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1. Dynamic Defense Provision via Network Functions Virtualization

   https://doi.org/10.1145/3040992.3041005  

   Park, Younghee; Chandaliya, Pritesh; Muralidharan, Akshaya; Kumar, Nikash; Hu, Hongxin (January 2017, Proceedings of the ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization)

   Network Function Virtualization (NFV) is a critical part of a new defense paradigm providing high flexibility at a lower cost through software-based virtual instances. Despite the promise of the NFV, the original Intrusion Detection System (IDS) designed for NFV still draws heavily on processing power and requires significant CPU resources. In this paper, we provide a framework for dynamic defense provision by building in light intrusion detection network functions (NFs) over NFV. Without using the existing IDSes, our system constructs a light intrusion detection system by using a chain of network functions in NFV. The entire IDS is broken down into separate light network functions according to different protocols. The intrusion detection NFs cover various protocol stacks from the link layer to the application layer protocols. They also include different deep packet inspection NFs for different application layer protocols. The experimental results show the proposed system reduces resource consumption while performing valid intrusion detection functions. 

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2. Enabling Dynamic Network Access Control with Anomaly-based IDS and SDN

   https://doi.org/10.1145/3309194.3309199  

   Li, Hongda; Wei, Feng; Hu, Hongxin (January 2019, Proceedings of the 2019 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization)

   In the Software Defined Networking (SDN) and Network Function Virtualization (NFV) era, it is critical to enable dynamic network access control. Traditionally, network access control policies are statically predefined as router entries or firewall rules. SDN enables more flexibility by re-actively installing flow rules into the switches to achieve dynamic network access control. However, SDN is limited in capturing network anomalies, which are usually important signs of security threats. In this paper, we propose to employ anomaly-based Intrusion Detection System (IDS) to capture network anomalies and generate SDN flow rules to enable dynamic network access control. We gain the knowledge of network anomalies from anomaly-based IDS by training an interpretable model to explain its outcome. Based on the explanation, we derive access control policies. We demonstrate the feasibility of our approach by explaining the outcome of an anomaly-based IDS built upon a Recurrent Neural Network (RNN) and generating SDN flow rules based on our explanation. 

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3. Towards Effective Virtualization of Intrusion Detection Systems

   https://doi.org/10.1145/3040992.3041004  

   Zhang, Nuyun; Li, Hongda; Hu, Hongxin; Park, Younghee (January 2017, Proceedings of the ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization)

   Traditional Intrusion Detection Systems (IDSes) are generally implemented on vendor proprietary appliances or middleboxes, which usually lack a general programming interface, and their versatility and flexibility are also very poor. Emerging Network Function Virtualization (NFV) technology can virtualize IDSes and elastically scale them to deal with attack traffic variations. However, existing NFV solutions treat a virtualized IDS as a monolithic piece of software, which could lead to inflexibility and significant waste of resources. In this paper, we propose a novel approach to virtualize IDSes as microservices where the virtualized IDSes can be customized on demand, and the underlying microservices could be shared and scaled independently. We also conduct experiments, which demonstrate that virtualizing IDSes as microservices can gain greater flexibility and resource efficiency. 

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4. Blockchain-Based Architecture for Secured Cyber-Attack Features Exchange

   Ajayi, O.; Saadawi, T. (August 2020, The 7th International conference on Cyber Security and cloud computing (IEEE CSCLOUD 2020))

   Despite the increased accuracy of intrusion detection systems (IDS) in identifying cyberattacks in computer networks and devices connected to the internet, distributed or coordinated attacks can still go undetected or not detected on time. The single vantage point limits the ability of these IDSs to detect such attacks. Due to this reason, there is a need for attack characteristics’ exchange among different IDS nodes. Researchers proposed a cooperative intrusion detection system to share these attack characteristics effectively. This approach was useful; however, the security of the shared data cannot be guaranteed. More specifically, maintaining the integrity and consistency of shared data becomes a significant concern. In this paper, we propose a blockchain-based solution that ensures the integrity and consistency of attack characteristics shared in a cooperative intrusion detection system. The proposed architecture achieves this by detecting and preventing fake features injection and compromised IDS nodes. It also facilitates scalable attack features exchange among IDS nodes, ensures heterogeneous IDS nodes participation, and it is robust to public IDS nodes joining and leaving the network. We evaluate the security analysis and latency. The result shows that the proposed approach detects and prevents compromised IDS nodes, malicious features injection, manipulation, or deletion, and it is also scalable with low latency. 

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5. A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures

   https://doi.org/10.1145/3406093  

   Otoum, Safa; Kantarci, Burak; Mouftah, Hussein (July 2021, ACM Transactions on Internet Technology)

   Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this article, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognise intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KDD’99 as real attack dataset in our simulations. Results present the performance metrics for three different IDSs, namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), and Q-learning based IDS (Q-IDS), to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that Q-IDS performs with detection rate while SARSA-IDS and TD-IDS perform at the order of . 

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