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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Simulation Analysis of SYN Flood and HTTP Flood Attacks on Cloud
Infrastructure Integrity
Ng Yen Phing, Caleb Chong Senn Yang, Low Choon Keat, Phoon Gar Chi
Department of Information and Communication Technology, Tunku Abdul Rahman University of
Management and Technology, Malaysia
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000680
Received: 28 October 2025; Accepted: 03 November 2025; Published: 20 November 2025
ABSTRACT
This paper presents a comparative simulation study of SYN Flood and HTTP Flood Distributed Denial-of-
Service (DDoS) attacks in cloud environments using CloudSim. A modular testbed was configured with attacker
VMs generating cloudlets and victim VMs handling legitimate workloads, under realistic network constraints.
Experimental results revealed distinct attack signatures: SYN Flood produced high volumes of half-open
connections, while HTTP Flood exhausted CPU, memory, and bandwidth due to resource-intensive request
processing. SYN Flood achieved a 35% packet loss rate with 10,000 cloudlets, while HTTP Flood produced a
40% loss rate with only 3,000 requests, demonstrating that application-layer attacks, though lower in volume,
cause more severe degradation. These findings highlight the importance of nuanced defense strategies tailored
to each attack type, beyond volumetric thresholds alone.
Keywords—CloudSim; DDoS; SYN Flood; HTTP Flood; Simulation
INTRODUCTION
Cloud computing offers elasticity, scalability, and multi-tenancy but remains highly vulnerable to Distributed
Denial-of-Service (DDoS) attacks. Among these, SYN Floods overwhelm TCP state tables at the protocol layer,
while HTTP Floods mimic legitimate user traffic at the application layer, making them difficult to detect. Current
research lacks standardized simulation frameworks for systematically comparing the effectiveness of different
DDoS attack types on cloud infrastructure, particularly in quantifying resource consumption patterns and service
degradation metrics. This study addresses this gap by employing CloudSim as a reproducible simulation
environment to quantify the differential effects of SYN Flood and HTTP Flood attacks on virtualized datacenter
resources, with emphasis on packet loss, downtime, and bandwidth consumption. Our primary contributions
include: (1) development of a modular CloudSim-based framework with configurable attack generators and
metrics collectors for systematic DDoS evaluation; (2) empirical demonstration that HTTP Floods achieve higher
loss rates (40.0%) despite using fewer cloudlets compared to SYN Floods (35.0% with 10,000 cloudlets); and
(3) quantitative analysis revealing HTTP Floods consume 93× more bandwidth resources (75.67 Mbps vs 0.81
Mbps), providing critical insights for cloud security resource allocation and defense mechanism design.
LITERATURE REVIEW
Distributed Denial-of-Service attacks have evolved significantly since their emergence in the late 1990s, with
cloud computing environments presenting unique challenges and vulnerabilities [1]. The classification of DDoS
attacks into volumetric, protocol, and application-layer categories provides a framework for understanding their
distinct impact mechanisms [2].
D DoS Attack Classification and Evolution
Modern DDoS attacks are categorized into three primary types: volumetric attacks that saturate network
bandwidth, protocol attacks that exploit weaknesses in network protocols, and application-layer attacks that
target specific services [3]. SYN Flood attacks exemplify protocol-level exploitation by overwhelming TCP
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connection state tables through the creation of numerous half-open connections, effectively exhausting server
resources before legitimate connections can be established [4].
HTTP Flood attacks represent a more sophisticated application-layer threat that mimics legitimate user behavior
by generating seemingly valid HTTP requests to web servers [5]. These attacks are particularly challenging to
detect because they operate within normal protocol parameters while consuming disproportionate server
resources through complex request processing [6].
B. Cloud-Specific Vulnerabilities
Cloud environments introduce unique attack vectors that traditional DDoS research has not adequately
addressed. Economic Denial-of-Sustainability (EDoS) attacks exploit auto-scaling mechanisms to inflate
operational costs, often triggering resource allocation before volumetric defenses recognize anomalous traffic
patterns [7]. The multi-tenancy characteristic of cloud computing compounds these risks through collateral
damage effects, where attacks against one tenant can degrade performance for neighboring workloads sharing
physical infrastructure resources [8].
