Agile Approaches for Cybersecurity Systems, IoT and Intelligent Transportation

Tashtoush, Yahya; Darweesh, Dirar; Hussari, Ghaith; Darwish, Omar; Darwish, Yousef; Issa, Luai Bani; Ashqar, Huthaifa I.

doi:10.1109/access.2021.3136861

Cited by 25 publications

(6 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, LSTMs have become a cornerstone in deep learning (DL) for effectively categorizing and processing sequential and time-series data, overcoming the limitations associated with discharging and disappearing gradients that plagued earlier RNN models [29]. Tashtouth et al [38] and Al-Eidi et al [39] discussed implementing cybersecurity systems and highlighted the dual nature of technological advancements in cybersecurity, offering insights into both the challenges and opportunities presented by novel attack vectors and defensive mechanisms.…”

Section: Nlp Models: Rnn and Lstmmentioning

confidence: 99%

Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection

Alshattnawi,

Shatnawi,

AlSobeh

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

As social media platforms continue their exponential growth, so do the threats targeting their security. Detecting disguised spam messages poses an immense challenge owing to the constant evolution of tactics. This research investigates advanced artificial intelligence techniques to significantly enhance multiplatform spam classification on Twitter and YouTube. The deep neural networks we use are state-of-the-art. They are recurrent neural network architectures with long- and short-term memory cells that are powered by both static and contextualized word embeddings. Extensive comparative experiments precede rigorous hyperparameter tuning on the datasets. Results reveal a profound impact of tailored, platform-specific AI techniques in combating sophisticated and perpetually evolving threats. The key innovation lies in tailoring deep learning (DL) architectures to leverage both intrinsic platform contexts and extrinsic contextual embeddings for strengthened generalization. The results include consistent accuracy improvements of more than 10–15% in multisource datasets, unlocking actionable guidelines on optimal components of neural models, and embedding strategies for cross-platform defense systems. Contextualized embeddings like BERT and ELMo consistently outperform their noncontextualized counterparts. The standalone ELMo model with logistic regression emerges as the top performer, attaining exceptional accuracy scores of 90% on Twitter and 94% on YouTube data. This signifies the immense potential of contextualized language representations in capturing subtle semantic signals vital for identifying disguised spam. As emerging adversarial attacks exploit human vulnerabilities, advancing defense strategies through enhanced neural language understanding is imperative. We recommend that social media companies and academic researchers build on contextualized language models to strengthen social media security. This research approach demonstrates the immense potential of personalized, platform-specific DL techniques to combat the continuously evolving threats that threaten social media security.

show abstract

Section: Nlp Models: Rnn and Lstmmentioning

confidence: 99%

Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection

Alshattnawi,

Shatnawi,

AlSobeh

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…• The Application Layer provides global management of the application based on information processed in the Middleware layer. The example of applications layer such as smart health [73], smart farming [74], smart home [75], smart city [76], intelligent transportation [77], and many others.…”

Section: B Iot Wsn System Generic Architecturementioning

confidence: 99%

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

Hakim

Septiyana

Suwarno

2022

Journal of Robotics and Control

View full text Add to dashboard Cite

In this modern age, Internet of Things (IoT) and Wireless Sensor Network (WSN) as its derivatives have become one of the most popular and important technological advancements. In IoT, all things and services in the real world are digitalized and it continues to grow exponentially every year. This growth in number of IoT device in the end has created a tremendous amount of data and new data services such as big data systems. These new technologies can be managed to produce additional value to the existing business model. It also can provide a forecasting service and is capable to produce decision-making support using computational intelligence methods. In this survey paper, we provide detailed research activities concerning Computational Intelligence methods application in IoT WSN. To build a good understanding, in this paper we also present various challenges and issues for Computational Intelligence in IoT WSN. In the last presentation, we discuss the future direction of Computational Intelligence applications in IoT WSN such as Self-Organizing Network (dynamic network) concept.

show abstract

“…When it comes to the execution of the cybersecurity solution-building activities, it seems that the agile approach should be adopted (Tashtoush et al , 2022). In the context of technology, this means that the final outcomes of the tools and methods depend on close cooperation and communication with end-users.…”

Section: The “New” Human-related Aspects Of Cybersecuritymentioning

confidence: 99%

Human-driven and human-centred cybersecurity: policy-making implications

Pawlicka

Pawlicki

Kozik

et al. 2022

View full text Add to dashboard Cite

Purpose The purpose of this paper is to challenge the prevailing, stereotypical approach of the human aspect of cybersecurity, i.e. treating people as weakness or threat. Instead, several reflections are presented, pertaining to the ways of making cybersecurity human-centred. Design/methodology/approach This paper bases on the authors’ own experiences, gathered whilst working in cybersecurity projects; the resulting comments and reflections have been enriched and backed up by the results of a targeted literature study. Findings The findings show that the way the human aspects of cybersecurity are understood is changing, and deviates from the stereotypical approach. Practical implications This paper provides a number of practical recommendations for policymakers, as well as cybersecurity managers on how to make the cybersecurity more human-centred; it also inspires further research directions. Originality/value This paper presents a fresh, positive approach to humans in cybersecurity and opens the doors to further discourse about new paradigms in the field.

show abstract

Agile Approaches for Cybersecurity Systems, IoT and Intelligent Transportation

Cited by 25 publications

References 70 publications

Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection

Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

Human-driven and human-centred cybersecurity: policy-making implications

Contact Info

Product

Resources

About