Browsing by Author "Lemma, Dagmawi (PhD)"

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    An Application Classification Framework for Information Leakage Detection on Android Platform
    (Addis Ababa University, 2021-06-11) Kuma, Teklu; Lemma, Dagmawi (PhD)
    The growing of android based smartphone popularity is one of the reasons which is attracting the distribution of information stealing applications developed by attackers. As the latest android operating system versions are being updated to detect vulnerabilities, malware applications are shifting their patterns from looking malicious to looking like a good-ware application in order not to be detected easily. The use of machine learning is adapted in various information leakage detection techniques. Machine learning classifiers are widely used to model Android information leakage patterns based on their static features and dynamic behavior. In order to overcome the problem of information leaking applications detection, in this thesis we proposed a machine learning based information leakage detection mechanism. Our proposed system utilizes the extracted features of samples of good-ware and malware applications to train classification model. The system extracts requested permissions, vulnerable application program interface calls, system calls sent in 30 seconds and intents, and uses them as features in various machine learning classifiers to build classification model. After performing various comparative analysis among classification algorithms and performance validation, we achieved high classification accuracy of 99.8 % using our high performing classification model. Using the model as one of the major components, we have designed the classification framework to classify a random application as a leaker or non-leaker by extracting its feature at different state and add the extracted feature into the dataset of our classification model, since we have used incremental supervised learning. Using incremental supervised learning is helping our classification model to improve its performance from time to time as more applications are getting classified by our framework.
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    An Application Classification Framework for Information Leakage Detection on Android Platform
    (Addis Ababa University, 6/11/2021) Kuma, Teklu; Lemma, Dagmawi (PhD)
    The growing of android based smartphone popularity is one of the reasons which is attracting the distribution of information stealing applications developed by attackers. As the latest android operating system versions are being updated to detect vulnerabilities, malware applications are shifting their patterns from looking malicious to looking like a good-ware application in order not to be detected easily. The use of machine learning is adapted in various information leakage detection techniques. Machine learning classifiers are widely used to model Android information leakage patterns based on their static features and dynamic behavior. In order to overcome the problem of information leaking applications detection, in this thesis we proposed a machine learning based information leakage detection mechanism. Our proposed system utilizes the extracted features of samples of good-ware and malware applications to train classification model. The system extracts requested permissions, vulnerable application program interface calls, system calls sent in 30 seconds and intents, and uses them as features in various machine learning classifiers to build classification model. After performing various comparative analysis among classification algorithms and performance validation, we achieved high classification accuracy of 99.8 % using our high performing classification model. Using the model as one of the major components, we have designed the classification framework to classify a random application as a leaker or non-leaker by extracting its feature at different state and add the extracted feature into the dataset of our classification model, since we have used incremental supervised learning. Using incremental supervised learning is helping our classification model to improve its performance from time to time as more applications are getting classified by our framework.
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    Application Service Behavior Prediction Model Over Inter-cloud Environment
    (Addis Ababa University, 2021-03-22) Tilahun, Soreti; Lemma, Dagmawi (PhD)
    Cloud computing is a computing model that delivers different services to its users through Internet. These services include storage, databases, networking, analytics and software. Nevertheless, the delivery of these services towards the users will be difficult, if resources on the cloud are overloaded due to increased workloads. To overcome this situation an environment called Inter-cloud environment is designed. This environment is designed by forming cloud of clouds where each cloud would use the computational, storage or any kind of infrastructural resource of other clouds. However, the aggregation of diversified computing systems in the Inter-cloud environment poses difficult problems in effective delivery of application services and resource provisioning. These problems arise because of the magnitudes and uncertainties of Inter-cloud components (workload, compute servers, services). This research aims to study the Inter-cloud environment along with the behaviors of the application services and to propose a prediction model that assists the environment with knowledge to future resource surge of each service. The application service behaviors prediction model will be used to predict the CPU utilization of Inter-cloud services. The prediction model was developed by the most widely used machine learning method, Artificial Neural Network (ANN). Among the Artificial Network Algorithms; Multilayer Perceptron Neural Network (MLP) is used to approximate any linear or non-linear function. MLP method is employed to develop application service behavior prediction model. The Inter-cloud environment is simulated using FederatedCloudsim framework. Materna workload traces and Bitbrain workload traces are used to generate random resource workload traces from the FederatedCloudsim framework. The generated resource workload traces have been used to analyze the problem, to train and test the proposed prediction model. Four experiments were designed to build the application service behavior prediction models using generated resource workload data of Materna workload and Bitbrain workload traces from Inter-cloud environment. From the evaluation of the prediction model two factors that could affect the accuracy of the predicted results are pointed out. In this work the Coefficient of Determination and Mean Squared Error metrics are used to analyze the accuracy of the predictor model.