C. Simulation-Based Research Methodologies
CloudSim has emerged as the preferred framework for DDoS research due to its extensibility and reproducibility
in modeling cloud environments. Karthik and Shah [9] demonstrated early applications of CloudSim for
modeling flooding attacks on virtual machines, establishing baseline methodologies for attack simulation in
controlled environments. Their work revealed significant service disruption through simulated flooding scenarios
but lacked comparative analysis between different attack types.
Ali et al. [10] advanced this research direction by implementing multi-layer detection frameworks incorporating
fuzzy logic and machine learning classifiers, achieving detection accuracy rates of 98-99% in simulated
environments. Their approach used 10 VMs for legitimate traffic generation and 40 cloudlet tasks representing
malicious attackers, providing a foundation for understanding attack-defense dynamics in cloud simulations.
Recent developments include reinforcement learning-based defense mechanisms, with Basulaim and AlAmoudi
[11] proposing a three-stage system combining multi-factor authentication, fuzzy-VIKOR detection algorithms,
and VM isolation strategies. Their CloudSim-Plus implementation demonstrated improved Quality of Service
metrics under HTTP flood conditions, though comparative analysis with protocol-layer attacks remained limited.
Sreeram and Vuppala [12] contributed to HTTP flood detection research by applying machine learning metrics
and bio-inspired algorithms, achieving high accuracy in identifying application-layer attacks. However, their
work focused primarily on detection mechanisms rather than quantifying resource consumption patterns across
different attack types.
D. Research Gap Identification
Despite significant advances in individual attack modeling and detection mechanisms, the literature reveals
insufficient comparative analysis of protocol-layer versus application-layer attacks in cloud environments.
Existing studies predominantly focus on detection and mitigation strategies rather than establishing standardized
metrics for quantifying attack effectiveness and resource impact patterns. This gap limits the development of
optimal defense resource allocation strategies and hinders the systematic evaluation of cloud infrastructure
resilience against diverse DDoS attack vectors.
METHODOLOGY
A. Simulation Environment Configuration
The experimental framework was implemented using CloudSim 3.x and CloudSim 5.0 (main library) to leverage
both stable functionality and enhanced network modeling capabilities. The simulation environment comprised
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two distinct logical datacenters to ensure clear separation between attack generation and victim infrastructure:
Attacker Datacenter: Hosted multiple VMs configured to generate malicious cloudlet streams representing SYN
Flood and HTTP Flood attack patterns. Each attacker VM was programmed with specific attack characteristics
including cloudlet generation rates, payload sizes, and connection duration parameters.
Victim Datacenter: Contained the target web-server VM alongside legitimate workload VMs to simulate realistic
cloud service scenarios. The victim infrastructure was configured with resource constraints representative of
typical IaaS deployments.
B. Network Parameter Configuration
Critical network parameters were standardized across all simulation runs to ensure reproducible results:
1. Victim datacenter bandwidth: 10 Mbps (reflecting typical small-to-medium cloud service allocations)
2. Network latency: 1 ms (representing optimized datacenter network conditions)
3. Packet transmission protocols: TCP for SYN Flood simulations, HTTP/TCP for application-layer attacks
C. Cloudlet Design and Traffic Modeling
Cloudlets served as abstractions for network requests and application transactions, with distinct configurations
for each attack type:
SYN Flood Cloudlets: Designed to represent TCP connection attempts with minimal processing requirements
but high-volume characteristics. Each cloudlet consumed minimal CPU cycles while maintaining connection
state information.
HTTP Flood Cloudlets: Configured to simulate resource-intensive web requests requiring substantial CPU,
memory, and I/O processing. These cloudlets incorporated realistic web transaction characteristics including file
transfers and database queries.
Legitimate Traffic Cloudlets: Generated baseline workload patterns to establish performance benchmarks and
simulate realistic mixed-traffic scenarios during attack periods.
D. Metrics Collection Framework
A custom metrics collection module was developed to capture comprehensive performance data throughout
simulation execution:
Real-time Event Logging: Captured cloudlet lifecycle events including creation, scheduling, execution start,
completion, and failure timestamps.
Resource Utilization Monitoring: Tracked CPU, memory, bandwidth, and storage utilization across all VMs at
predetermined intervals.
Drop Event Detection: Identified and categorized cloudlet failures based on resource exhaustion, timeout
conditions, or explicit rejection by the CloudSim broker.
