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Security and privacy trends in the industrial internet of things
This book, written by leaders in the protection field of critical infrastructures, provides an extended overview of the technological and operative advantages together with the security problems and challenges of the new paradigm of the Internet of Things in today's industry, also known as the Industry Internet of Things (IIoT). The incorporation of the new embedded technologies and the interconnected networking advances in the automation and monitoring processes, certainly multiplies the functional complexities of the underlying control system, whilst increasing security and privacy risks. The critical nature of the application context and its relevance for the well-being of citizens and their economy, attracts the attention of multiple, advanced attackers, with stealthy abilities to evade security policies, ex-filter information or exploit vulnerabilities. Some real-life events and registers in CERTs have already clearly demonstrated how the control industry can become vulnerable to multiple types of advanced threats whose focus consists in hitting the safety and security of the control processes. This book, therefore, comprises a detailed spectrum of research papers with highly analytical content and actuation procedures to cover the relevant security and privacy issues such as data protection, awareness, response and resilience, all of them working at optimal times. Readers will be able to comprehend the construction problems of the fourth industrial revolution and are introduced to effective, lightweight protection solutions which can be integrated as part of the new IIoT-based monitoring ecosystem.
Book
Wireshark for security professionals
Master Wireshark to solve real-world security problems If you don't already use Wireshark for a wide range of information security tasks, you will after this book. Mature and powerful, Wireshark is commonly used to find root cause of challenging network issues. This book extends that power to information security professionals, complete with a downloadable, virtual lab environment. Wireshark for Security Professionals covers both offensive and defensive concepts that can be applied to essentially any InfoSec role. Whether into network security, malware analysis, intrusion detection, or penetration testing, this book demonstrates Wireshark through relevant and useful examples. Master Wireshark through both lab scenarios and exercises. Early in the book, a virtual lab environment is provided for the purpose of getting hands-on experience with Wireshark. Wireshark is combined with two popular platforms: Kali, the security-focused Linux distribution, and the Metasploit Framework, the open-source framework for security testing. Lab-based virtual systems generate network traffic for analysis, investigation and demonstration. In addition to following along with the labs you will be challenged with end-of-chapter exercises to expand on covered material. Lastly, this book explores Wireshark with Lua, the light-weight programming language. Lua allows you to extend and customize Wireshark's features for your needs as a security professional. Lua source code is available both in the book and online. Lua code and lab source code are available online through GitHub, which the book also introduces. The book's final two chapters greatly draw on Lua and TShark, the command-line interface of Wireshark. By the end of the book you will gain the following: -Master the basics of Wireshark -Explore the virtual w4sp-lab environment that mimics a real-world network -Gain experience using the Debian-based Kali OS among other systems -Understand the technical details behind network attacks -Execute exploitation and grasp offensive and defensive activities, exploring them through Wireshark -Employ Lua to extend Wireshark features and create useful scripts To sum up, the book content, labs and online material, coupled with many referenced sources of PCAP traces, together present a dynamic and robust manual for information security professionals seeking to leverage Wireshark
eBook
Hacking web apps : detecting and preventing web application security problems
How can an information security professional keep up with all of the hacks, attacks, and exploits on the Web? One way is to read Hacking Web Apps. The content for this book has been selected by author Mike Shema to make sure that we are covering the most vicious attacks out there. Not only does Mike let you in on the anatomy of these attacks, but he also tells you how to get rid of these worms, trojans, and botnets and how to defend against them in the future. Countermeasures are detailed so that you can fight against similar attacks as they evolve. Attacks featured in this book include: . SQL Injection . Cross Site Scripting . Logic Attacks . Server Misconfigurations . Predictable Pages . Web of Distrust . Breaking Authentication Schemes . HTML5 Security Breaches . Attacks on Mobile Apps Even if you don't develop web sites or write HTML, Hacking Web Apps can still help you learn how sites are attacked-as well as the best way to defend against these attacks. Plus, Hacking Web Apps gives you detailed steps to make the web browser - sometimes your last line of defense - more secure. More and more data, from finances to photos, is moving into web applications. How much can you trust that data to be accessible from a web browser anywhere and safe at the same time? Some of the most damaging hacks to a web site can be executed with nothing more than a web browser and a little knowledge of HTML. Learn about the most common threats and how to stop them, including HTML Injection, XSS, Cross Site Request Forgery, SQL Injection, Breaking Authentication Schemes, Logic Attacks, Web of Distrust, Browser Hacks and many more.
eBook
Mastering reverse engineering : re-engineer your ethical hacking skills
If you want to analyze software in order to exploit its weaknesses and strengthen its defenses, then you should explore reverse engineering. Reverse Engineering is a hackerfriendly tool used to expose security flaws and questionable privacy practices.In this book, you will learn how to analyse software even without having access to its source code or design documents. You will start off by learning the low-level language used to communicate with the computer and then move on to covering reverse engineering techniques. Next, you will explore analysis techniques using real-world tools such as IDA Pro and x86dbg. As you progress through the chapters, you will walk through use cases encountered in reverse engineering, such as encryption and compression, used to obfuscate code, and how to to identify and overcome anti-debugging and anti-analysis tricks. Lastly, you will learn how to analyse other types of files that contain code. By the end of this book, you will have the confidence to perform reverse engineering. -- back cover.
