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In this paper we evaluate the feasibility of running a lightweight Intrusion Detection System within a constrained sensor or IoT node. We propose mIDS, which monitors and detects attacks using a statistical analysis tool based on Binary Logistic Regression (BLR). mIDS takes as input only local node parameters for both benign and malicious behavior and derives a normal behavior model that detects abnormalities within the constrained node. We offer a proof of correct operation by testing mIDS in a setting where network-layer attacks are present. In such a system, critical data from the routing layer is obtained and used as a basis for profiling sensor behavior. Our results show that, despite the lightweight implementation, the proposed solution achieves attack detection accuracy levels within the range of 96%-100%.

Machine learning models have long be proposed to detect the presence of unauthorized activity within computer networks. They are used as anomaly detection techniques to detect abnormal behaviors within the network. We propose to use Support Vector Machine (SVM) learning anomaly detection model to detect abnormalities within the Internet of Things. SVM creates its normal profile hyperplane based on both benign and malicious local sensor activity. An important aspect of our work is the use of actual IoT network traffic with specific networklayer attacks implemented by us. This is in contrast to other works creating supervised learning models, with generic datasets. The proposed detection model achieves up to 100% accuracy when evaluated with unknown data taken from the same network topology as it was trained and 81% accuracy when operating in an unknown topology.

Introduction: Wheelchair users are at a high risk of experiencing non-neuropathic pain of musculoskeletal origin as a result of being wheelchair-bound. The aim of this systematic review was to establish the prevalence of musculoskeletal pain in wheelchair users that is attributable to wheelchair use, and to describe the different pain syndromes and discuss risk factors and management options. Methods: After a systematic MEDLINE search, we identified 40 papers eligible for inclusion. Results: The pooled prevalence of musculoskeletal pain at any location was 50% (95% CI 33-67%). The most common pain syndrome was shoulder pain (pooled prevalence 44%, 95% CI 36-52%). Wheelchair users were 5.8 times as likely to suffer from shoulder pain as controls (95% CI 2.7-12.2, p \ 0.0001). Other pain syndromes included neck, elbow, wrist, hand and low back pain.Older age and increased duration of wheelchair use were the most significant determinants of pain in wheelchair users. Conclusions: Musculoskeletal pain as a result of wheelchair use is very common amongst wheelchair users. Management of pain should follow national and international guidelines. Optimal adjustment of seating position may prevent pain, and is important to be taken into consideration.