Suicide Bomb Attack Identification and Analytics through Data Mining Techniques

Ferooz, Faria; Hassan, Malik Tahir; Awan, Mazhar Javed; Nobanee, Haitham; Kamal, Maryam; Yasin, Awais; Zain, Azlan Mohd

doi:10.3390/electronics10192398

Cited by 14 publications

(10 citation statements)

References 38 publications

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“…Presently, numerous applications were executed in finance, horticulture, payment, and medication territory. This blockchain technology application is used in air traffic controllers (ATCs) to lessen fracture, failure, and ungraceful activities in the air terminal industry [89][90][91][92][93][94].…”

Section: Industrial and Academic Areamentioning

confidence: 99%

Blockchain-Based IoT Devices in Supply Chain Management: A Systematic Literature Review

et al. 2021

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Through recent progress, the forms of modern supply chains have evolved into complex networks. The supply chain management systems face a variety of challenges. These include lack of visibility of the upstream party (Provider) to the downstream party (Client); lack of flexibility in the face of sudden variations in demand and control of operating costs; lack of reliance on safety stakeholders; ineffective management of supply chain risks. Blockchain (BC) is used in the supply chain to overcome the growing demands for items. The Internet of Things (IoT) is a profoundly encouraging innovation that can help companies observe, track, and monitor products, activities, and processes within their respective value chain networks. Research establishments and logical gatherings are ceaselessly attempting to answer IoT gadgets in supply chain management. This paper presents orderly writing on and reviewing of Blockchain-based IoT advances and their current usage. We discuss the smart devices used in this system and which device is the most appropriate in the supply chain. This paper also looks at future examination themes in blockchain-based IoT, referred to as the executive’s framework production network. The essential deliberate writing audit has been consolidated by surveying research articles circulated in highly reputable publications between 2016 and 2021. Lastly, current issues and challenges are present to provide researchers with promising future directions in IoT supply chain management systems.

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Section: Industrial and Academic Areamentioning

confidence: 99%

Blockchain-Based IoT Devices in Supply Chain Management: A Systematic Literature Review

et al. 2021

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“…The IoT devices are susceptible to weaponization and hijacking for the Distributed Denial of Service (DDoS) attacks [13], man-in-the-middle attack [14], targeted code injection [15], and pose estimation [16]. In addition to this, the IoT devices are remotely controlled by the bad actors, creating a significant impact while transmitting data in the network.…”

Section: Introductionmentioning

confidence: 99%

Threat Analysis and Distributed Denial of Service (DDoS) Attack Recognition in the Internet of Things (IoT)

et al. 2022

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The Internet of Things (IoT) plays a crucial role in various sectors such as automobiles and the logistic tracking medical field because it consists of distributed nodes, servers, and software for effective communication. Although this IoT paradigm has suffered from intrusion threats and attacks that cause security and privacy issues, existing intrusion detection techniques fail to maintain reliability against the attacks. Therefore, the IoT intrusion threat has been analyzed using the sparse convolute network to contest the threats and attacks. The web is trained using sets of intrusion data, characteristics, and suspicious activities, which helps identify and track the attacks, mainly, Distributed Denial of Service (DDoS) attacks. Along with this, the network is optimized using evolutionary techniques that identify and detect the regular, error, and intrusion attempts under different conditions. The sparse network forms the complex hypotheses evaluated using neurons, and the obtained event stream outputs are propagated to further hidden layer processes. This process minimizes the intrusion involvement in IoT data transmission. Effective utilization of training patterns in the network successfully classifies the standard and threat patterns. Then, the effectiveness of the system is evaluated using experimental results and discussion. Network intrusion detection systems are superior to other types of traditional network defense in providing network security. The research applied an IGA-BP network to combat the growing challenge of Internet security in the big data era, using an autoencoder network model and an improved genetic algorithm to detect intrusions. MATLAB built it, which ensures a 98.98% detection rate and 99.29% accuracy with minimal processing complexity, and the performance ratio is 90.26%. A meta-heuristic optimizer was used in the future to increase the system’s ability to forecast attacks.

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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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Suicide Bomb Attack Identification and Analytics through Data Mining Techniques

Cited by 14 publications

References 38 publications

Blockchain-Based IoT Devices in Supply Chain Management: A Systematic Literature Review

Blockchain-Based IoT Devices in Supply Chain Management: A Systematic Literature Review

Threat Analysis and Distributed Denial of Service (DDoS) Attack Recognition in the Internet of Things (IoT)

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

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