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"Local area networks (Computer networks) Security measures."
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Configuring Check Point NGX VPN-1/Firewall-1
Check Point NGX VPN-1/Firewall-1 is the next major release of Check Point's flagship firewall software product, which has over 750,000 registered users. The most significant changes to this release are in the areas of Route Based VPN, Directional VPN, Link Selection & Tunnel Management, Multiple Entry Points, Route Injection Mechanism, Wire Mode, and SecurePlatform Pro. Many of the new features focus on how to configure and manage Dynamic Routing rules, which are essential to keeping an enterprise network both available *and* secure. Demand for this book will be strong because Check Point is requiring all of its 3rd party developers to certify their products for this release.* Packed full with extensive coverage of features new to the product, allowing 3rd party partners to certify NGX add-on products quickly* Protect your network from both internal and external threats and learn to recognize future threats* All yuou need to securly and efficiently deploy, troubleshoot, and maintain Check Point NXG
eBook
Security risk management : building an information security risk management program from the ground up
The goal of Security Risk Management is to teach you practical techniques that will be used on a daily basis, while also explaining the fundamentals so you understand the rationale behind these practices. Security professionals often fall into the trap of telling the business that they need to fix something, but they can't explain why. This book will help you to break free from the so-called \"best practices\" argument by articulating risk exposures in business terms. You will learn techniques for how to perform risk assessments for new IT projects, how to efficiently manage daily risk activities, and how to qualify the current risk level for presentation to executive level management. While other books focus entirely on risk analysis methods, this is the first comprehensive guide for managing security risks. Named a 2011 Best Governance and ISMS Book by InfoSec Reviews Includes case studies to provide hands-on experience using risk assessment tools to calculate the costs and benefits of any security investmentExplores each phase of the risk management lifecycle, focusing on policies and assessment processes that should be used to properly assess and mitigate riskPresents a roadmap for designing and implementing a security risk management program
eBook
Detection of Management-Frames-Based Denial-of-Service Attack in Wireless LAN Network Using Artificial Neural Network
Wireless Local Area Networks (WLANs) have become an increasingly popular mode of communication and networking, with a wide range of applications in various fields. However, the increasing popularity of WLANs has also led to an increase in security threats, including denial of service (DoS) attacks. In this study, management-frames-based DoS attacks, in which the attacker floods the network with management frames, are particularly concerning as they can cause widespread disruptions in the network. Attacks known as denial of service (DoS) can target wireless LANs. None of the wireless security mechanisms in use today contemplate defence against them. At the MAC layer, there are multiple vulnerabilities that can be exploited to launch DoS attacks. This paper focuses on designing and developing an artificial neural network (NN) scheme for detecting management-frames-based DoS attacks. The proposed scheme aims to effectively detect fake de-authentication/disassociation frames and improve network performance by avoiding communication interruption caused by such attacks. The proposed NN scheme leverages machine learning techniques to analyse patterns and features in the management frames exchanged between wireless devices. By training the NN, the system can learn to accurately detect potential DoS attacks. This approach offers a more sophisticated and effective solution to the problem of DoS attacks in wireless LANs and has the potential to significantly enhance the security and reliability of these networks. According to the experimental results, the proposed technique exhibits higher effectiveness in detection compared to existing methods, as evidenced by a significantly increased true positive rate and a decreased false positive rate.
Journal Article
Unauthorised access
The first guide to planning and performing a physical penetration test on your computer's securityMost IT security teams concentrate on keeping networks and systems safe from attacks from the outside-but what if your attacker was on the inside? While nearly all IT teams perform a variety of network and application penetration testing procedures, an audit and test of the physical location has not been as prevalent. IT teams are now increasingly requesting physical penetration tests, but there is little available in terms of training. The goal of the test is to demonstrate any deficiencies in operating procedures concerning physical security.Featuring a Foreword written by world-renowned hacker Kevin D. Mitnick and lead author of The Art of Intrusion and The Art of Deception, this book is the first guide to planning and performing a physical penetration test. Inside, IT security expert Wil Allsopp guides you through the entire process from gathering intelligence, getting inside, dealing with threats, staying hidden (often in plain sight), and getting access to networks and data.Teaches IT security teams how to break into their own facility in order to defend against such attacks, which is often overlooked by IT security teams but is of critical importanceDeals with intelligence gathering, such as getting access building blueprints and satellite imagery, hacking security cameras, planting bugs, and eavesdropping on security channelsIncludes safeguards for consultants paid to probe facilities unbeknown to staffCovers preparing the report and presenting it to managementIn order to defend data, you need to think like a thief-let Unauthorised Accessshow you how to get inside.
eBook
Rule-Based System with Machine Learning Support for Detecting Anomalies in 5G WLANs
The purpose of this paper is to design and implement a complete system for monitoring and detecting attacks and anomalies in 5G wireless local area networks. Regrettably, the development of most open source systems has been stopped, making them unable to detect emerging forms of threats. The system provides a modular framework to create and add new detection rules as new attacks emerge. The system is based on packet analysis modules and rules and incorporates machine learning models to enhance its efficiency. The use of rule-based detection establishes a strong basis for the identification of recognized threats, whereas the additional implementation of machine learning models enables the detection of new and emerging attacks at an early stage. Therefore, the ultimate aim is to create a tool that constantly evolves by integrating novel attack detection techniques. The efficiency of the system is proven experimentally with accuracy levels up to 98.57% and precision as well as recall scores as high as 92%.