E. Experimental Design and Parameters
The experimental methodology employed controlled parameter variation to assess attack effectiveness across
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different scenarios:
Attack Intensity Scaling: SYN Flood simulations varied from 1,000 to 10,000 cloudlets, while HTTP Flood tests
ranged from 500 to 3,000 cloudlets to reflect realistic attack volume differences.
Resource Allocation Testing: VM specifications were systematically varied to determine optimal configurations
for different attack types and to assess scalability limitations.
Temporal Analysis: Extended simulation runs captured long-term effects of sustained attacks on system stability
and recovery patterns.
F. Statistical Validation
Multiple simulation runs were conducted for each parameter configuration to ensure statistical significance of
results. Standard deviation calculations and confidence interval analysis validated the reproducibility of observed
performance degradation patterns.
RESULTS
A. Attack Attributes
TABLE I
Attribute
SYN Flood
HTTP Flood
Attack Type
Volume-based (TCP)
Application-layer
Cloudlets
10,000
3,000
Cloudlet Length (MI)
200
100
File Size (MB)
1
50
Output Size (MB)
1
100
Hybrid Attack : 5,000 SYN cloudlets + 1,500 HTTP cloudlets
HTTP Flood requests are fewer in number but more resource-intensive, requiring larger file sizes and outputs.
B. VM Specification
TABLE II
Attribute
SYN Flood
HTTP Flood
Hybrid Attack
VM MIPS
1,000
1,500
1300
VM RAM (MB)
512
1,024
768
VM Bandwidth (Mb/s)
100
200
150
HTTP Flood required significantly higher VM resources to process complex application traffic.
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C. Host Specification
TABLE III
Attribute
SYN Flood
HTTP Flood
Hybrid Attack
Host RAM (MB)
2,048
4,096
3072
Total MIPS
2,000
6,000
4000
Bandwidth (Mb/s)
10,000
15,000
12,500
E. Attack Performance Characteristics
Fig. 1
TABLE IV
Attribute
SYN Flood
HTTP Flood
Hybrid Attack
VM MIPS
1,000
1,500
1300
VM RAM (MB)
512
1,024
768
VM Bandwidth
100
200
150
(Mb/s)
HTTP Flood required significantly higher VM resources to process complex application traffic.
TABLE V
Attribute
SYN Flood
HTTP Flood
Hybrid Attack
Host RAM (MB)
2,048
4,096
3072
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Total MIPS
2,000
6,000
4000
Bandwidth (Mb/s)
10,000
15,000
12,500
C. Attack Performance Characteristics
Fig. 1
TABLE VI
Characteristic
SYN Flood
HTTP Flood
Hybrid Attack
Primary Impact
Connection table
exhaustion
CPU/Memory
exhaustion
Multi-layer
exhaustion
Bandwidth Usage (Mbps)
0.81
75.67
87.5
Processing Load (pps)
110.99
85.65
98.5
Connection Duration (s)
45.18
50.21
52.8
Observation: Hybrid attacks exceed both individual attack types in bandwidth consumption (87.5 Mbps) and
processing load (98.5 pps), demonstrating compound effects that simultaneously exhaust network capacity and
computational resources. While HTTP Flood remains the dominant bandwidth consumer (75.67 Mbps), the
addition of SYN traffic increases total bandwidth by 15.6% and processing overhead by 15%, creating a
multilayer assault that overwhelms defenses optimized for single attack vectors.
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F. Loss Rate Analysis
Fig. 2
Formula [13]:
Lr(t, s) - Loss Rate at time t and scenario s
●Represents the percentage of packets/cloudlets dropped during the simulation σ - Summation operator
●Indicates we're summing values across multiple instances
Ci(t) - Individual Cloudlet at time t and index i
●Represents a single computational task/packet in CloudSim
●Each cloudlet has properties like length, file size, and processing requirements n - Total number of cloudlets
●Upper limit of the summation (total cloudlets submitted) m - Number of dropped cloudlets
●Upper limit for dropped cloudlet summation
Where:
●Dropped Cloudlets: Cloudlets rejected due to resource exhaustion or network capacity limitations
●Total Submitted Cloudlets: All cloudlets submitted by both legitimate and attack traffic generators
The methodology distinguishes between different drop mechanisms (resource exhaustion vs. network
congestion) to provide detailed analysis of failure modes.
