Wireless Intrusion Prevention Systems

The wireless networks have changed the way organizations work and offered a new range of possibilities, but at the same time they introduced new security threats. While an attacker needs physical access to a wired network in order to launch an attack, a wireless network allows anyone within its range to passively monitor the traffic or even start an attack. One of the countermeasures can be the use of Wireless Intrusion Prevention Systems.Network security, IDS, IPS, wireless intrusion detection, wireless intrusion prevention.

Foundations for Intrusion Prevention

We propose an infrastructure that helps a system administrator to identify a newly published vulnerability on the site hosts and to evaluate the vulnerability’s threat with respect to the administrator’s security priorities. The infrastructure foundation is the vulnerability semantics, a small set of attributes for vulnerability definition. We demonstrate that with a few attributes it is possible to define the majority of the known vulnerabilities in a way that (i) facilitates their accurate identification, and (ii) enables the administrator to rank the vulnerabilities found according to the organization’s security priorities. A large scale experiment demonstrates that our infrastructure can find significant vulnerabilities even in a site with a high security awareness

Evaluating the effectiveness of an intrusion prevention / honeypot hybrid

An intrusion prevention system is a variation of an intrusion detection system that drops packets that are anomalous based on a chosen criteria. An intrusion prevention system is typically placed on the outer perimeter of a network to prevent intruders from reaching vulnerable machines inside the network, though it can also be placed inside the network in front of systems requiring extra security measures. Unfortunately, intrusion prevention systems, even when properly configured, are susceptible to both false positives and false-negatives. The risk of false positives typically leads organizations to deploy these systems with the prevention capability disabled and only focus on detection. In this paper I propose an expansion to current intrusion prevention systems that combines them with the principles behind honeypots to reduce false positives while capturing attack traffic to improve prevention rules. In an experiment using the Snort-inline intrusion prevention system, I was able to reduce the rate of false positives to zero without negatively impacting the rate of false-negatives. I was further able to capture a successful attack in a way that minimized disruption to legitimate users but allowed the compromised system to be later analyzed to find weaknesses, improve prevention rules, and prevent future attacks

Prevention in Healthcare: An Explainable AI Approach

Intrusion prevention is a critical aspect of maintaining the security of healthcare systems, especially in the context of sensitive patient data. Explainable AI can provide a way to improve the effectiveness of intrusion prevention by using machine learning algorithms to detect and prevent security breaches in healthcare systems. This approach not only helps ensure the confidentiality, integrity, and availability of patient data but also supports regulatory compliance. By providing clear and interpretable explanations for its decisions, explainable AI can enable healthcare professionals to understand the reasoning behind the intrusion detection system's alerts and take appropriate action. This paper explores the application of explainable AI for intrusion prevention in healthcare and its potential benefits for maintaining the security of healthcare systems

Implementasi Intrusion Prevention System (IPS) Pada Keamanan Jaringan Dengan Notifikasi Berbasis Telegram di Jurusan Teknik Komputer

Pada keamanan jaringan memiliki beberapa metode yang digunakan untuk mengamankan jaringan tersebut. Pada penelitian ini menggunakan metode Intrusion Prevention System yang merupakan sebuah metode keamanan yang memanfaatkan teknologi firewall pada MikroTik. Intrusion Prevention System (IPS) adalah perangkat lunak yang berkerja untuk mendeteksi aktifitas yang mencurigakan dan melakukan pencegahan terhadap intrusi pada jaringan. Pada  router MikroTik yang menyediakan beberapa fasilitas untuk mendukung keamanan dan akses jaringan dapat diterapkan sebuah sistem untuk mendeteksi jika terjadi penyerangan pada jaringan komputer. Serangan atau penyusupan dapat dicegah dengan menerapkan Intrusion Prevention System dan serangan dapat terdeteksi tergantung pada pola serangan yang ada di dalam rule IPS. Administrator system dapat mengetahui serangan yang terjadi pada server internet melalui pesan notifikasi yang memuat informasi jenis serangan dan kapan terjadinya yang dikirim oleh system yang dibuat melalui Telegra

Intrusion Detection using Open Source Tools

We have witnessed in the recent years that open source tools have gained popularity among all types of users, from individuals or small businesses to large organizations and enterprises. In this paper we will present three open source IDS tools: OSSEC, Prelude and SNORT.Network security, IDS, IPS, intrusion detection, intrusion prevention, open source

Analysis of intrusion prevention methods

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2004Includes bibliographical references (leaves: 105-108)Text in English; Abstract: Turkish and Englishviii, 108 leavesToday, the pace of the technological development and improvements has compelled the development of new and more complex applications. The obligatory of application development in a short time to rapidly changing requirements causes skipping of some stages, mostly the testing stage, in the software development cycle thus, leads to the production of applications with defects. These defects are, later, discovered by intruders to be used to penetrate into computer systems. Current security technologies, such as firewalls, intrusion detection systems, honeypots, network-based antivirus systems, are insufficient to protect systems against those, continuously increasing and rapid-spreading attacks. Intrusion Prevention System (IPS) is a new technology developed to block today.s application-specific, data-driven attacks that spread in the speed of communication. IPS is the evolved and integrated state of the existing technologies; it is not a new approach to network security. In this thesis, IPS products of various computer security appliance developer companies have been analyzed in details. At the end of these analyses, the requirements of network-based IPSs have been identified and an architecture that fits those requirements has been proposed. Also, a sample network-based IPS has been developed by modifying the open source application Snort

Intrusion Prevention through Optimal Stopping

We study automated intrusion prevention using reinforcement learning. Following a novel approach, we formulate the problem of intrusion prevention as an (optimal) multiple stopping problem. This formulation gives us insight into the structure of optimal policies, which we show to have threshold properties. For most practical cases, it is not feasible to obtain an optimal defender policy using dynamic programming. We therefore develop a reinforcement learning approach to approximate an optimal threshold policy. We introduce T-SPSA, an efficient reinforcement learning algorithm that learns threshold policies through stochastic approximation. We show that T-SPSA outperforms state-of-the-art algorithms for our use case. Our overall method for learning and validating policies includes two systems: a simulation system where defender policies are incrementally learned and an emulation system where statistics are produced that drive simulation runs and where learned policies are evaluated. We show that this approach can produce effective defender policies for a practical IT infrastructure.Comment: Preprint; Submitted to IEEE for review. major revision 1/4 2022. arXiv admin note: substantial text overlap with arXiv:2106.0716