Memory efficient federated deep learning for intrusion detection in IoT networks.

Deep Neural Networks (DNNs) methods are widely proposed for cyber security monitoring. However, training DNNs requires a lot of computational resources. This restricts direct deployment of DNNs to resource-constrained environments like the Internet of Things (IoT), especially in federated learning settings that train an algorithm across multiple decentralized edge devices. Therefore, this paper proposes a memory efficient method of training a Fully Connected Neural Network (FCNN) for IoT security monitoring in federated learning settings. The model‘s performance was evaluated against eleven realistic IoT benchmark datasets. Experimental results show that the proposed method can reduce memory requirement by up to 99.46 percentage points when compared to its benchmark counterpart, while maintaining the state-of-the-art accuracy and F1 score

Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring.

The application of Deep Neural Networks (DNNs) for monitoring cyberattacks in Internet of Things (IoT) systems has gained significant attention in recent years. However, achieving optimal detection performance through DNN training has posed challenges due to computational intensity and vulnerability to adversarial samples. To address these issues, this paper introduces an optimization method that combines regularization and simulated micro-batching. This approach enables the training of DNNs in a robust, efficient, and resource-friendly manner for IoT security monitoring. Experimental results demonstrate that the proposed DNN model, including its performance in Federated Learning (FL) settings, exhibits improved attack detection and resistance to adversarial perturbations compared to benchmark baseline models and conventional Machine Learning (ML) methods typically employed in IoT security monitoring. Notably, the proposed method achieves significant reductions of 79.54% and 21.91% in memory and time usage, respectively, when compared to the benchmark baseline in simulated virtual worker environments. Moreover, in realistic testbed scenarios, the proposed method reduces memory footprint by 6.05% and execution time by 15.84%, while maintaining accuracy levels that are superior or comparable to state-of-the-art methods. These findings validate the feasibility and effectiveness of the proposed optimization method for enhancing the efficiency and robustness of DNN-based IoT security monitoring

An energy-efficient full-duplex MAC protocol for distributed wireless networks.

In this letter, we present an energy-efficient medium access control (MAC) protocol for distributed full-duplex (FD) wireless network, termed as energy-FDM. The key aspects of the energy-FDM include energy-efficiency, coexistence of distinct types of FD links, throughput improvement, and backward comparability with conventional half-duplex (HD) nodes. Performance evaluation demonstrates the effectiveness of proposed protocol as a viable solution for full-duplex wireless networks

Resource efficient boosting method for IoT security monitoring.

Machine learning (ML) methods are widely proposed for security monitoring of Internet of Things (IoT). However, these methods can be computationally expensive for resource constraint IoT devices. This paper proposes an optimized resource efficient ML method that can detect various attacks on IoT devices. It utilizes Light Gradient Boosting Machine (LGBM). The performance of this approach was evaluated against four realistic IoT benchmark datasets. Experimental results show that the proposed method can effectively detect attacks on IoT devices with limited resources, and outperforms the state of the art techniques

RRP: a reliable reinforcement learning based routing protocol for wireless medical sensor networks.

Wireless medical sensor networks (WMSNs) offer innovative healthcare applications that improve patients' quality of life, provide timely monitoring tools for physicians, and support national healthcare systems. However, despite these benefits, widespread adoption of WMSN advancements is still hampered by security concerns and limitations of routing protocols. Routing in WMSNs is a challenging task due to the fact that some WMSN requirements are overlooked by existing routing proposals. To overcome these challenges, this paper proposes a reliable multi-agent reinforcement learning based routing protocol (RRP). RRP is a lightweight attacks-resistant routing protocol designed to meet the unique requirements of WMSN. It uses a novel Q-learning model to reduce resource consumption combined with an effective trust management system to defend against various packet-dropping attacks. Experimental results prove the lightweightness of RRP and its robustness against blackhole, selective forwarding, sinkhole and complicated on-off attacks

