A comprehensive study on cybersecurity challenges and opportunities in the <scp>IoT</scp> world

Lone, Aejaz Nazir; Mustajab, Suhel; Alam, Mahfooz

doi:10.1002/spy2.318

Cited by 44 publications

(4 citation statements)

References 136 publications

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“…While IoT offers immense benefits in terms of efficiency and automation, it also brings forth intricate cybersecurity challenges [2]. The interconnectivity and heterogeneity of IoT devices exposes these networks to sophisticated cyber threats, demanding robust defined mechanisms to safeguard sensitive data and preserve operational continuity [3].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Intrusion Detection with LSTM-Based Model, Feature Selection, and SMOTE for Imbalanced Data

Sayegh,

Dong,

Al-madani

2024

Applied Sciences

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This study introduces a sophisticated intrusion detection system (IDS) that has been specifically developed for internet of things (IoT) networks. By utilizing the capabilities of long short-term memory (LSTM), a deep learning model renowned for its proficiency in modeling sequential data, our intrusion detection system (IDS) effectively discerns between regular network traffic and potential malicious attacks. In order to tackle the issue of imbalanced data, which is a prevalent concern in the development of intrusion detection systems (IDSs), we have integrated the synthetic minority over-sampling technique (SMOTE) into our approach. This incorporation allows our model to accurately identify infrequent incursion patterns. The rebalancing of the dataset is accomplished by SMOTE through the generation of synthetic samples belonging to the minority class. Various strategies, such as the utilization of generative adversarial networks (GANs), have been put forth in order to tackle the issue of data imbalance. However, SMOTE (synthetic minority over-sampling technique) presents some distinct advantages when applied to intrusion detection. The SMOTE is characterized by its simplicity and proven efficacy across diverse areas, including in intrusion detection. The implementation of this approach is straightforward and does not necessitate intricate adversarial training techniques such as generative adversarial networks (GANs). The interpretability of SMOTE lies in its ability to generate synthetic samples that are aligned with the properties of the original data, rendering it well suited for security applications that prioritize transparency. The utilization of SMOTE has been widely embraced in the field of intrusion detection research, demonstrating its effectiveness in augmenting the detection capacities of intrusion detection systems (IDSs) in internet of things (IoT) networks and reducing the consequences of class imbalance. This study conducted a thorough assessment of three commonly utilized public datasets, namely, CICIDS2017, NSL-KDD, and UNSW-NB15. The findings indicate that our LSTM-based intrusion detection system (IDS), in conjunction with the implementation of SMOTE to address data imbalance, outperforms existing methodologies in accurately detecting network intrusions. The findings of this study provide significant contributions to the domain of internet of things (IoT) security, presenting a proactive and adaptable approach to safeguarding against advanced cyberattacks. Through the utilization of LSTM-based deep learning techniques and the mitigation of data imbalance using SMOTE, our AI-driven intrusion detection system (IDS) enhances the security of internet of things (IoT) networks, hence facilitating the wider implementation of IoT technologies across many industries.

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Section: Introductionmentioning

confidence: 99%

Enhanced Intrusion Detection with LSTM-Based Model, Feature Selection, and SMOTE for Imbalanced Data

Sayegh,

Dong,

Al-madani

2024

Applied Sciences

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“…Cloud computing resource scheduling and IoT security have made great progress through continuous development [1]. Many studies focus on cloud computing scheduling, including algorithms based on deep reinforcement learning [2], system security [3], ordinal optimization [4], and multi-objective trustawareness [5].…”

Section: Introductionmentioning

confidence: 99%

A cloud-edge collaborative task scheduling method based on model segmentation

Zhang,

Chen,

et al. 2024

J Cloud Comp

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With the continuous development and combined application of cloud computing and artificial intelligence, some new methods have emerged to reduce task execution time for training neural network models in a cloud-edge collaborative environment. The most attractive method is neural network model segmentation. However, many factors affect the segmentation point, such as resource allocation, system energy consumption, load balancing, and network Bandwidth allocation. Some segmentation methods consider the shortest task execution time, which ignores the utilization of resources at the edge and can result in resource waste. Additionally, these factors are difficult to measure, which presents a challenge in calculating the best segmentation point to achieve the goal of maximum resource utilization and minimum task execution time. To solve this problem, this paper proposes a cloud-edge collaborative task scheduling method based on model segmentation (CECMS). This method first analyzes the factors affecting the segmentation point of the model and then obtains accurate factors that affect the segmentation point calculation through the pre-execution method. Furthermore, a multi-objective solution algorithm is improved to calculate the optimal model segmentation point. And tasks are separately offloaded to the edge and cloud based on the optimal model segmentation point. Finally, the experiments are conducted to verify the effectiveness of this method. Finally, the effectiveness of the CECMS method was verified through simulation experiments. Compared with the Dynamic Adaptive DNN Surgery (DADS) method and an adaptive DNN inference acceleration framework algorithm with end–edge–cloud collaborative computing algorithm (ADC), CECMS achieves the same effectiveness as DADS and ADC in optimizing task execution time by comprehensively considering the utilization of edge resources and minimizing task execution time, while also effectively ensuring resource utilization.

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“…The properties of the attributes frequently have an influence on SDP model efficiency (Tanwar and Kakkar, 2019). The author (Lone et al, 2023), has used AI and machine learning techniques for security emerging computing and software. The final establishment of an accepted set of characteristics for predicting defects has not yet taken place.…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach for optimizing software defect prediction using a grey wolf optimization and multilayer perceptron

Mustaqeem,

Mustajab,

Alam

2024

IJICC

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PurposeSoftware defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.Design/methodology/approachThe integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.FindingsThe performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.Originality/valueExperimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

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A comprehensive study on cybersecurity challenges and opportunities in the IoT world

Cited by 44 publications

References 136 publications

Enhanced Intrusion Detection with LSTM-Based Model, Feature Selection, and SMOTE for Imbalanced Data

Enhanced Intrusion Detection with LSTM-Based Model, Feature Selection, and SMOTE for Imbalanced Data

A cloud-edge collaborative task scheduling method based on model segmentation

A hybrid approach for optimizing software defect prediction using a grey wolf optimization and multilayer perceptron

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