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A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

Hameed SS, Hassan WH, Abdul Latiff L, Ghabban F

PeerJ Comput Sci, 2021;7:e414.
PMID: 33834100 DOI: 10.7717/peerj-cs.414

Background: The Internet of Medical Things (IoMTs) is gradually replacing the traditional healthcare system. However, little attention has been paid to their security requirements in the development of the IoMT devices and systems. One of the main reasons can be the difficulty of tuning conventional security solutions to the IoMT system. Machine Learning (ML) has been successfully employed in the attack detection and mitigation process. Advanced ML technique can also be a promising approach to address the existing and anticipated IoMT security and privacy issues. However, because of the existing challenges of IoMT system, it is imperative to know how these techniques can be effectively utilized to meet the security and privacy requirements without affecting the IoMT systems quality, services, and device's lifespan.
Methodology: This article is devoted to perform a Systematic Literature Review (SLR) on the security and privacy issues of IoMT and their solutions by ML techniques. The recent research papers disseminated between 2010 and 2020 are selected from multiple databases and a standardized SLR method is conducted. A total of 153 papers were reviewed and a critical analysis was conducted on the selected papers. Furthermore, this review study attempts to highlight the limitation of the current methods and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data.
Results: It was observed that ML techniques have been significantly deployed for device and network layer security. Most of the current studies improved traditional metrics while ignored performance complexity metrics in their evaluations. Their studies environments and utilized data barely represent IoMT system. Therefore, conventional ML techniques may fail if metrics such as resource complexity and power usage are not considered.
Fulltext Depression and its associated factors among secondary school students in Malaysia

Abdul Latiff L, Tajik E, Ibrahim N, Abubakar AS, Ali SS

Southeast Asian J. Trop. Med. Public Health, 2016 Jan;47(1):131-41.
PMID: 27086434

Research in the field of factors associated with depression among adolescents is lacking in Malaysia. The aims of the present study were to assess the current prevalence of depression and its related factors among secondary school students in Pasir Gudang, South Malaysia. In this cross sectional study, 2,927 secondary school students (13-17 years old) from urban areas were screened for symptoms of mental disorder as well as demographic and risk behaviors using a validated Depression, Anxiety and Stress Scale (DASS) 12 questionnaire. The majority of the respondents (53.8%) were Malay, of which 53.1% were female. Symptoms of mild depression were found in 33.2% of the respondents, while the prevalence of the symptoms of moderate, severe, and extremely severe depression was 21.5%, 18.1%, and 3.0%, respectively. Logistic regression suggested that participants who were Chinese or had a lower average grade were three times more likely to have depression, while those who came from a single-parent family were twice as likely to have this condition. This study indicated that academic performance and risk behaviors along with the demographic characteristics are important contributors to adolescent depression.
Fulltext A Hybrid Lightweight System for Early Attack Detection in the IoMT Fog

Hameed SS, Selamat A, Abdul Latiff L, Razak SA, Krejcar O, Fujita H, et al.

Sensors (Basel), 2021 Dec 11;21(24).
PMID: 34960384 DOI: 10.3390/s21248289

Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection is alternatively used to overcome these problems. However, the current fog-based systems cannot handle the ever-increasing IoMT's big data. Moreover, they are not lightweight and are designed for network attack detection only. In this work, a hybrid (for host and network) lightweight system is proposed for early attack detection in the IoMT fog. In an adaptive online setting, six different incremental classifiers were implemented, namely a novel Weighted Hoeffding Tree Ensemble (WHTE), Incremental K-Nearest Neighbors (IKNN), Incremental Naïve Bayes (INB), Hoeffding Tree Majority Class (HTMC), Hoeffding Tree Naïve Bayes (HTNB), and Hoeffding Tree Naïve Bayes Adaptive (HTNBA). The system was benchmarked with seven heterogeneous sensors and a NetFlow data infected with nine types of recent attack. The results showed that the proposed system worked well on the lightweight fog devices with ~100% accuracy, a low detection time, and a low memory usage of less than 6 MiB. The single-criteria comparative analysis showed that the WHTE ensemble was more accurate and was less sensitive to the concept drift.
Testing comparison models of DASS-12 and its reliability among adolescents in Malaysia

Osman ZJ, Mukhtar F, Hashim HA, Abdul Latiff L, Mohd Sidik S, Awang H, et al.

Compr Psychiatry, 2014 Oct;55(7):1720-5.
PMID: 24952938 DOI: 10.1016/j.comppsych.2014.04.011

OBJECTIVE: The 21-item Depression, Anxiety and Stress Scale (DASS-21) is frequently used in non-clinical research to measure mental health factors among adults. However, previous studies have concluded that the 21 items are not stable for utilization among the adolescent population. Thus, the aims of this study are to examine the structure of the factors and to report on the reliability of the refined version of the DASS that consists of 12 items.
METHOD: A total of 2850 students (aged 13 to 17 years old) from three major ethnic in Malaysia completed the DASS-21. The study was conducted at 10 randomly selected secondary schools in the northern state of Peninsular Malaysia. The study population comprised secondary school students (Forms 1, 2 and 4) from the selected schools.
RESULTS: Based on the results of the EFA stage, 12 items were included in a final CFA to test the fit of the model. Using maximum likelihood procedures to estimate the model, the selected fit indices indicated a close model fit (χ(2)=132.94, df=57, p=.000; CFI=.96; RMR=.02; RMSEA=.04). Moreover, significant loadings of all the unstandardized regression weights implied an acceptable convergent validity. Besides the convergent validity of the item, a discriminant validity of the subscales was also evident from the moderate latent factor inter-correlations, which ranged from .62 to .75. The subscale reliability was further estimated using Cronbach's alpha and the adequate reliability of the subscales was obtained (Total=76; Depression=.68; Anxiety=.53; Stress=.52).
CONCLUSION: The new version of the 12-item DASS for adolescents in Malaysia (DASS-12) is reliable and has a stable factor structure, and thus it is a useful instrument for distinguishing between depression, anxiety and stress.