problems, called controller placement problem, has attracted much more research attention. The task in the problem is to simultaneously find optimal number and location of controllers satisfying a set of routing and capacity constraints. In this paper, we suggest an effective solution method based on dynamic switches allocation. We, then compare our method to an existing standard mathematical programming solver on an extensive set of problem instances. It turns out that our suggested method is computationally much more effective and efficient over small to large instances.

The appearance of social networks has been one of the most important events in recent decades. One of the issues raised in these networks, is how to trust. The purpose of this paper is to examine the impact of security, trust and privacy about information sharing on mobile social networks. The study also describes how users' gender moderates the privacy and security impact on trust. The current research is a descriptive-survey. In this research, Data from 190 social network users were gathered through a questionnaire designed in the form of a Telegram bot and examined using structural equation modeling techniques. Also, regarding to measure validity, convergent validity and divergent validity. And in order to fit reliability, Cronbach's alpha and composite reliability have been used. Analysis of the findings of the study shows that the security and privacy have a direct impact on users' trust. The findings also show that perceived security acts as a mediator between privacy and trust. The results show that trust has a significant impact on information sharing behavior and the impact of privacy and security on trust is stronger for men. In addition, the impact of trust on the desire to give information is more than desire to get information. In this study, a considerable effect between desire to get information and information sharing wasn't observed.

Placement of virtual sensors in servers of cloud environment is one of the most important issues in resource management of sensor-cloud networks. Virtual sensors allocate resources of cloud servers to themselves, run different applications and use equivalent physical sensors. Selecting an appropriate method for placement of virtual sensors on cloud servers has a significant impact on resource management and energy consumption. Also, because of the real-time requirements of sensor-cloud networks, traffic-aware placement is necessary to minimize delay in a network and respond to user requests in the shortest possible time. A new method for traffic-aware placement and migration of virtual sensors in sensor-cloud networks is proposed using the unique characteristics of sensor-cloud networks such as grouping virtual sensors and sharing them among different applications. This approach tries to meet the needs of users and the quality of service expected by the applications. Also, the resources of cloud servers are managed properly in this approach. The results of simulation show an optimization in energy consumption and also a reduction in traffic costs and reduction in service level agreement violation.

Recently, the integration of ubiquitous wireless sensor network (WSN) and powerful cloud computing (CC) has attracted growing attention and efforts in both academic and industry. In this new paradigm, cloud computing can be exploited to perform analysis of online as well as offline data streams provided by sensor networks. This can help to deal with the inherent limitations of WSN in combining and analyzing of the heterogeneous large number of sensory data. The study we present in this paper provides a comprehensive analysis and discussion of the representative works on large-scale WSNs, the need for integrating sensor with the cloud, the main challenges deriving from such integration, and future research directions in this promising field

Wireless sensor networks (WSN) is presented solution for the problem of low-cost and flexible monitoring event, especially in places with limited access or dangerous for humans. Wireless sensor networks are built from wireless devices with limited resources thus to Long-Term survival of the Network must use energy-efficient mechanisms, algorithms and protocols. one of the mechanisms controlling the topology (TC), which is composed of two parts, a topology construction and topology maintenance.This paper introduces the new algorithm for energy aware topology construction based on connected dominating set, namely, algorithm EA-CDS, wich is a simple mechanism to build the distributed, energy-efficient topology, that finds optimal connected subset and removes unnecessary nodes with minimum power consumption while the network remains connected and coverage is complete.also ,to maintain network connectivity and unfailing the nodes energy that are constantly located at sending packets path along with it , the DGTERec topology maintenance algorithm is used to balance energy consumption in nodes .Evaluation results show that the EA-CDS protocol for increasing the useful lifetime of the network (Postponing the first node death) and the number of messages sent over the same two protocols (EECDS and CDS-RULE-K) has obvious advantages.
To ensure that the results of Atarraya are acceptable,The performance of the proposed method is evaluated with MATLAB and output results analysis, confirmed the results of the ATARRAYA simulator.

Experts have increasingly noticed the necessity of aligning marketing information security policy with strategic information systems plan to enhance the utilization of information assets. Therefore, this study aimed to provide a model to explain the dimensions and components of aligning the marketing information security policy with strategic information systems plan. In this regard, dimensions and components of the alignment were extracted using the documents, libraries and previous literature and then a conceptual model was obtained using the Delphi technique. The model included following dimensions: management and governance, participation, information, information technology architecture, and communication, each with 13, 6, 10, 4, and 6 components, respectively. Using the DEMATEL method, the causal relationships between the dimensions of the alignment model were studied. The results showed that the management and governance, IT architecture and participation are the cause dimensions and the communication and information are the effect dimensions. Finally, using confirmatory factor analysis, the model was tested and some suggestions were presented to enhance the degree of the alignment of marketing information security policy with strategic information systems plan.