The Internet of Things (IoT) paradigm has displayed tremendous growth in recent years, resulting in innovations like Industry 4.0 and smart environments that provide improvements to efficiency, management of assets and facilitate intelligent decision making. However, these benefits are offset by considerable cybersecurity concerns that arise due to inherent vulnerabilities, which hinder IoT-based systems' Confidentiality, Integrity, and Availability. Security vulnerabilities can be detected through the application of penetration testing, and specifically, a subset of the information-gathering stage, known as vulnerability identification. Yet, existing penetration testing solutions can not discover zero-day vulnerabilities from IoT environments, due to the diversity of generated data, hardware constraints, and environmental complexity. Thus, it is imperative to develop effective penetration testing solutions for the detection of vulnerabilities in smart IoT environments. In this paper, we propose a deep learning-based penetration testing framework, namely Long Short-Term Memory Recurrent Neural Network-Enabled Vulnerability Identification (LSTM-EVI). We utilize this framework through a novel cybersecurity-oriented testbed, which is a smart airport-based testbed comprised of both physical and virtual elements. The framework was evaluated using this testbed and on real-time data sources. Our results revealed that the proposed framework achieves about 99% detection accuracy for scanning attacks, outperforming other four peer techniques.
IP cameras have always been part of the Internet of Things (IoT) and are among the most widely used devices in both home and professional environments. Unfortunately, the vulnerabilities of IP cameras have attracted malicious activities. For example, in 2016, a massive attack resulted in thousands of cameras and IoT devices being breached and used to create a botnet. Given this history and the extremely sensitive nature of the data these devices have access to, it is natural to question what security measures are in place today. In this paper, a vulnerability assessment and penetration testing…
The factories of the future require efficient interconnection of their physical machines into the cyber space to cope with the emerging need of an increased uptime of machines, higher performance rates, an improved level of productivity and a collective collaboration along the supply chain. With the rapid growth of the Internet of Things (IoT), and its application in industrial areas, the so called Industrial Internet of Things (IIoT)/Industry 4.0 emerged. However, further to the rapid growth of IoT/IIoT systems, cyber attacks are an emerging threat and simple manual security testing can often…
It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of machine learning in countering these susceptibilities. Fol…
CONTEXT: Applying vulnerability detection techniques is one of many tasks using the limited resources of a software project. OBJECTIVE: The goal of this research is to assist managers and other decision-makers in making informed choices about the use of software vulnerability detection techniques through an empirical study of the efficiency and effectiveness of four techniques on a Java-based web application. METHOD: We apply four different categories of vulnerability detection techniques \textendash~ systematic manual penetration testing (SMPT), exploratory manual penetration testing (EMPT), …
Network penetration testing identifies the exploits and vulnerabilities those exist within computer network infrastructure and help to confirm the security measures. The objective of this paper is to explain methodology and methods behind penetration testing and illustrate remedies over it, which will provide substantial value for network security Penetration testing should model real world attacks as closely as possible. An authorized and scheduled penetration testing will probably detected by IDS (Intrusion Detection System). Network penetration testing is done by either or manual automated …
Decoupling Reconnaissance and Exploitation: Measuring the Capability Boundaries of LLM-Based Web Penetration Testing
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州先进研究院）；Nanjing University of Science and Technology（南京理工大学）；Research Institute, Runjian Co., Ltd（润杰有限公司研究院）
IBM T.J. Watson Research Center（IBM托马斯·J·沃森研究中心）；IBM Security（IBM安全事业部）
Organizations struggle to handle sheer number of vulnerabilities in their cloud environments. The de facto methodology used for prioritizing vulnerabilities is to use Common Vulnerability Scoring System (CVSS). However, CVSS has inherent limitations that makes it not ideal for prioritization. In this work, we propose a new way of prioritizing vulnerabilities. Our approach is inspired by how offensive security practitioners perform penetration testing. We evaluate our approach with a real world case study for a large client, and the accuracy of machine learning to automate the process end to en…
Enhancing Security Testing Software for Systems that Cannot be Subjected to the Risks of Penetration Testing Through the Incorporation of Multi-threading and and Other Capabilities