TABLE V
Attack
Total Cloudlets
Successful
Dropped
Loss Rate
SYN Flood
10,000
6,500
3,500
35.0%
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HTTP Flood
3,000
1,800
1,200
40.0%
Hybrid Attack
6,500
2730
3770
58.0%
(SYN+HTTP)
Observation: Despite lower volume, HTTP Flood exhibited the highest loss rate (40%) among individual
attacks, underscoring its potency compared to protocol-layer SYN Floods (35%). This 5-percentage-point
difference demonstrates that application-layer attacks achieve greater service degradation through
resourceintensive processing rather than sheer request volume—HTTP Flood used only 3,000 cloudlets
compared to SYN Flood's 10,000, yet produced superior attack effectiveness. Most significantly, Hybrid Attack
accumulated the highest loss rate (58%), representing an 18-percentage-point increase over HTTP Flood and a
23-percentage-point increase over SYN Flood. This compound amplification effect—achieving 58% loss with
only 6,500 total cloudlets (65% of SYN's volume)—validates that multi-vector attacks simultaneously exhaust
both connection tables and application resources, creating synergistic degradation that exceeds the sum of
individual attack impacts. The Hybrid attack's combined protocol properties overwhelm VM processing capacity
by forcing simultaneous handling of high-volume connection attempts and resource-intensive HTTP
transactions, demonstrating that sophisticated attackers employing coordinated multi-layer strategies pose
significantly greater threats than single-vector assaults.
G. Resource Consumption Analysis
TABLE VII (CPU Utilization Over Time)
Time (s)
No Attack (%)
SYN Flood (%)
HTTP Flood (%)
0
15
15
15
10
18
35
42
20
16
58
68
30
20
72
85
40
17
85
94
50
19
89
98
60
18
92
99
70
16
90
99
80
17
88
98
90
18
87
97
Observation: HTTP Flood attacks saturated CPU resources to 99% within 60 seconds, maintaining
nearmaximum utilization throughout the attack duration. In contrast, SYN Flood peaked at 92% before
stabilizing at 87%, while baseline operations remained consistently below 20%. This demonstrates HTTP Flood's
superior capability to exhaust computational resources through application-layer processing demands.
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Fig. 3
TABLE VIII (Bandwidth Usage Over Time)
Time (s)
No Attack
SYN Flood
HTTP Flood
0
2.1
2.1
2.1
10
2.3
0.8
35.0
20
2.0
0.85
58.0
30
2.5
0.82
68.0
40
2.2
0.79
72.0
50
2.4
0.81
75.0
60
2.3
0.83
76.0
70
2.1
0.80
75.0
80
2.2
0.81
74.0
90
2.3
0.82
75.67
Observation: The stark contrast in bandwidth consumption patterns confirms the resource-intensity difference
between attack types. HTTP Flood consumed 75.67 Mbps (93× more than SYN Flood's 0.81 Mbps), saturating
network capacity and blocking legitimate traffic flows. SYN Flood's minimal bandwidth requirement (below
baseline 2.3 Mbps) demonstrates its reliance on connection-state exhaustion rather than network saturation,
representing fundamentally different attack vectors requiring distinct defense strategies.
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Fig. 4
TABLE IX (Memory Utilization Over Time)
Time (s)
No Attack
SYN Flood
HTTP Flood
0
256
256
256
10
280
350
450
20
270
420
650
30
290
465
820
40
275
485
920
50
285
495
980
60
280
500
1010
70
270
498
1020
80
275
495
1018
90
280
492
1015
Observation: Memory exhaustion patterns reveal HTTP Flood's aggressive resource consumption, growing
from 256 MB baseline to 1,020 MB peak (4× increase), while SYN Flood reached only 500 MB (1.95× increase).
HTTP Flood's memory demands stem from maintaining complex application-state information, buffering large
file transfers, and processing intensive web transactions. The stabilization at 60 seconds indicates VM memory
capacity saturation, after which the system can only swap existing allocations.