Book
Enhancing intrusion detection: a hybrid machine and deep learning approach
The volume of data transferred across communication infrastructures has recently increased due to technological advancements in cloud computing, the Internet of Things (IoT), and automobile networks. The network systems transmit diverse and heterogeneous data in dispersed environments as communication technology develops. The communications using these networks and daily interactions depend on network security systems to provide secure and reliable information. On the other hand, attackers have increased their efforts to render systems on networks susceptible. An efficient intrusion detection system is essential since technological advancements embark on new kinds of attacks and security limitations. This paper implements a hybrid model for Intrusion Detection (ID) with Machine Learning (ML) and Deep Learning (DL) techniques to tackle these limitations. The proposed model makes use of Extreme Gradient Boosting (XGBoost) and convolutional neural networks (CNN) for feature extraction and then combines each of these with long short-term memory networks (LSTM) for classification. Four benchmark datasets CIC IDS 2017, UNSW NB15, NSL KDD, and WSN DS were used to train the model for binary and multi-class classification. With the increase in feature dimensions, current intrusion detection systems have trouble identifying new threats due to low test accuracy scores. To narrow down each dataset’s feature space, XGBoost, and CNN feature selection algorithms are used in this work for each separate model. The experimental findings demonstrate a high detection rate and good accuracy with a relatively low False Acceptance Rate (FAR) to prove the usefulness of the proposed hybrid model.
Journal Article
A Comprehensive Review of Cyber Security Vulnerabilities, Threats, Attacks, and Solutions
Internet usage has grown exponentially, with individuals and companies performing multiple daily transactions in cyberspace rather than in the real world. The coronavirus (COVID-19) pandemic has accelerated this process. As a result of the widespread usage of the digital environment, traditional crimes have also shifted to the digital space. Emerging technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and cryptocurrencies are raising security concerns in cyberspace. Recently, cyber criminals have started to use cyber attacks as a service to automate attacks and leverage their impact. Attackers exploit vulnerabilities that exist in hardware, software, and communication layers. Various types of cyber attacks include distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware. Due to new-generation attacks and evasion techniques, traditional protection systems such as firewalls, intrusion detection systems, antivirus software, access control lists, etc., are no longer effective in detecting these sophisticated attacks. Therefore, there is an urgent need to find innovative and more feasible solutions to prevent cyber attacks. The paper first extensively explains the main reasons for cyber attacks. Then, it reviews the most recent attacks, attack patterns, and detection techniques. Thirdly, the article discusses contemporary technical and nontechnical solutions for recognizing attacks in advance. Using trending technologies such as machine learning, deep learning, cloud platforms, big data, and blockchain can be a promising solution for current and future cyber attacks. These technological solutions may assist in detecting malware, intrusion detection, spam identification, DNS attack classification, fraud detection, recognizing hidden channels, and distinguishing advanced persistent threats. However, some promising solutions, especially machine learning and deep learning, are not resistant to evasion techniques, which must be considered when proposing solutions against intelligent cyber attacks.
Journal Article
An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems
The Industrial Internet of Things (IIoT) promises to deliver innovative business models across multiple domains by providing ubiquitous connectivity, intelligent data, predictive analytics, and decision-making systems for improved market performance. However, traditional IIoT architectures are highly susceptible to many security vulnerabilities and network intrusions, which bring challenges such as lack of privacy, integrity, trust, and centralization. This research aims to implement an Artificial Intelligence-based Lightweight Blockchain Security Model (AILBSM) to ensure privacy and security of IIoT systems. This novel model is meant to address issues that can occur with security and privacy when dealing with Cloud-based IIoT systems that handle data in the Cloud or on the Edge of Networks (on-device). The novel contribution of this paper is that it combines the advantages of both lightweight blockchain and Convivial Optimized Sprinter Neural Network (COSNN) based AI mechanisms with simplified and improved security operations. Here, the significant impact of attacks is reduced by transforming features into encoded data using an Authentic Intrinsic Analysis (AIA) model. Extensive experiments are conducted to validate this system using various attack datasets. In addition, the results of privacy protection and AI mechanisms are evaluated separately and compared using various indicators. By using the proposed AILBSM framework, the execution time is minimized to 0.6 seconds, the overall classification accuracy is improved to 99.8%, and detection performance is increased to 99.7%. Due to the inclusion of auto-encoder based transformation and blockchain authentication, the anomaly detection performance of the proposed model is highly improved, when compared to other techniques.
Journal Article