Deakin University（迪肯大学）；RMIT University（皇家墨尔本理工大学）；The University of Adelaide（阿德莱德大学）；Cyber Security Cooperative Research Centre（网络安全合作研究中心）
Internet of Things (IoT) based applications face an increasing number of potential security risks, which need to be systematically assessed and addressed. Expert-based manual assessment of IoT security is a predominant approach, which is usually inefficient. To address this problem, we propose an automated security assessment framework for IoT networks. Our framework first leverages machine learning and natural language processing to analyze vulnerability descriptions for predicting vulnerability metrics. The predicted metrics are then input into a two-layered graphical security model, which c…
Discovering vulnerabilities in applications of real-world complexity is a daunting task: a vulnerability may affect a single line of code, and yet it compromises the security of the entire application. Even worse, vulnerabilities may manifest only in exceptional circumstances that do not occur in the normal operation of the application. It is widely recognized that state-of-the-art penetration testing tools play a crucial role, and are routinely used, to dig up vulnerabilities. Yet penetration testing is still primarily a human-driven activity, and its effectiveness still depends on the skills…
Widespread interest in the emerging area of predictive analytics is driving industries such as manufacturing to explore new approaches to the collection and management of data provided from Industrial Internet of Things (IIoT) devices. Often, analytics processing for Business Intelligence (BI) is an intensive task, and it also presents both an opportunity for competitive advantage as well as a security vulnerability in terms of the potential for losing Intellectual Property (IP). This article explores two approaches to securing BI in the manufacturing domain. Simulation results indicate that a…
Penetration testing (or pentesting) is one of the widely used and important methodologies to assess the security of computer systems and networks. Traditional pentesting relies on the domain expert knowledge and requires considerable human effort all of which incurs a high cost. The automation can significantly improve the efficiency, availability and lower the cost of penetration testing. Existing approaches to the automation include those which map vulnerability scanner results to the corresponding exploit tools, and those addressing the pentesting as a planning problem expressed in terms of…
In multi-tier network systems, custom applications, Web services and platform environments, storing data and information assets becomes a challenge for any organisation. Although there are different methods to secure network systems, the best way to test the level of security is to conduct penetration testing. In this paper, we describe how we performed live penetration testing for a particular network, namely, 192.168.3.0/24 (Case Study) by identifying the system vulnerabilities to enable its penetration. After compromising the system, critical data (Flags) must be found, indicating our succe…
The integration of sensors and communication technology in power systems, known as the smart grid, is an emerging topic in science and technology. One of the critical issues in the smart grid is its increased vulnerability to cyber threats. As such, various types of threats and defense mechanisms are proposed in literature. This paper offers a bibliometric survey of research papers focused on the security aspects of Internet of Things (IoT) aided smart grids. To the best of the authors' knowledge, this is the very first bibliometric survey paper in this specific field. A bibliometric analysis …
Staying on the Attack Path: Structured State for Long-Horizon Automated Penetration Testing
Tianjin University（天津大学）；Changchun University of Science and Technology（长春理工大学）；Jilin Province Key Laboratory of Network and Information Security（吉林省网络与信息安全重点实验室）；Shihezi University（石河子大学）
Internet of Things (IoT) is a whole new ecosystem comprised of heterogeneous connected devices -i.e. computers, laptops, smart-phones and tablets as well as embedded devices and sensors-that communicate to deliver capabilities making our living, cities, transport, energy, and many other areas more intelligent. The main concerns raised from the IoT ecosystem are the devices poor support for patching/updating and the poor on-board computational power. A number of issues stem from this: inherent vulnerabilities and the inability to detect and defend against external attacks. Also, due to the natu…
Deloitte Consulting LLC（德勤咨询有限责任公司）；National Security Institute, Virginia Tech（弗吉尼亚理工大学国家安全研究所）；Deloitte & Touche LLP（德勤会计师事务所）
