B.3. Communication/Networking and Information Technology
Roya Morshedi; S. Mojtaba Matinkhah; Mohammad Taghi Sadeghi
Abstract
IoT devices has witnessed a substantial increase due to the growing demand for smart devices. Intrusion Detection Systems (IDS) are critical components for safeguarding IoT networks against cyber threats. This study presents an advanced approach to IoT network intrusion detection, leveraging deep learning ...
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IoT devices has witnessed a substantial increase due to the growing demand for smart devices. Intrusion Detection Systems (IDS) are critical components for safeguarding IoT networks against cyber threats. This study presents an advanced approach to IoT network intrusion detection, leveraging deep learning techniques and pristine data. We utilize the publicly available CICIDS2017 dataset, which enables comprehensive training and testing of intrusion detection models across various attack scenarios, such as Distributed Denial of Service (DDoS) attacks, port scans, botnet activity, and more. Our goal is to provide a more effective method than the previous methods. Our proposed deep learning model incorporates dense transition layers and LSTM architecture, designed to capture both spatial and temporal dependencies within the data. We employed rigorous evaluation metrics, including sparse categorical cross-entropy loss and accuracy, to assess model performance. The results of our approach show outstanding accuracy, reaching a peak of 0.997 on the test data. Our model demonstrates stability in loss and accuracy metrics, ensuring reliable intrusion detection capabilities. Comparative analysis with other machine learning models confirms the effectiveness of our approach. Moreover, our study assesses the model's resilience to Gaussian noise, revealing its capacity to maintain accuracy in challenging conditions. We provide detailed performance metrics for various attack types, offering insights into the model's effectiveness across diverse threat scenarios.
B.3. Communication/Networking and Information Technology
S. Mojtaba Matinkhah; Roya Morshedi; Akbar Mostafavi
Abstract
The Internet of Things (IoT) has emerged as a rapidly growing technology that enables seamless connectivity between a wide variety of devices. However, with this increased connectivity comes an increased risk of cyber-attacks. In recent years, the development of intrusion detection systems (IDS) has ...
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The Internet of Things (IoT) has emerged as a rapidly growing technology that enables seamless connectivity between a wide variety of devices. However, with this increased connectivity comes an increased risk of cyber-attacks. In recent years, the development of intrusion detection systems (IDS) has become critical for ensuring the security and privacy of IoT networks. This article presents a study that evaluates the accuracy of an intrusion detection system (IDS) for detecting network attacks in the Internet of Things (IoT) network. The proposed IDS uses the Decision Tree Classifier and is tested on four benchmark datasets: NSL-KDD, BOT-IoT, CICIDS2017, and MQTT-IoT. The impact of noise on the training and test datasets on classification accuracy is analyzed. The results indicate that clean data has the highest accuracy, while noisy datasets significantly reduce accuracy. Furthermore, the study finds that when both training and test datasets are noisy, the accuracy of classification decreases further. The findings of this study demonstrate the importance of using clean data for training and testing an IDS in IoT networks to achieve accurate classification. This research provides valuable insights for the development of a robust and accurate IDS for IoT networks.
B.3. Communication/Networking and Information Technology
Newsha Nowrozian; Farzad Tashtarian; Yahya Forghani
Abstract
Wireless rechargeable sensor networks (WRSNs) are widely used in many fields. However, the limited battery capacity of sensor nodes (SNs) prevents its development. To extend the battery life of SNs, they can be charged by a mobile charger (MC) equipped with radio frequency-based wireless power transfer ...
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Wireless rechargeable sensor networks (WRSNs) are widely used in many fields. However, the limited battery capacity of sensor nodes (SNs) prevents its development. To extend the battery life of SNs, they can be charged by a mobile charger (MC) equipped with radio frequency-based wireless power transfer (WPT). The paper addressed the issue of optimizing route planning and charging based on an MC with directional charging in on-demand networks. A mixed integer linear programming model (MILP) is proposed to obtain the appropriate stopping points (SPs) and orientation charging angles to respond to input requests in the shortest possible time and with minimum energy consumption. First, to select the SPs and the orientation charging direction, we utilize a clustering and discretization technique while minimizing the number of SPs and maximizing the charging cover. Then, to decrease the charging time of the required SNs as well as the MC's energy consumption, we propose a heuristic search algorithm for adjusting the moving path for the directional mobile charger. Finally, experimental simulations are performed to evaluate the performance of the proposed directional charging scheduling algorithm, and the results reveal that the suggested approach outperforms existing studies in terms of MC energy consumption, charging delay, and distance traveled.