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Fig. 5
TABLE X (Response Time Over Time)
Time (s)
No Attack
SYN Flood
HTTP Flood
Threshold (ms)
0
120
120
120
2000
10
135
450
680
2000
20
125
820
1350
2000
30
140
1200
1950
2000
40
130
1650
2450
2000
50
138
1850
2850
2000
60
135
1950
3100
2000
70
125
1920
3200
2000
80
130
1880
3150
2000
90
135
1850
3100
2000
Observation: Response time degradation serves as the most critical service quality indicator. HTTP Flood
exceeded the 2,000 ms threshold at 40 seconds, reaching 3,200 ms peak (26.7× baseline), rendering the service
effectively unusable. SYN Flood approached but remained just below the threshold (1,950 ms maximum),
indicating degraded but marginally functional service. The threshold breach timing correlates directly with
resource saturation points observed in CPU and memory metrics, validating the integrated nature of DDoS
impact across multiple system layers.
Note: Response time threshold of 2000ms (2 seconds) indicates service degradation boundary.
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Fig. 6
TABLE XI ( CPU Utilization Heatmap Data (%))
VM Instance
0-15s
15-30s
30-45s
45-60s
60-75s
75-90s
VM1
25
28
32
35
38
40
VM2
42
48
55
62
68
72
VM3
75
82
88
92
95
97
VM4
85
90
93
95
96
97
VM5
88
92
94
96
97
98
VM6
87
90
92
94
95
96
Observation: The heatmap reveals heterogeneous attack impact distribution across VM instances. VM1-VM2
experienced moderate stress (40-72% peak utilization), while VM3-VM6 reached critical saturation (95-98%).
This spatial variation indicates that attack traffic does not distribute uniformly, with certain VMs bearing
disproportionate loads due to CloudSim's scheduling algorithms and network topology. The temporal progression
shows accelerated escalation in the first 45 seconds (0-88% on VM3) before reaching sustained saturation,
suggesting early detection windows exist before complete system compromise.
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Fig. 7
Colour Legend:
● Green (0-30%): Low utilization - system operating normally
● Yellow (31-70%): Medium utilization - increased load
● Red (71-100%): High utilization - near saturation
G. Countermeasure Effectiveness
TABLE XII (Loss Rate Reduction by Defense Mechanism)
Defense Mechanism
SYN Flood Loss Rate
(%)
HTTP Flood Loss Rate
(%)
Average Reduction
(%)
No Defense (Baseline)
35.0
40.0
0.0
Rate Limiting
22.0
28.0
28.6
Adaptive Load Balancing
18.0
25.0
38.6
ML-Based Detection
8.0
12.0
73.3
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Fig 8
Observation: Countermeasure effectiveness increases dramatically with sophistication level. Rate limiting
provides modest improvement (28.6% average reduction) by capping throughput but cannot distinguish
malicious from legitimate high-volume traffic. Load balancing achieves 38.6% reduction through resource
distribution, effectively delaying saturation. ML-based detection delivers superior performance (73.3%
reduction), cutting SYN loss to 8% and HTTP to 12%, demonstrating that behavioral analysis outperforms simple
volumetric approaches. The consistent pattern across both attack types validates ML detection as a universal
defense strategy applicable regardless of attack vector.
TABLE XIII Detection Accuracy by Approach
Detection Approach
Accuracy (%)
False Positive Rate (%)
False Negative Rate (%)
Threshold-Based
72
18
28
Statistical Analysis
85
10
15
Machine Learning
94
4
6
AI-Based Anomaly Detection
98
2
2
Observation: Detection accuracy improvements follow a clear technological progression, with each generation
reducing error rates substantially. Threshold-based approaches suffer from high false negatives (28%), missing
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sophisticated attacks that stay below volumetric triggers. Statistical methods improve to 85% accuracy but still
generate 10% false positives, potentially blocking legitimate traffic spikes. Machine learning achieves 94%
accuracy with balanced 4-6% error distribution, while AI-based anomaly detection reaches near-optimal 98%
accuracy with only 2% error rates in both categories. The dramatic false positive reduction (from 18% to 2%) is
particularly critical for preventing service disruption to legitimate users during normal traffic spikes.