Journal Article
WiMAX Security and Quality of Service
WiMAX is the first standard technology to deliver true broadband mobility at speeds that enable powerful multimedia applications such as Voice over Internet Protocol (VoIP), online gaming, mobile TV, and personalized infotainment. WiMAX Security and Quality of Service, focuses on the interdisciplinary subject of advanced Security and Quality of Service (QoS) in WiMAX wireless telecommunication systems including its models, standards, implementations, and applications. Split into 4 parts, Part A of the book is an end-to-end overview of the WiMAX architecture, protocol, and system requirements. Security is an essential element in the wireless world and Part B is fully dedicated to this topic. Part C provides an in depth analysis of QoS, including mobility management in WiMAX. Finally, Part D introduces the reader to advanced and future topics. One of the first texts to cover security, QoS and deployments of WiMAX in the same book. Introduces the primary concepts of the interdisciplinary nature of WiMAX security and QoS, and also includes discussion of hot topics in the field. Written for engineers and researchers, answering practical questions from industry and the experimental field in academia. Explains how WiMAX applications’ security and QoS are interconnected and interworked among the cross layers.
eBook
Intelligent Resource Allocation Scheme Using Reinforcement Learning for Efficient Data Transmission in VANET
Vehicular ad hoc networks (VANETs) use multiple channels to communicate using wireless access in vehicular environment (WAVE) standards to provide a variety of vehicle-related applications. The current IEEE 802.11p WAVE communication channel structure is composed of one control channel (CCH) and several service channels (SCHs). SCHs are used for non-safety data transmission, while the CCH is used for broadcasting beacons, control, and safety. WAVE devices transmit data that alternate between CCHs and SCHs, and each channel is active for a duration called the CCH interval (CCHI) and SCH interval (SCHI), respectively. Currently, both intervals are fixed at 50 ms. However, fixed-length intervals cannot effectively respond to dynamically changing traffic loads. Additionally, when many vehicles are simultaneously using the limited channel resources for data transmission, the network performance significantly degrades due to numerous packet collisions. Herein, we propose an adaptive resource allocation technique for efficient data transmission. The technique dynamically adjusts the SCHI and CCHI to improve network performance. Moreover, to reduce data collisions and optimize the network’s backoff distribution, the proposed scheme applies reinforcement learning (RL) to provide an intelligent channel access algorithm. The simulation results demonstrate that the proposed scheme can ensure high throughputs and low transmission delays.
Journal Article
StegoEDCA: An Efficient Covert Channel for Smart Grids Based on IEEE 802.11e Standard
Smart grids are continuously evolving, incorporating modern technologies such as Wi-Fi, Zigbee, LoRaWAN or BLE. Wi-Fi are commonly used to transmit data from measurement systems, distribution control and monitoring systems, as well as network protection systems. However, since Wi-Fi networks primarily operate on unlicensed frequency bands, this introduces significant security risks for sensitive data transmission. In this paper, we propose a novel and highly efficient covert channels that utilize IEEE 802.11 Enhanced Distributed Channel Access (EDCA) for data transmission. It is also the first ever covert channel that employ three or four independent covert mechanisms to enhance operational efficiency. The proposed mechanism is also the first to exploit the Transmission Opportunity (TXOP) period and the access categories of the EDCA function. The protocol was developed and tested using the ns-3 simulator, achieving excellent performance results. Its efficiency remains consistent even under heavy network load with additional background traffic. These covert channels provide an innovative solution for securely transmitting large volumes of data within the smart grid.
Journal Article
Enhancing Network Security: A Machine Learning-Based Approach for Detecting and Mitigating Krack and Kr00k Attacks in IEEE 802.11
The rise in internet users has brought with it the impending threat of cybercrime as the Internet of Things (IoT) increases and the introduction of 5G technologies continues to transform our digital world. It is now essential to protect communication networks from illegal intrusions to guarantee data integrity and user privacy. In this situation, machine learning techniques used in data mining have proven to be effective tools for constructing intrusion detection systems (IDS) and improving their precision. We use the well-known AWID3 dataset, a comprehensive collection of wireless network traffic, to investigate the effectiveness of machine learning in enhancing network security. Our work primarily concentrates on Krack and Kr00k attacks, which target the most recent and dangerous flaws in IEEE 802.11 protocols. Through diligent implementation, we were able to successfully identify these threats using an IDS model that is based on machine learning. Notably, the resilience of our method was demonstrated by our ensemble classifier’s astounding 99% success rate in detecting the Krack attack. The effectiveness of our suggested remedy was further demonstrated by the high accuracy rate of 96.7% displayed by our neural network-based model in recognizing instances of the Kr00k attack. Our research shows the potential for considerably boosting network security in the face of new threats by leveraging the capabilities of machine learning and a diversified dataset. Our findings open the door for stronger, more proactive security measures to protect IEEE. 802.11 networks’ integrity, resulting in a safer online environment for all users.
Journal Article