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    Application Service Behavior Prediction Model Over Inter-cloud Environment
    (Addis Ababa University, 3/22/2021) Tilahun, Soreti; Lemma, Dagmawi (PhD)
    Cloud computing is a computing model that delivers different services to its users through Internet. These services include storage, databases, networking, analytics and software. Nevertheless, the delivery of these services towards the users will be difficult, if resources on the cloud are overloaded due to increased workloads. To overcome this situation an environment called Inter-cloud environment is designed. This environment is designed by forming cloud of clouds where each cloud would use the computational, storage or any kind of infrastructural resource of other clouds. However, the aggregation of diversified computing systems in the Inter-cloud environment poses difficult problems in effective delivery of application services and resource provisioning. These problems arise because of the magnitudes and uncertainties of Inter-cloud components (workload, compute servers, services). This research aims to study the Inter-cloud environment along with the behaviors of the application services and to propose a prediction model that assists the environment with knowledge to future resource surge of each service. The application service behaviors prediction model will be used to predict the CPU utilization of Inter-cloud services. The prediction model was developed by the most widely used machine learning method, Artificial Neural Network (ANN). Among the Artificial Network Algorithms; Multilayer Perceptron Neural Network (MLP) is used to approximate any linear or non-linear function. MLP method is employed to develop application service behavior prediction model. The Inter-cloud environment is simulated using FederatedCloudsim framework. Materna workload traces and Bitbrain workload traces are used to generate random resource workload traces from the FederatedCloudsim framework. The generated resource workload traces have been used to analyze the problem, to train and test the proposed prediction model. Four experiments were designed to build the application service behavior prediction models using generated resource workload data of Materna workload and Bitbrain workload traces from Inter-cloud environment. From the evaluation of the prediction model two factors that could affect the accuracy of the predicted results are pointed out. In this work the Coefficient of Determination and Mean Squared Error metrics are used to analyze the accuracy of the predictor model.
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    Developing a Detection Method for Interconnect Bypass Frauds Using Fuzzy Logic
    (Addis Ababa University, 2021-07-12) Ayalew, Tadele; Lemma, Dagmawi (PhD)
    An interconnect bypass fraud is a telecom fraud that manipulates technological advancements and realized over the existing cellular networks with the intention of gaining illegal benefits. It results a degraded quality of service and financial loss. Existing prevention mechanisms collect call detail records to detect the fraud by analyzing various predefined behaviors. Hence, such systems play the role of intrusion detection by recording known behaviors. Thus, illegal accesses of a cellular network would be detected if the activity is similar with previously identified suspicious act, this further is a major concern of having a higher rate of false positive and/or false negative alarms. As interconnect bypass fraudsters are basically attacking the cellular network from a stationed location through a series of fixed network elements, a mobile subscriber, yet stationed is a suspect to be fraudster. In order detect new fraudulent act by studying the activity with respect to the natural set of mobile users (i.e., mobility) and mitigate the false negative and false positive rates, we have introduced a new detection method through a design science approach. We intend to trace mobile subscription but operates from fixed location. Our method gets inputs from home location register and monitors the mobility history of cellular network users by applying a fuzzy logic. We have tested the method by logging the location histories of 1037 randomly selected users. We have detected the fraudulent users with 1.92% up to 5.88% false positive rate and 0.88% up to 5.88% false negative rate.
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    Developing a Detection Method for Interconnect Bypass Frauds Using Fuzzy Logic
    (Addis Ababa University, 7/12/2021) Ayalew, Tadele; Lemma, Dagmawi (PhD)
    An interconnect bypass fraud is a telecom fraud that manipulates technological advancements and realized over the existing cellular networks with the intention of gaining illegal benefits. It results a degraded quality of service and financial loss. Existing prevention mechanisms collect call detail records to detect the fraud by analyzing various predefined behaviors. Hence, such systems play the role of intrusion detection by recording known behaviors. Thus, illegal accesses of a cellular network would be detected if the activity is similar with previously identified suspicious act, this further is a major concern of having a higher rate of false positive and/or false negative alarms. As interconnect bypass fraudsters are basically attacking the cellular network from a stationed location through a series of fixed network elements, a mobile subscriber, yet stationed is a suspect to be fraudster. In order detect new fraudulent act by studying the activity with respect to the natural set of mobile users (i.e., mobility) and mitigate the false negative and false positive rates, we have introduced a new detection method through a design science approach. We intend to trace mobile subscription but operates from fixed location. Our method gets inputs from home location register and monitors the mobility history of cellular network users by applying a fuzzy logic. We have tested the method by logging the location histories of 1037 randomly selected users. We have detected the fraudulent users with 1.92% up to 5.88% false positive rate and 0.88% up to 5.88% false negative rate.