B.3. Communication/Networking and Information Technology
A. Azimi Kashani; M. Ghanbari; A. M. Rahmani
Abstract
Vehicular ad hoc networks are an emerging technology with an extensive capability in various applications including vehicles safety, traffic management and intelligent transportation systems. Considering the high mobility of vehicles and their inhomogeneous distributions, designing an efficient routing ...
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Vehicular ad hoc networks are an emerging technology with an extensive capability in various applications including vehicles safety, traffic management and intelligent transportation systems. Considering the high mobility of vehicles and their inhomogeneous distributions, designing an efficient routing protocol seems necessary. Given the fact that a road is crowded at some sections and is not crowded at the others, the routing protocol should be able to dynamically make decisions. On the other hand, VANET networks environment is vulnerable at the time of data transmission. Broadcast routing, similar to opportunistic routing, could offer better efficiency compared to other protocols. In this paper, a fuzzy logic opportunistic routing (FLOR) protocol is presented in which the packet rebroadcasting decision-making process is carried out through the fuzzy logic system along with three input parameters of packet advancement, local density, and the number of duplicated delivered packets. The rebroadcasting procedures use the value of these parameters as inputs to the fuzzy logic system to resolve the issue of multicasting, considering the crowded and sparse zones. NS-2 simulator is used for evaluating the performance of the proposed FLOR protocol in terms of packet delivery ratio, the end-to-end delay, and the network throughput compared with the existing protocols such as: FLOODING, P-PERSISTENCE and FUZZBR. The performance comparison also emphasizes on effective utilization of the resources. Simulations on highway environment show that the proposed protocol has a better QoS efficiency compared to the above published methods in the literature
B.3. Communication/Networking and Information Technology
Z. Shaeiri; J. Kazemitabar; Sh. Bijani; M. Talebi
Abstract
As fraudsters understand the time window and act fast, real-time fraud management systems becomes necessary in Telecommunication Industry. In this work, by analyzing traces collected from a nationwide cellular network over a period of a month, an online behavior-based anomaly detection system is provided. ...
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As fraudsters understand the time window and act fast, real-time fraud management systems becomes necessary in Telecommunication Industry. In this work, by analyzing traces collected from a nationwide cellular network over a period of a month, an online behavior-based anomaly detection system is provided. Over time, users' interactions with the network provides a vast amount of usage data. These usage data are modeled to profiles by which users can be identified. A statistical model is proposed that allocate a risk number to each upcoming record which reveals deviation from the normal behavior stored in profiles. Based on the amount of this deviation a decision is made to flag the record as normal or anomaly. If the activity is normal the associated profile is updated; otherwise the record is flagged as anomaly and it will be considered for further investigation. For handling the big data set and implementing the methodology we have used the Apache Spark engine which is an open source, fast and general-purpose cluster computing system for big data handling and analyzes. Experimental results show that the proposed approach can perfectly detect deviations from the normal behavior and can be exploited for detecting anomaly patterns.
B.3. Communication/Networking and Information Technology
V. Babaiyan; Seyyede A. Sarfarazi
Abstract
Telecommunication Companies use data mining techniques to maintain good relationships with their existing customers and attract new customers and identifying profitable/unprofitable customers. Clustering leads to better understanding of customer and its results can be used to definition and decision-making ...
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Telecommunication Companies use data mining techniques to maintain good relationships with their existing customers and attract new customers and identifying profitable/unprofitable customers. Clustering leads to better understanding of customer and its results can be used to definition and decision-making for promotional schemes. In this study, we used the 999-customer purchase records in South Khorasan Telecommunication Company which has been collected during a year. The purpose of this study is to classify customers into several clusters. Since the clusters and the number of their members are determined, high-consumption users will be logged out of the system and high-value customers who are missed will be identified. In this research we divided our customers into five categories: loyal, potential, new, missed and high-consumption by using the Clementine software, developing the RFM model to the LRFM model and TwoStep and k_Means algorithms. Thus, this category will be a good benchmark for company's future decisions and we can make better decisions for each group of customers in the future.
