Internet of Things (IoT) devices are vulnerable to zero-day attacks because most of them have weak or no inherent security due to the resource constraints of the devices. This weakness underscores the growing need for anomaly-based intrusion detection systems tailored to IoT networks. Nevertheless, general anomaly detection traditionally has a high number of false positives that drain analysts' time. Also, a semantic difference exists between the system's results and the operators' interpretations. We introduce a machine learning-based framework to tackle these issues in traditional systems in this paper by combining large language models (LLMs). Our model is effective in identifying possible threats as well as filling the semantic gap. The framework uses isolation forests to detect anomalies and random forests to measure device integrity. To further improve the assessment of anomalies and increase interpretability, system insights are further refined using GPT-4o mini, an LLM. The model gives statistical summaries of the IoT traffic, a risk score, and an explanation in easy language, which is easy to understand and therefore makes the process of decision-making easier. Such a novel system reduces the reliance on dedicated network operators and allows non-technical users to better understand and act on the results of the system.
M. Saeed, Rashid A. Saeed, Salah Hagahmoodi et al.· Baghdad Science Journal· 0 citations
A new paradigm for satisfying the ever-growing demands of real-time Sixth Generation (6G) applications is Mobile Edge Computing (MEC). Additionally, base stations and Internet of Things devices that incorporate renewable energy harvesting capabilities have the potential to lower grid energy use. To maximize system potential and lower carbon emissions, it is crucial to make effective decisions about job offloading and resource allocation. A carbon-aware MEC architecture that uses both grid and renewable energy sources is proposed in this paper. Our goal is to jointly manage resource allocation and task offloading while monitoring carbon emissions and task queue delays to optimize system behavior under uncertainty, specifically for stochastic workloads and variable renewable generation. To balance these two cost components (emissions and queue length), we create a combined optimization problem. We develop a deep deterministic policy gradient (DDPG)-based joint optimization technique to address this issue in a constantly changing environment. In the optimization, we consider greedy policy (GP) and full offloading (FO), as well as time-average carbon emission (TACE) and time-average queue length (TAQL) as performance metrics, and time-average queue length (TAQL) and full execution (FE) as baseline strategies; we also evaluate normalized time-average cumulative reward (NTACR). This method uses continuous-action reinforcement learning to generate efficient, real-time control policies. For the proposed MEC network, numerical statistics show that our approach can lead to effective offloading and lower carbon emissions.
M. Saeed, Rashid A Saeed, M. A. Ahmed et al.· 2026 6th International Confe...· 0 citations