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    Developing on-Site Solid Waste Segregation Framework Through Image Processing and Low Performance Iot Technologies
    (Addis Ababa University, 5/25/2020) Gezahegne, Melak; Lemma, Dagmawi (PhD)
    Internet of things (IoT) often uses devices that are resource scared having very limited processing power and storage capacity. However, some scenarios demand resource intensive technology such as camera technology that usually requires high computational resource due to the images captured by camera requires an image processing. One of the scenario is on-site solid waste segregation. To provide an alternative solution for such scenario, a framework is developed based on the technology of IoT, Image processing, and Deep Learning. The framework is presented based on four-layered IoT architecture and Faster-RCNN architecture. In the framework, the camera (used to capture real-time visual information) and actuator (to perform physical actuation) are placed at the perception layer. The captured video transmitted to through wired or wireless via transport layer to processing layer. In the processing layer, the real-time object detection facilitated based on Faster-RCNN objected detection architecture and represent the detected object to the lightweight information(title) after successful detection. The title which actually represent the object is communicated through serial communication to the Actuation service provider placed at the application layer. The Actuation service provider running on the low-performance IoT device at application layer and delivers actuation service to physical actuation (perception layer) based on the title it receives from processing layer. The Physical actuation makes a physical action using Actuator IoT device. For the scenario of on-site waste segregation, the actuator is attached to the dustbin hatch and performs opening and closing of the dustbin to realize the segregation based on the command it receives form Actuation service provider. The implementation of the thesis based on Tensor Flow object detection API framework (to develop real-time object detection) and the Arduino Uno IDE(C++) to develop the actuation service provider embedded system. In addition, the IoT device hardware circuit was designed and simulated using Proteus Simulator. During the evaluation, the concept is simulated and experimentally tested based on the scenario of on-site solid waste segregation. We have trained the object detection model to detect the class of plastic and glass objects. The result shows that real-time actuation can be successfully accomplished if the object detection model successfully trained to detect the corresponding object (visual information).
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    Developing on-Site Solid Waste Segregation Framework Through Image Processing and Low Performance Iot Technologies
    (Addis Ababa University, 2020-05-25) Gezahegne, Melak; Lemma, Dagmawi (PhD)
    Internet of things (IoT) often uses devices that are resource scared having very limited processing power and storage capacity. However, some scenarios demand resource intensive technology such as camera technology that usually requires high computational resource due to the images captured by camera requires an image processing. One of the scenario is on-site solid waste segregation. To provide an alternative solution for such scenario, a framework is developed based on the technology of IoT, Image processing, and Deep Learning. The framework is presented based on four-layered IoT architecture and Faster-RCNN architecture. In the framework, the camera (used to capture real-time visual information) and actuator (to perform physical actuation) are placed at the perception layer. The captured video transmitted to through wired or wireless via transport layer to processing layer. In the processing layer, the real-time object detection facilitated based on Faster-RCNN objected detection architecture and represent the detected object to the lightweight information(title) after successful detection. The title which actually represent the object is communicated through serial communication to the Actuation service provider placed at the application layer. The Actuation service provider running on the low-performance IoT device at application layer and delivers actuation service to physical actuation (perception layer) based on the title it receives from processing layer. The Physical actuation makes a physical action using Actuator IoT device. For the scenario of on-site waste segregation, the actuator is attached to the dustbin hatch and performs opening and closing of the dustbin to realize the segregation based on the command it receives form Actuation service provider. The implementation of the thesis based on Tensor Flow object detection API framework (to develop real-time object detection) and the Arduino Uno IDE(C++) to develop the actuation service provider embedded system. In addition, the IoT device hardware circuit was designed and simulated using Proteus Simulator. During the evaluation, the concept is simulated and experimentally tested based on the scenario of on-site solid waste segregation. We have trained the object detection model to detect the class of plastic and glass objects. The result shows that real-time actuation can be successfully accomplished if the object detection model successfully trained to detect the corresponding object (visual information).
