| 1. |
- Beitollahi, Hakem, et al.
(författare)
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Application Layer DDoS Attack Detection Using Cuckoo Search Algorithm-Trained Radial Basis Function
- 2022
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Ingår i: IEEE Access. - Piscataway, NJ : IEEE. - 2169-3536. ; 10, s. 63844-63854
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Tidskriftsartikel (refereegranskat)abstract
- In an application-layer distributed denial of service (App-DDoS) attack, zombie computers bring down the victim server with valid requests. Intrusion detection systems (IDS) cannot identify these requests since they have legal forms of standard TCP connections. Researchers have suggested several techniques for detecting App-DDoS traffic. There is, however, no clear distinction between legitimate and attack traffic. In this paper, we go a step further and propose a Machine Learning (ML) solution by combining the Radial Basis Function (RBF) neural network with the cuckoo search algorithm to detect App-DDoS traffic. We begin by collecting training data and cleaning them, then applying data normalizing and finding an optimal subset of features using the Genetic Algorithm (GA). Next, an RBF neural network is trained by the optimal subset of features and the optimizer algorithm of cuckoo search. Finally, we compare our proposed technique to the well-known k-nearest neighbor (k-NN), Bootstrap Aggregation (Bagging), Support Vector Machine (SVM), Multi-layer Perceptron) MLP, and (Recurrent Neural Network) RNN methods. Our technique outperforms previous standard and well-known ML techniques as it has the lowest error rate according to error metrics. Moreover, according to standard performance metrics, the results of the experiments demonstrate that our proposed technique detects App-DDoS traffic more accurately than previous techniques. © 2013 IEEE.
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| 2. |
- Norollah, Amin, et al.
(författare)
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A security-aware hardware scheduler for modern multi-core systems with hard real-time constraints
- 2022
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Ingår i: Microprocessors and microsystems. - Amsterdam : Elsevier. - 0141-9331 .- 1872-9436. ; 95
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we propose an online security-aware hardware scheduler, the so-called Secure And Fast hardware Scheduler (SAFAS), for real-time task scheduling in multi-core systems in the presence of schedule-based side-channel attacks. To avoid such attacks and ensure that all tasks meet their deadlines, SAFAS schedules critical tasks and their replicas using a hardware-based strict Least Slack Time first (LST) algorithm independently while it independently schedules the non-critical tasks using a hardware-based EDF algorithm. SAFAS enhances the system performance and reduces the chance of side-channel attacks due to the different processing cores allocated to each task in each scheduling interval. The hardware scheduler operates independently and in parallel with the multi-core system and hides the scheduling characteristics from adversaries. The software-based Earliest Deadline First (EDF) algorithm is also used for schedulability tests and feasibility analysis of hard real-time periodic tasks to maximize the number of tasks scheduled successfully in the multi-core system. SAFAS has been synthesized and simulated on a Xilinx Vivado 2018.2 and implemented on a Spartan-7 FPGA chip. Our experimental results indicate that SAFAS increases the performance of the system by 4.8 times as compared to previous state-of-the-art hardware schedulers while guaranteeing that all critical tasks and their replicas meet their deadlines. © 2022 Elsevier B.V.
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| 3. |
- Norollah, Amin, et al.
(författare)
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Protecting Security-Critical Real-Time Systems against Fault Attacks in Many-Core Platforms
- 2022
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Ingår i: 2022 CPSSI 4th International Symposium on Real-Time and Embedded Systems and Technologies (RTEST). - : IEEE. - 9781665499101 - 9781665499118
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Konferensbidrag (refereegranskat)abstract
- Single-core platforms have been widely used for Many security-critical real-time systems. However, the ever-increasing high-performance requirements demanded by various industries and the advent of serious bottlenecks again increasing the performance of single-core platforms have necessitated the employment of many-core platforms in the design of such systems. This design shift from single to many-core platforms has been accompanied by security issues and has produced emerging security challenges. Fault injection attacks are one of the primary attacks that are used to infiltrate the tasks to reduce the system performance or cause system failures. In this paper, an online security-aware real-time hardware scheduler is proposed and used to avoid fault attacks using the task replication method. In the proposed real-time system, critical tasks and their replicas are scheduled with Least Slack Time first (LST) algorithm independently in the hardware under real-time constraints. Our synthesis and simulation results using Xilinx Vivado 2018.2 indicates that the proposed scheduler guarantees that all critical tasks and their replicas meet their deadlines. The results also show that our scheduler reduces the chance of a successful Fault attack and loss of the final result in critical tasks. © 2022 IEEE.
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| 4. |
- Salahvarzi, Arash, et al.
(författare)
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WiSE : When Learning Assists Resolving STT-MRAM Efficiency Challenges
- 2023
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Ingår i: IEEE Transactions on Emerging Topics in Computing. - Piscataway, NJ : IEEE. - 2168-6750. ; 11:1, s. 43-55
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Tidskriftsartikel (refereegranskat)abstract
- Spin Transfer Torque Magnetic RAM (STT-MRAM) is one of the most promising on-chip technologies, which delivers high density, non-volatility, and near-zero leakage power. However, STT-MRAM suffers from three reliability issues, namely, read disturbance, write failure, and retention failure, that present significant challenges to its use as a reliable on-chip memory. All of these three reliability challenges become even more threatening with any increase in STT-MRAM cell temperature. Write operations are regarded as the main source of heat generation and temperature increase in STT-MRAM on-chip memories. This paper first presents experiments to show how the heat generated by consecutive writes affects the reliability of an STT-MRAM on-chip cache. Then, it proposes the WiSE framework, an approach to reduce the STT-MRAM-based cache memory temperature and improve its reliability. WiSE utilizes the Reinforcement Learning (RL) technique to detect high-density write operation patterns in STT-MRAM cache. To manage the write operations across the STT-MRAM caches, WiSE introduces a new temperature-aware replacement policy. The simulation results show that while WiSE imposes only about 1% performance overhead, it improves retention failure rate, read disturbance rate and write failure rate by 64%, 57%, and 47%, respectively, compared to Least Recently Used (LRU) replacement policy. (c) IEEE
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| 5. |
- Sharif, Dyari Mohammed, et al.
(författare)
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Detection of Application-Layer DDoS Attacks Produced by Various Freely Accessible Toolkits Using Machine Learning
- 2023
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Ingår i: IEEE Access. - Piscataway, NJ : IEEE. - 2169-3536. ; 11, s. 51810-51819
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Tidskriftsartikel (refereegranskat)abstract
- Distributed Denial of Service (DDoS) attacks are a growing threat to online services, and various methods have been developed to detect them. However, past research has mainly focused on identifying attack patterns and types, without specifically addressing the role of freely available DDoS attack tools in the escalation of these attacks. This study aims to fill this gap by investigating the impact of the easy availability of DDoS attack tools on the frequency and severity of attacks. In this paper, a machine learning solution to detect DDoS attacks is proposed, which employs a feature selection technique to enhance its speed and efficiency, resulting in a substantial reduction in the feature subset. The provided evaluation metrics demonstrate that the model has a high accuracy level of 99.9%, a precision score of 96%, a recall score of 98%, and an F1 score of 97%. Moreover, the examination found that by utilizing a deliberate approach for feature selection, our model's efficacy was massively raised. © 2013 IEEE.
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