Android source code vulnerability detection: a systematic literature review

The use of mobile devices is rising daily in this technological era. A continuous and increasing number of mobile applications are constantly offered on mobile marketplaces to fulfil the needs of smartphone users. Many Android applications do not address the security aspects appropriately. This is often due to a lack of automated mechanisms to identify, test, and fix source code vulnerabilities at the early stages of design and development. Therefore, the need to fix such issues at the initial stages rather than providing updates and patches to the published applications is widely recognized. Researchers have proposed several methods to improve the security of applications by detecting source code vulnerabilities and malicious codes. This Systematic Literature Review (SLR) focuses on Android application analysis and source code vulnerability detection methods and tools by critically evaluating 118 carefully selected technical studies published between 2016 and 2022. It highlights the advantages, disadvantages, applicability of the proposed techniques and potential improvements of those studies. Both Machine Learning (ML) based methods and conventional methods related to vulnerability detection are discussed while focusing more on ML-based methods since many recent studies conducted experiments with ML. Therefore, this paper aims to enable researchers to acquire in-depth knowledge in secure mobile application development while minimizing the vulnerabilities by applying ML methods. Furthermore, researchers can use the discussions and findings of this SLR to identify potential future research and development directions

ETAREE: an effective trend-aware reputation evaluation engine for wireless medical sensor networks.

Wireless Medical Sensor Networks (WMSN) will play a significant role in the advancements of modern healthcare applications. Security concerns are still the main obstacle to the widespread adoption of this technology. Conventional security approaches, such as authentication and encryption, are able to defend against external attacks effectively. However, internally launched threats, either by compromised or selfish nodes, require further security measures to be detected. In this paper, an Effective Trend-Aware Reputation Engine (ETAREE) is proposed for WMSN. ETAREE uses a novel updating mechanism to evaluate the reputation value, which makes it effective in detecting malicious nodes. Moreover, the proposed updating mechanism of ETAREE can efficiently detect on-off attacks. ETAREE security evaluations have been presented and compared with different reputation evaluation models, demonstrating faster detection of malicious behaviours

The Immunological Effectiveness of Some Common Plants

Three plant species were picked randomly and their alcoholic extracts have been screened to know their effects on the phagocytic capability and intracellular killing of yeast by human peripheral macrophages. Macrophage cultures were incubated with different concentration of each plant extract: for 15 min., 30 min .and 45 min. The phagocytes activity in Iresine herbstii extract was significantly (p?0.05) increased with increasing dose and time of incubation. In Mentha piperita extract, increasing in dose and time of incubation leads to elevate phagocytic capbility, especially in the dose of 20% and 25% of plant extract, perhaps because the antimicrobial and antiviral activities of this plant, as well as strong antioxidant and antitumor actions. While in Elettaria cardamomum, a significant elevation has been observed in phagocytic efficiency when the dose of extract increase to 15%, then decreased in the subsequent doses (20% and 25%), in three periods of time. These findings may suggest that cardamom exert immunomodulatory roles

TrustMod: a trust management module for NS-3 simulator.

Trust management offers a further level of defense against internal attacks in ad hoc networks. Deploying an effective trust management scheme can reinforce the overall network security. Regardless of limitations, however, security researchers often use numerical simulations to prove the merits of novel methods. This is due to the lack of an adequate testbed to evaluate the proposed trust schemes. Therefore, there is a demanding need to develop a generic testbed that can be used to evaluate the trust relationship for different networks and protocols. This paper proposes TrustMod, an NS-3 module consisting of three main components to evaluate the different trust relationships: direct trust, uncertainty, and indirect trust. It is designed to meet usability, generalisability, flexibility, scalability and high-performance requirements. A series of experiments involving 1680 simulations were performed to prove the design and implementation accuracy of TrustMod. The performance results show that TrustMod's resource footprint is minimal, even for very large networks

A survey on wireless body area networks: architecture, security challenges and research opportunities.

In the era of communication technologies, wireless healthcare networks enable innovative applications to enhance the quality of patients’ lives, provide useful monitoring tools for caregivers, and allows timely intervention. However, due to the sensitive information within the Wireless Body Area Networks (WBANs), insecure data violates the patients’ privacy and may consequently lead to improper medical diagnosis and/or treatment. Achieving a high level of security and privacy in WBAN involves various challenges due to its resource limitations and critical applications. In this paper, a comprehensive survey of the WBAN technology is provided, with a particular focus on the security and privacy concerns along with their countermeasures, followed by proposed research directions and open issues