DyProSD: a dynamic protocol specific defense for high-rate DDoS flooding attacks

Boro, Debojit; Bhattacharyya, Dhruba K.

doi:10.1007/s00542-016-2978-0

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…The PSO-1NN, PSO-5NN, and PSO-7NN were constructed to report the best achieved results after comparing their performance values in both software product and process experiments. In terms of PSO, we employed the default values of PSO as suggested in the studies ( He, Ma & Zhang, 2016 ; Boro & Bhattacharyya, 2017 ), the parameters setting of the PSO can be found in Table 1 .…”

Section: Experiments and Resultsmentioning

confidence: 99%

Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects

Al‐Ahmad

Al-Zoubi

Kabir

et al. 2022

PeerJ Computer Science

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Software engineering is one of the most significant areas, which extensively used in educational and industrial fields. Software engineering education plays an essential role in keeping students up to date with software technologies, products, and processes that are commonly applied in the software industry. The software development project is one of the most important parts of the software engineering course, because it covers the practical side of the course. This type of project helps strengthening students’ skills to collaborate in a team spirit to work on software projects. Software project involves the composition of software product and process parts. Software product part represents software deliverables at each phase of Software Development Life Cycle (SDLC) while software process part captures team activities and behaviors during SDLC. The low-expectation teams face challenges during different stages of software project. Consequently, predicting performance of such teams is one of the most important tasks for learning process in software engineering education. The early prediction of performance for low-expectation teams would help instructors to address difficulties and challenges related to such teams at earliest possible phases of software project to avoid project failure. Several studies attempted to early predict the performance for low-expectation teams at different phases of SDLC. This study introduces swarm intelligence -based model which essentially aims to improve the prediction performance for low-expectation teams at earliest possible phases of SDLC by implementing Particle Swarm Optimization-K Nearest Neighbours (PSO-KNN), and it attempts to reduce the number of selected software product and process features to reach higher accuracy with identifying less than 40 relevant features. Experiments were conducted on the Software Engineering Team Assessment and Prediction (SETAP) project dataset. The proposed model was compared with the related studies and the state-of-the-art Machine Learning (ML) classifiers: Sequential Minimal Optimization (SMO), Simple Linear Regression (SLR), Naïve Bayes (NB), Multilayer Perceptron (MLP), standard KNN, and J48. The proposed model provides superior results compared to the traditional ML classifiers and state-of-the-art studies in the investigated phases of software product and process development.

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Section: Experiments and Resultsmentioning

confidence: 99%

Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects

Al‐Ahmad

Al-Zoubi

Kabir

et al. 2022

PeerJ Computer Science

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“…The magnitudes of DDoS attacks have increased over the last decade. For example, in 2012, a botnet-based DDoS attack flooded a group of US banks with up to 75 Gbps of malicious traffic [10], while in 2013, a nonprofit organization named Spamhaus suffered a massive DDoS attack involving 300 Gbps of traffic [11]. In 2014, an unnamed Internet service provider was attacked by a network time protocol that generated traffic of up to 400 Gbps, which led to it becoming inaccessible to clients [12].…”

Section: Background and Motivationmentioning

confidence: 99%

“…Fig. 2 demonstrates the increasing magnitudes of DDoS attacks in recent years [10]- [15], [119]. DDoS attacks have a significant impact on businesses due to the associated operating and financial costs [16].…”

Section: Background and Motivationmentioning

confidence: 99%

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Aljuhani

2021

IEEE Access

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“…Entropy variations. To detect slow request or response attacks, users can find the difference in the entropy values (flow-header features and behavioral features) of attack packets at the source or destination IP address and their corresponding ports [14]. In [15], short term entropy was employed for early attack detection and long term entropy for attack classification.…”

Section: Ddos Attack Defense Techniquesmentioning

confidence: 99%

Edge2Guard: Botnet Attacks Detecting Offline Models for Resource-Constrained IoT Devices

Sudharsan

Sundaram

Patel

et al. 2021

2021 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Work

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In today's IoT smart environments, dozens of MCUbased connected device types exist such as HVAC controllers, smart meters, smoke detectors, etc. The security conditions for these essential IoT devices remain unsatisfactory since: (i) many of them are built with cost as the driving design tenet, resulting in poor configurations and open design; (ii) their memory and computational resource constraints make it highly challenging to implement practical attack protection mechanisms; and (iii) currently, manufacturers use simplified light protocol versions to save memory for extra features (to boost sales). When such issues and vulnerabilities are exploited, devices can be compromised and converted into bots whereby severe DDoS attacks can be launched by a botmaster. Such tiny devices are safe only when connected to networks with defense mechanisms installed in their networking devices like routers and switches, which might not be present everywhere, e.g. on public/free Wi-Fi networks. To safeguard tiny IoT devices from cyberattacks, we provide resource-friendly standalone attack detection models termed Edge2Guard (E2G) that enable MCU-based IoT devices to instantly detect IoT attacks without depending on networks or any external protection mechanisms. During evaluation, our top-performing E2G models detected and classified ten types of Mirai and Bashlite malware with close to 100% detection rates.

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DyProSD: a dynamic protocol specific defense for high-rate DDoS flooding attacks

Cited by 13 publications

References 15 publications

Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects

Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Edge2Guard: Botnet Attacks Detecting Offline Models for Resource-Constrained IoT Devices

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