The growing sophistication and frequency of cyber threats in communication networks demand Intrusion Detection Systems (IDS) that adapt to evolving attack patterns.Traditional approaches, based on static rules or purely supervised models, often fail to recognize novel attacks, leaving critical infrastructures exposed.Reinforcement Learning (RL) provides a dynamic alternative by enabling agents to refine detection policies through continuous feedback.In this work, we propose a Qlearning-based Intrusion Detection (Q-ID) system and train it on the CICIDS2017 dataset.The RL formulation defines the state as the flow's feature vector, the action as the classification decision, and the reward as +1 for correct predictions and -1 otherwise.To ensure stable convergence, the reward is integrated with cross-entropy loss in a hybrid objective, allowing continued improvement even after the supervised component has plateaued.Unlike prior IDS methods that rely solely on offline supervised training, our approach fuses reinforcement feedback with supervised optimization to support adaptive and robust detection.Experimental results, conducted under class imbalance and realistic evaluation splits, show that the proposed system achieves 99.3% accuracy, outperforming strong baselines including deep neural networks and traditional classifiers.Moreover, the RL agent demonstrates robustness under skewed traffic distributions and adaptability to previously unseen attack types.These results highlight reinforcement learning as a promising paradigm for building resilient IDS in critical communication environments.

Modern smart vehicles have a Controller Area Network (CAN) that supports intra-vehicle communication between intelligent Electronic Control Units (ECUs). The CAN is known to be vulnerable to various cyber attacks. In this paper, we propose a unified framework that can detect multiple types of cyber attacks (viz., Denial of Service, Fuzzy, Impersonation) affecting the CAN. Specifically, we construct a feature by observing the timing information of CAN packets exchanged over the CAN bus network over partitioned time windows to construct a low dimensional representation of the entire CAN network as a time series latent space. Then, we apply a two tier anomaly based intrusion detection model that keeps track of short term and long term memory of deviations in the initial time series latent space, to create a 'stateful latent space'. Then, we learn the boundaries of the benign stateful latent space that specify the attack detection criterion. To find hyper-parameters of our proposed model, we formulate a preference based multi-objective optimization problem that optimizes security objectives tailored for a network-wide time series anomaly based intrusion detector by balancing trade-offs between false alarm count, time to detection, and missed detection rate. We use real benign and attack datasets collected from a Kia Soul vehicle to validate our framework and show how our performance outperforms existing works.

Multipath TCP (MPTCP) has been widely used as an efficient way for communication in many applications.Data centers, smartphones, and network operators use MPTCP to balance the traffic in a network efficiently.MPTCP is an extension of TCP (Transmission Control Protocol), which provides multiple paths, leading to higher throughput and low latency.Although MPTCP has shown better performance than TCP in many applications, it has its own challenges.The network can become congested due to heavy traffic in the multiple paths (subflows) if the subflow rates are not determined correctly.Moreover, communication latency can occur if the packets are not scheduled correctly between the subflows.This paper reviews techniques to solve the above-mentioned problems based on two main approaches; non data-driven (classical) and data-driven (Machine Learning) approaches.This paper compares these two approaches and highlights their strengths and weaknesses with a view to motivating future researchers in this exciting area of machine learning for communications.This paper also provides details on the simulation of MPTCP and its implementations in real environments.