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    A Framework for Verifying Paper Based Document Using Multiple QR Codes
    (Addis Ababa University, 2019-09-30) Abriham, Wondmagegn; Lemma, Dagmawi (PhD)
    With the growth in popularity of two dimensional (2D) barcodes such as Quick Response (QR) code, automatic verification of printed documents has become possible. 2D barcodes are types of barcode that can store data in two dimension. They have very large storage capacity compared to their one dimensional (1D) counterparts. In order to verify paper based document using a digital signature, the digital signature must be stored in 2D barcodes like QR code and printed on a document. This digital signature for paper based documents, which we called paper based digital signature (PBDS) in this thesis, not only holds the digital signature, but also a copy of the message from the document. Although QR code has large storage capacity, its storage capacity has a limit. This creates a problem as the size of the document to be signed increases, even with the help of compression algorithms to reduce the size of data to be stored in QR codes. In this thesis, a framework is developed to make the signing and verification of printed documents automatically verifiable. In the framework, two PBDS architectures are designed, BBDS-A and PBDS-B. These architectures use multiple QR codes for a single PBDS. PBDS-B is an architecture primarily designed to solve the problem of not being able to sign and verify paper based document with larger number of characters than QR code storage capacity. However, this architecture has one problem. The number of QR codes in a PBDS can be too many to track and challenging to verify documents. PBDS-A is also a multi-QR code PBDS designed to solve the problem observed in PBDS-B. In PBDS-A, the number of QR codes used is much smaller than PBDS-B. Design science research approach is used to conduct the thesis. An iterative framework development was followed between problem identification, solution design, evaluation and literature review to conduct the thesis. A test is conducted on 15 documents, five of which have less than 1000 characters, another five documents have number of characters between 1000 and 5000 characters, and the final five document have number of characters between 5000 and 10000. The evaluation confirms that multi-QR code PBDS have better capability of verifying large documents. In addition, the test shows that PBDS-A is more accurate during verification and much more easily verifiable than PBDS-B.
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    A Framework for Verifying Paper Based Document Using Multiple QR Codes
    (Addis Ababa University, 9/30/2019) Abriham, Wondmagegn; Lemma, Dagmawi (PhD)
    With the growth in popularity of two dimensional (2D) barcodes such as Quick Response (QR) code, automatic verification of printed documents has become possible. 2D barcodes are types of barcode that can store data in two dimension. They have very large storage capacity compared to their one dimensional (1D) counterparts. In order to verify paper based document using a digital signature, the digital signature must be stored in 2D barcodes like QR code and printed on a document. This digital signature for paper based documents, which we called paper based digital signature (PBDS) in this thesis, not only holds the digital signature, but also a copy of the message from the document. Although QR code has large storage capacity, its storage capacity has a limit. This creates a problem as the size of the document to be signed increases, even with the help of compression algorithms to reduce the size of data to be stored in QR codes. In this thesis, a framework is developed to make the signing and verification of printed documents automatically verifiable. In the framework, two PBDS architectures are designed, BBDS-A and PBDS-B. These architectures use multiple QR codes for a single PBDS. PBDS-B is an architecture primarily designed to solve the problem of not being able to sign and verify paper based document with larger number of characters than QR code storage capacity. However, this architecture has one problem. The number of QR codes in a PBDS can be too many to track and challenging to verify documents. PBDS-A is also a multi-QR code PBDS designed to solve the problem observed in PBDS-B. In PBDS-A, the number of QR codes used is much smaller than PBDS-B. Design science research approach is used to conduct the thesis. An iterative framework development was followed between problem identification, solution design, evaluation and literature review to conduct the thesis. A test is conducted on 15 documents, five of which have less than 1000 characters, another five documents have number of characters between 1000 and 5000 characters, and the final five document have number of characters between 5000 and 10000. The evaluation confirms that multi-QR code PBDS have better capability of verifying large documents. In addition, the test shows that PBDS-A is more accurate during verification and much more easily verifiable than PBDS-B.
