Real-Time DDoS Attack Detection System Using Big Data Approach

Awan, Mazhar Javed; Farooq, Umar; Babar, Hafiz Muhammad Aqeel; Yasin, Awais; Nobanee, Haitham; Hussain, Muzammil; Hakeem, Owais; Zain, Azlan Mohd

doi:10.3390/su131910743

Cited by 130 publications

(45 citation statements)

References 56 publications

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“…The data used in this study are limited to Pakistan, but, in the future, we plan to extend the functionalities to predict and identify suicide attacks at a global level through inspiration from recent work of big data Spark ML and big deep learning models [45][46][47][48][49][50][51][52][53].…”

Section: Discussionmentioning

confidence: 99%

Suicide Bomb Attack Identification and Analytics through Data Mining Techniques

et al. 2021

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Suicide bomb attacks are a high priority concern nowadays for every country in the world. They are a massively destructive criminal activity known as terrorism where one explodes a bomb attached to himself or herself, usually in a public place, taking the lives of many. Terrorist activity in different regions of the world depends and varies according to geopolitical situations and significant regional factors. There has been no significant work performed previously by utilizing the Pakistani suicide attack dataset and no data mining-based solutions have been given related to suicide attacks. This paper aims to contribute to the counterterrorism initiative for the safety of this world against suicide bomb attacks by extracting hidden patterns from suicidal bombing attack data. In order to analyze the psychology of suicide bombers and find a correlation between suicide attacks and the prediction of the next possible venue for terrorist activities, visualization analysis is performed and data mining techniques of classification, clustering and association rule mining are incorporated. For classification, Naïve Bayes, ID3 and J48 algorithms are applied on distinctive selected attributes. The results exhibited by classification show high accuracy against all three algorithms applied, i.e., 73.2%, 73.8% and 75.4%. We adapt the K-means algorithm to perform clustering and, consequently, the risk of blast intensity is identified in a particular location. Frequent patterns are also obtained through the Apriori algorithm for the association rule to extract the factors involved in suicide attacks.

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Section: Discussionmentioning

confidence: 99%

Suicide Bomb Attack Identification and Analytics through Data Mining Techniques

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“…Awan et al [13] developed a procedure for detecting and relieving DDoS attacks through using Apache Spark from a large amount of data delivery times. In their research outcomes, the Apache Spark process shows much faster data execution than other existing frameworks.…”

Section: Related Workmentioning

confidence: 99%

“…Increasing user loads within IoT connections raises latency-based delays toward gateway endpoints [10][11][12]. When distributed processes have been merged and aligned to continue [13,14] through the network gateway, they occur due to delayed package delivery and inconsistent planning of processes for high memory gain at the edge of the network nodes [15,16].…”

Section: Introductionmentioning

confidence: 99%

BDPS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration

et al. 2021

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The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd–Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.

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“…On average, 360,000 new malware files were detected every day in 2020, and the number of files found daily has increased by 5.2%. This rapid growth in malware production and distribution became possible due to the use of intelligent and automatic malware generation software such as SpyEye of Zeus and denial of service [2,3]. Newer dangers are evolving as blended threats continue to combine various types of assault into one with more deadly payloads.…”

Section: Introductionmentioning

confidence: 99%

Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention

et al. 2021

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In recent years the amount of malware spreading through the internet and infecting computers and other communication devices has tremendously increased. To date, countless techniques and methodologies have been proposed to detect and neutralize these malicious agents. However, as new and automated malware generation techniques emerge, a lot of malware continues to be produced, which can bypass some state-of-the-art malware detection methods. Therefore, there is a need for the classification and detection of these adversarial agents that can compromise the security of people, organizations, and countless other forms of digital assets. In this paper, we propose a spatial attention and convolutional neural network (SACNN) based on deep learning framework for image-based classification of 25 well-known malware families with and without class balancing. Performance was evaluated on the Malimg benchmark dataset using precision, recall, specificity, precision, and F1 score on which our proposed model with class balancing reached 97.42%, 97.95%, 97.33%, 97.11%, and 97.32%. We also conducted experiments on SACNN with class balancing on benign class, also produced above 97%. The results indicate that our proposed model can be used for image-based malware detection with high performance, despite being simpler as compared to other available solutions.

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Real-Time DDoS Attack Detection System Using Big Data Approach

Cited by 130 publications

References 56 publications

Suicide Bomb Attack Identification and Analytics through Data Mining Techniques

Suicide Bomb Attack Identification and Analytics through Data Mining Techniques

BDPS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration

Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention

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