TABLE XIV (Performance Metrics Under Defense)
Metric
No Defense
Rate Limiting
Load Balancing
ML Detection
Avg Response Time (ms) -
SYN
1850
1420
980
450
Avg Response Time (ms) -
HTTP
3100
2650
2180
850
Throughput (requests/sec)
65
78
82
92
CPU Utilization (%)
97
85
72
58
Observation: Performance improvements correlate directly with defense sophistication. ML-based detection
achieves the most dramatic gains: SYN response times drop 75.7% (1,850→450ms), HTTP by 72.6%
(3,100→850ms), while throughput increases 41.5% (65→92 req/s). CPU utilization reduction to 58% indicates
efficient traffic filtering prevents resource exhaustion before it occurs. Rate limiting and load balancing show
incremental improvements but cannot match ML's ability to selectively block malicious traffic while preserving
legitimate service capacity. These metrics demonstrate that effective DDoS defense must optimize both security
(blocking attacks) and performance (maintaining service quality), with ML-based approaches achieving optimal
balance between threat mitigation and user experience.
G. Countermeasure Effectiveness
TABLE XV( Loss Rate vs Infrastructure Size)
Infrastructure
Size
SYN Flood Loss Rate
(%)
HTTP Flood Loss Rate
(%)
Avg Response Time
(ms)
10 VMs
35.0
40.0
1850
50 VMs
32.0
38.0
1620
100 VMs
28.0
35.0
1380
200 VMs
22.0
30.0
980
500 VMs
18.0
25.0
650
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Fig 9
Observation: Infrastructure scaling demonstrates logarithmic resilience improvement with diminishing returns
at higher scales. Doubling from 10 to 50 VMs reduces SYN loss by only 3 percentage points (35%→32%), while
10× scaling to 500 VMs achieves 17-point reduction (35%→18%). Response time improvements follow similar
patterns, decreasing 64.9% overall (1,850→650ms) but with smaller gains at each increment. This suggests an
optimal infrastructure size exists where additional scaling becomes economically inefficient—the 200-500 VM
range shows only 4-5 point loss rate improvements despite 2.5× cost increase, indicating 200 VMs may represent
the practical scaling ceiling for cost-effective DDoS resilience without specialized defense mechanisms.
TABLE XVI (Resource Allocation per Infrastructure Scale)
Infrastructure Size
Total MIPS
Total RAM (GB)
Total Bandwidth (Gbps)
Hosts
10 VMs
10,000
5
1
2
50 VMs
50,000
25
5
10
100 VMs
100,000
50
10
20
200 VMs
200,000
100
20
40
500 VMs
500,000
250
50
100
Observation: Resource allocation scales linearly with infrastructure size, maintaining consistent per-VM ratios
(1,000 MIPS, 512 MB RAM, 100 Mbps bandwidth). This uniform distribution enables controlled experimental
comparison across scales, isolating the effect of parallelization from resource heterogeneity. The host-to-VM
ratio remains constant at 1:5, ensuring that resource contention patterns remain comparable. Bandwidth scaling
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from 1 Gbps to 50 Gbps is particularly critical for HTTP Flood resilience, as the 75.67 Mbps attack consumption
becomes progressively less significant relative to total capacity (7.6% at 1 Gbps vs. 0.15% at 50 Gbps),
explaining the logarithmic loss rate improvements observed in Table XV.
TABLE XVII (Cost-Effectiveness Analysis)
Infrastructure Size
Attack Success Rate
(%)
Infrastructure Cost Index*
Cost per % Protection
10 VMs
37.5
1.0
- (baseline)
50 VMs
35.0
5.0
2.0
100 VMs
31.5
10.0
1.67
200 VMs
26.0
20.0
1.74
500 VMs
21.5
50.0
3.13
*Cost index normalized to 10-VM configuration as baseline (1.0)
Observation: Infrastructure scaling demonstrates logarithmic resilience improvement with diminishing returns
at higher scales. Doubling from 10 to 50 VMs reduces SYN loss by only 3 percentage points (35%→32%), while
10× scaling to 500 VMs achieves 17-point reduction (35%→18%). Response time improvements follow similar
patterns, decreasing 64.9% overall (1,850→650ms) but with smaller gains at each increment. This suggests an
optimal infrastructure size exists where additional scaling becomes economically inefficient—the 200-500 VM
range shows only 4-5 point loss rate improvements despite 2.5× cost increase, indicating 200 VMs may represent
the practical scaling ceiling for cost-effective DDoS resilience without specialized defense mechanisms.
