A Network Intrusion Detection System (NIDS) helps system administrators to detect network security breaches in their organizations. However, many challenges arise while developing a flexible and efficient NIDS for unforeseen and unpredictable attacks. We propose a deep learning based approach for developing such an efficient and flexible NIDS. We use Self-taught Learning (STL), a deep learning based technique, on NSL-KDD - a benchmark dataset for network intrusion. We present the performance of our approach and compare it with a few previous work. Compared metrics include accuracy, precision, recall, and f-measure values.

Autonomous systems will soon be integrating into our lives as home assistants, delivery drones, and driverless cars. The implementation of the level of automation in these systems from being manually controlled to fully autonomous would depend upon the autonomy approach chosen to design these systems. This article reviews the historical evolution of autonomy, its approaches, and the current trends in related fields to build robust autonomous systems. Toward such a goal and with the increased number of cyberattacks, the security of these systems needs special attention from the research community. To gauge the extent to which research has been done in this area, we discuss the cybersecurity of these systems. It is essential to model the system from a security perspective, identify the threats and vulnerabilities, and then model the attacks. A survey in this direction explores the theoretical/analytical system and attack models that have been proposed over the years and identifies the research gap that needs to be addressed by the research community.

Logically centralized nature of the controller in Software-defined Networking (SDN) makes it vulnerable to various adversarial attacks. These attacks have capabilities to degrade the performance of the managed network or bring it down in the worst case. For this reason, a large-scale deployment of SDN needs evaluation of the impact of adversarial attacks on network services. In this work, we implement various attacks in SDN and analyze their impact on the performance of web-based services running over it. Prior to that, we briefly discuss different kinds of vulnerabilities and threats in SDN. We consider the connection set-up latency and loss, for web-client requests, as metrics for the evaluation. We observe a significant degradation in the performances of web services, in terms of response time and availability, in the presence of implemented attacks.

Autonomous vehicles (AVs) have revolutionized the automotive industry by leveraging data to perceive and interact with their environment effectively. Data safety is essential for supporting AV decision-making and ensuring reliability in complex environments. AVs continuously collect data from multiple sources like LiDAR, RADAR, cameras, and ultrasonic sensors to monitor road conditions, traffic signals, and pedestrian movements. An effective data classification framework is crucial for managing vast amounts of information and securing AV systems against cyber threats. This paper proposes a comprehensive framework for AV data classification, categorizing data by sensitivity, usage, and source. By integrating a review of the literature, real-world cases, and practical insights, this study introduces a novel data classification model and explores sensitivity criteria. The findings aim to assist industry stakeholders in creating secure, efficient, and sustainable AV ecosystems.