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    Improving the Performance of Proof of Work-Based Bitcoin Mining Using CUDA
    (Addis Ababa University, 2021-02-23) Mehammed, Seid; Lemma, Dagmawi (PhD)
    The most dominant blockchain consensus algorithm is Proof of Work (POW). It is an algorithm, which scales up the bitcoin transaction well globally, by competition a cryptographic hash function. This process is named mining. POW-based bitcoin mining is a well-known problem of computational and memory-intensive algorithms. On the other hand, the high-threaded CUDA architecture has become with enhanced performance for a various range of computation and memory-intensive applications. Thus, the feature of a massive number of software threads with low overhead context switch provides high computational throughput and hides the memory access latencies. However, it is not effective enough for all applications because of two challenges that directly affect performance such as scheduling new threads and the overhead to start a new kernel on the CUDA. The existing work tried to model the performance of POW-based mining from various aspects. However, no model considers all of these factors came together at the same time. The main contribution of the thesis is a combination of the POW-based bitcoin mining algorithm with a focus on the higher-level analysis of algorithm performance and lower-level details about runtime configuration (thread per block) and scheduling on CUDA. To demonstrate the feasibility of our method, the models are validated through bitcoin block-header data from implementations of POW-based bitcoin mining using CUDA. We evaluated the performance of the models across a large variety of parameters and data values. The results indicate that the model can be effectively used on various optimization techniques. It was able to get a performance, which is almost 4 times when compared to the baseline serial algorithm of POW-based mining implementation.
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    Improving the Performance of Proof of Work-Based Bitcoin Mining Using CUDA
    (Addis Ababa University, 2/23/2021) Mehammed, Seid; Lemma, Dagmawi (PhD)
    The most dominant blockchain consensus algorithm is Proof of Work (POW). It is an algorithm, which scales up the bitcoin transaction well globally, by competition a cryptographic hash function. This process is named mining. POW-based bitcoin mining is a well-known problem of computational and memory-intensive algorithms. On the other hand, the high-threaded CUDA architecture has become with enhanced performance for a various range of computation and memory-intensive applications. Thus, the feature of a massive number of software threads with low overhead context switch provides high computational throughput and hides the memory access latencies. However, it is not effective enough for all applications because of two challenges that directly affect performance such as scheduling new threads and the overhead to start a new kernel on the CUDA. The existing work tried to model the performance of POW-based mining from various aspects. However, no model considers all of these factors came together at the same time. The main contribution of the thesis is a combination of the POW-based bitcoin mining algorithm with a focus on the higher-level analysis of algorithm performance and lower-level details about runtime configuration (thread per block) and scheduling on CUDA. To demonstrate the feasibility of our method, the models are validated through bitcoin block-header data from implementations of POW-based bitcoin mining using CUDA. We evaluated the performance of the models across a large variety of parameters and data values. The results indicate that the model can be effectively used on various optimization techniques. It was able to get a performance, which is almost 4 times when compared to the baseline serial algorithm of POW-based mining implementation.
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    Lightweight Neural Networks for Context Aware Autonomous Embedded System Development
    (Addis Ababa University, 2020-02-02) Mosisa, Abdi; Lemma, Dagmawi (PhD)
    An embedded system is a microcontroller or microprocessor-based system which is designed to perform a specific task by collecting, processing and communicating information. While focusing on specific task, it is also desired to make such system for better and efficient result. In due course, one of the challenges is contextualizing the collected information to predict the output and making smart decision to produce the output. The learning system that can contextualize the surrounding environment should have a capability of automatic mechanism of inferring information like humans do. This calls for neural networks that provide an embedded intelligence for smart systems to make decisions at machine speed. The main challenge to develop such system is the constraints in memory size, computational power and other characteristics of embedded system that can significantly restrict developers from implementing learning algorithms to solve the problem. This thesis presents lightweight neural networks so as to show a method for implementing context-aware embedded system in environment where there is resource limitation. A testbed is setup for collecting the data, training and evaluation. Arduino board is investigated as a main experimental device for the proposed algorithms. The algorithms are simulated using C on Arduino. A good result was obtained after deploying the algorithm and knowledgebase on arduino board for sensor reading.
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    Lightweight Neural Networks for Context Aware Autonomous Embedded System Development
    (Addis Ababa University, 2/2/2020) Mosisa, Abdi; Lemma, Dagmawi (PhD)
    An embedded system is a microcontroller or microprocessor-based system which is designed to perform a specific task by collecting, processing and communicating information. While focusing on specific task, it is also desired to make such system for better and efficient result. In due course, one of the challenges is contextualizing the collected information to predict the output and making smart decision to produce the output. The learning system that can contextualize the surrounding environment should have a capability of automatic mechanism of inferring information like humans do. This calls for neural networks that provide an embedded intelligence for smart systems to make decisions at machine speed. The main challenge to develop such system is the constraints in memory size, computational power and other characteristics of embedded system that can significantly restrict developers from implementing learning algorithms to solve the problem. This thesis presents lightweight neural networks so as to show a method for implementing context-aware embedded system in environment where there is resource limitation. A testbed is setup for collecting the data, training and evaluation. Arduino board is investigated as a main experimental device for the proposed algorithms. The algorithms are simulated using C on Arduino. A good result was obtained after deploying the algorithm and knowledgebase on arduino board for sensor reading.