B.3. Communication/Networking and Information Technology
Seyed M. Hosseinirad
Abstract
Due to the resource constraint and dynamic parameters, reducing energy consumption became the most important issues of wireless sensor networks topology design. All proposed hierarchy methods cluster a WSN in different cluster layers in one step of evolutionary algorithm usage with complicated parameters ...
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Due to the resource constraint and dynamic parameters, reducing energy consumption became the most important issues of wireless sensor networks topology design. All proposed hierarchy methods cluster a WSN in different cluster layers in one step of evolutionary algorithm usage with complicated parameters which may lead to reducing efficiency and performance. In fact, in WSNs topology, increasing a layer of cluster is a tradeoff between time complexity and energy efficiency. In this study, regarding the most important WSN’s design parameters, a novel dynamic multilayer hierarchy clustering approach using evolutionary algorithms for densely deployed WSN is proposed. Different evolutionary algorithms such as Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) are used to find an efficient evolutionary algorithm for implementation of the clustering proposed method. The obtained results demonstrate the PSO performance is more efficient compared with other algorithms to provide max network coverage, efficient cluster formation and network traffic reduction. The simulation results of multilayer WSN clustering design through PSO algorithm show that this novel approach reduces the energy communication significantly and increases lifetime of network up to 2.29 times with providing full network coverage (100%) till 350 rounds (56% of network lifetime) compared with WEEC and LEACH-ICA clsutering.
B.3. Communication/Networking and Information Technology
M. Zahedi; A. Arjomandzadeh
Abstract
Multi-part words in English language are hyphenated and hyphen is used to separate different parts. Persian language consists of multi-part words as well. Based on Persian morphology, half-space character is needed to separate parts of multi-part words where in many cases people incorrectly use space ...
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Multi-part words in English language are hyphenated and hyphen is used to separate different parts. Persian language consists of multi-part words as well. Based on Persian morphology, half-space character is needed to separate parts of multi-part words where in many cases people incorrectly use space character instead of half-space character. This common incorrectly use of space leads to some serious issues in Persian text processing and text readability. In order to cope with the issues, this work proposes a new model to correct spacing in multi-part words. The proposed method is based on statistical machine translation paradigm. In machine translation paradigm, text in source language is translated into a text in destination language on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The proposed method uses statistical machine translation techniques considering unedited multi-part words as a source language and the space-edited multi-part words as a destination language. The results show that the proposed method can edit and improve spacing correction process of Persian multi-part words with a statistically significant accuracy rate.
B.3. Communication/Networking and Information Technology
A. Ghaffari; S. Nobahary
Abstract
Wireless sensor networks (WSNs) consist of a large number of sensor nodes which are capable of sensing different environmental phenomena and sending the collected data to the base station or Sink. Since sensor nodes are made of cheap components and are deployed in remote and uncontrolled environments, ...
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Wireless sensor networks (WSNs) consist of a large number of sensor nodes which are capable of sensing different environmental phenomena and sending the collected data to the base station or Sink. Since sensor nodes are made of cheap components and are deployed in remote and uncontrolled environments, they are prone to failure; thus, maintaining a network with its proper functions even when undesired events occur is necessary which is called fault tolerance. Hence, fault management is essential in these networks. In this paper, a new method has been proposed with particular attention to fault tolerance and fault detection in WSN. The performance of the proposed method was simulated in MATLAB. The proposed method was based on majority vote which can detect permanently faulty sensor nodes with high detection. Accuracy and low false alarm rate were excluded them from the network. To investigate the efficiency of the new method, the researchers compared it with Chen, Lee, and hybrid algorithms. Simulation results indicated that the novel proposed method has better performance in parameters such as detection accuracy (DA) and a false alarm rate (FAR) even with a large set of faulty sensor nodes.