H. Service Model Comparison (Iaas, Paas, SaaS)
TABLE XVIII (Attack Impact by Service Model)
Service Model
SYN Flood Loss Rate
(%)
HTTP Flood Loss Rate
(%)
Avg Response Time
(ms)
IaaS
35.0
40.0
1850
PaaS
20.0
28.0
1220
SaaS
8.0
15.0
580
Observation: Service abstraction level inversely correlates with DDoS vulnerability. IaaS exposes raw
infrastructure to direct network attacks, yielding highest loss rates (35-40%). PaaS introduces platformmanaged
protection layers, reducing impact by 43-30% through containerization and managed runtimes. SaaS achieves
optimal resilience (8-15% loss) via application-aware defenses that can distinguish legitimate from malicious
traffic at the business logic layer. Response time improvements mirror this pattern (1,850→1,220→580ms),
demonstrating that higher abstraction enables more sophisticated, context-aware defense mechanisms. However,
this comes at the cost of reduced user control and flexibility, presenting organizations with a fundamental
security-autonomy trade-off.
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TABLE XIX (Defense Capability by Service Modell)
Service Model
Built-in Protection
Detection
Capability
Mitigation Speed
User
Control
IaaS
Manual
Limited
Slow (user-dependent)
High
PaaS
Platform-level
Moderate
Medium (automated)
Medium
SaaS
Application-aware
High
Fast (built-in)
Low
Observation: Service models present a fundamental security-control trade-off. IaaS offers maximum flexibility
and user control but requires manual defense implementation, resulting in slow, inconsistent protection
dependent on tenant expertise. PaaS balances automation and control through platform-managed protections,
achieving moderate detection with reasonable mitigation speeds via containerized isolation and resource
throttling. SaaS sacrifices customization for security, providing fast, sophisticated application-layer defenses
with minimal user intervention but limited ability to tune protection parameters. Organizations must weigh
operational autonomy against security effectiveness when selecting service models, with high-risk applications
potentially benefiting from IaaS control despite vulnerability, while commodity services gain superior protection
through SaaS abstraction.
TABLE XX Resource Exposure by Service Model
Resource Type
IaaS Exposure
PaaS Exposure
SaaS Exposure
Network Layer
Direct
Filtered
Protected
VM/Container
Direct Control
Managed
Abstracted
Application Layer
User Responsibility
Shared
Provider Managed
Database Connections
Direct
Pool Managed
Fully Managed
API Endpoints
Exposed
Rate-limited
Throttled
Observation: Resource exposure maps directly to attack surface vulnerability. IaaS presents all layers for direct
exploitation—network, compute, application, and data—placing complete defensive burden on tenants who may
lack security expertise. PaaS reduces exposure through managed services, implementing connection pooling,
rate limiting, and container isolation that automatically mitigate many attack vectors without user intervention.
SaaS abstracts all infrastructure concerns, exposing only controlled API endpoints with built-in throttling and
authentication, essentially eliminating protocol-layer attacks while focusing defense on application logic
vulnerabilities. The progression from direct to manage to abstracted resources explains the 45× loss rate
improvement observed across service models, validating that managed security services outperform ad-hoc
tenant implementations in DDoS resilience.
DISCUSSION
The findings reveal that attack sophistication outweighs volume: SYN Flood depends on overwhelming TCP
state tables with thousands of requests, while HTTP Flood relies on fewer, resource-intensive transactions. This
validates the growing concern of application-layer DDoS where traffic appears legitimate but cripples system
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performance. The 40% loss rate in HTTP Flood confirms that conventional volume-based defense systems are
insufficient. While CloudSim provides a controlled, reproducible environment for systematic DDoS analysis, we
acknowledge that simulation results require validation against real-world datasets to confirm external validity.
Future work will compare these findings with empirical data from production cloud environments and evaluate
the implemented countermeasures under realistic traffic patterns.
CONCLUSION
This study demonstrated, via CloudSim simulation, that both SYN Flood and HTTP Flood attacks severely
compromise cloud infrastructure integrity, but HTTP Flood is more destructive due to its application-layer
complexity and high resource consumption. Cloud defenses must therefore move beyond volumetric detection
to intelligent, behavior-based mechanisms capable of identifying application-level anomalies. Future work will
integrate machine-learning-based detection and mitigation modules into the same CloudSim testbed for
comparative evaluation.
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