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    RFID Security Through Dynamic Tag Content Management
    (Addis Ababa University, 2/2/2020) Kasahun, Bemenet; Lemma, Dagmawi (PhD)
    Radio frequency identification (RFID) tag exchanges data with an RFID reader through radio waves. These tags can be attached to almost any object, such as baggage‟s, containers, construction materials, laundry and bottles. It can also be attached to animals, humans and vehicles. It is seen as a means to enhance efficiency and introduce new functionality in products such as intelligent fridges or washing machines, to query their contents. However, concern has arisen about the possibility of using RFID technology for tracking and profiling individual people. Privacy, integrity, data security and civil rights concerns are expressed and may lead to the failure of RFID technology to realize its promise. Cryptographic solutions may be a consideration. However, standard cryptographic primitives (hash functions, message authentication codes, block/stream ciphers, etc.) are quite demanding in terms of circuit size, power consumption and memory size, so they make costly solutions for RFID tags. We analyze the security hole and present these concerns and find out the problem as static tag_id identification and no access control. As we see none of RFID tags employ read passwords or other read access control. Because the tag content on the RFID tag never changes until next encoding, the ability to read will enable several security risks. First, the adversary may determine which object owns the tag and infer the origin of the object carrying the tag. Second, static identifier can be used both to track and hotlist tagged objects easily. The main security risk of the static tag content is the leakage of content and cloning the tag. Because of the static properties, RFID tag content always remains unchanged in the entire system, and an attacker can conducts multiple actions on the target tag. Tag that stores static information is vulnerable to attacks such as cloning. So our proposed solution comes with the idea of using dynamic tag content to the tag which is making the tag_id dynamic by changing it every time the tag is read/scanned. The tags store a dynamic number, called tag_id. The back-end system issues these numbers and keeps track of which number is written on which tag to prevent cloning attack. This work makes use of the tag‟s rewritable memory for changing tag content after every scan. Finally, we simulate our work using Arduino and Proteus simulation tools.
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    RFID Security Through Dynamic Tag Content Management
    (Addis Ababa University, 2020-02-02) Kasahun, Bemenet; Lemma, Dagmawi (PhD)
    Radio frequency identification (RFID) tag exchanges data with an RFID reader through radio waves. These tags can be attached to almost any object, such as baggage‟s, containers, construction materials, laundry and bottles. It can also be attached to animals, humans and vehicles. It is seen as a means to enhance efficiency and introduce new functionality in products such as intelligent fridges or washing machines, to query their contents. However, concern has arisen about the possibility of using RFID technology for tracking and profiling individual people. Privacy, integrity, data security and civil rights concerns are expressed and may lead to the failure of RFID technology to realize its promise. Cryptographic solutions may be a consideration. However, standard cryptographic primitives (hash functions, message authentication codes, block/stream ciphers, etc.) are quite demanding in terms of circuit size, power consumption and memory size, so they make costly solutions for RFID tags. We analyze the security hole and present these concerns and find out the problem as static tag_id identification and no access control. As we see none of RFID tags employ read passwords or other read access control. Because the tag content on the RFID tag never changes until next encoding, the ability to read will enable several security risks. First, the adversary may determine which object owns the tag and infer the origin of the object carrying the tag. Second, static identifier can be used both to track and hotlist tagged objects easily. The main security risk of the static tag content is the leakage of content and cloning the tag. Because of the static properties, RFID tag content always remains unchanged in the entire system, and an attacker can conducts multiple actions on the target tag. Tag that stores static information is vulnerable to attacks such as cloning. So our proposed solution comes with the idea of using dynamic tag content to the tag which is making the tag_id dynamic by changing it every time the tag is read/scanned. The tags store a dynamic number, called tag_id. The back-end system issues these numbers and keeps track of which number is written on which tag to prevent cloning attack. This work makes use of the tag‟s rewritable memory for changing tag content after every scan. Finally, we simulate our work using Arduino and Proteus simulation tools.