Reinforcement learning (RL) has been applied to attack graphs for penetration testing, however, trained agents do not reflect reality because the attack graphs lack operational nuances typically captured within the intelligence preparation of the battlefield (IPB) that include notions of (cyber) terrain. In particular, current practice constructs attack graphs exclusively using the Common Vulnerability Scoring System (CVSS) and its components. We present methods for constructing attack graphs using notions from IPB on cyber terrain analysis of obstacles, avenues of approach, key terrain, obser…
This paper investigates the security and privacy of Internet-connected children's smart toys through case studies of three commercially-available products. We conduct network and application vulnerability analyses of each toy using static and dynamic analysis techniques, including application binary decompilation and network monitoring. We discover several publicly undisclosed vulnerabilities that violate the Children's Online Privacy Protection Rule (COPPA) as well as the toys' individual privacy policies. These vulnerabilities, especially security flaws in network communications with first-p…
Zhejiang University of Technology（浙江工业大学）；Army Engineering University of PLA（中国人民解放军陆军工程大学）
Penetration testing (PT) is an efficient network testing and vulnerability mining tool by simulating a hacker's attack for valuable information applied in some areas. Compared with manual PT, intelligent PT has become a dominating mainstream due to less time-consuming and lower labor costs. Unfortunately, RL-based PT is still challenged in real exploitation scenarios because the agent's action space is usually high-dimensional discrete, thus leading to algorithm convergence difficulty. Besides, most PT methods still rely on the decisions of security experts. Addressing the challenges, for the …
The extensive networking of devices and the large amount of data generated from the Internet of Things (IoT) has brought security issues to the attention of the researcher. Java is the most common platform for embedded applications such as IoT, Wireless Sensors Networks (WSN), Near Field Communications (NFC) and Radio Frequency Identification (RFID). The object programming languages such as Java, SWIFT, PHP and C++ use garbage collection after any object run which creates security loophole for attacks such as Next Memory Address Occupation (NMAO), memory replay, Learning Tasks Behaviors (LTB).…
As the internet penetration rate in Africa increases, so does the proliferation of the Internet of Things (IoT) devices. Along with this growth in internet access is the risk of cyberattacks to vulnerable IoT devices mushrooming in the African cyberspace. One way to determine IoT vulnerabilities is to find open ports within Africa s cyberspace. Our research leverages Shodan search engine, a powerful tool for discovering IoT devices facing the public internet, to find open ports across Africa. We conduct an analysis of our findings, ranking countries from most to least vulnerable to cyberattack…
Guided Reasoning in LLM-Driven Penetration Testing Using Structured Attack Trees
Department of Electrical and Computer Engineering, Rochester Institute of Technology（罗切斯特理工学院电子与计算机工程系）；Institute for Informatics and Applied Technology, Gonzaga University（贡萨加大学信息学与应用技术研究所）
VEXAIoT: Autonomous IoT Vulnerability EXploitation using AI Agents
Department of Computer Science Tennessee Tech University Cookeville, TN, USA；School of Interdisciplinary Informatics University of Nebraska Omaha Omaha, NE, USA；1 Department of Computer Science, Tennessee Tech University, TN, USA.；2 School of Interdisciplinary Informatics, University of Nebraska Omaha, NE, USA.；3 Dept. of Mathematics \& Computer Science, University of North Carolina at Pembroke, NC, USA.
After the rise of E-commerce, social media and messenger bots, rapid developments have been made in the field of connecting things, gadgets, and devices, i.e, the Internet of Things (IoT). In the fast-paced lifestyle, it is very difficult to maintain multiple keys for traditional mechanical locks. Electromagnetic smart locks are a possible solution to this problem. To connect a smart lock with a key, Bluetooth Low Energy (BLE) protocol can be used. BLE protocol is vulnerable to Man-in-the-Middle (MITM) attack. Ensuring security over BLE is an ongoing challenge. This paper aims to analyze the M…
The resource-constrained nature of the Internet of Things (IoT) devices, poses a challenge in designing a secure, reliable, and particularly high-performance communication for this family of devices. Although side-channel resistant ciphers (either block cipher or stream cipher) are the well-suited solution to establish a guaranteed secure communication, the energy-intensive nature of these ciphers makes them undesirable for particularly lightweight IoT solutions. In this paper, we introduce ExTru, a novel encrypted communication protocol based on stream ciphers that adds a configurable switchi…
Internet of Things (IoT) devices are becoming increasingly important. These devices are often resource-limited, hindering rigorous enforcement of security policies. Assessing the vulnerability of IoT devices is an important problem, but analyzing their firmware is difficult for a variety of reasons, including requiring the purchase of devices. This paper finds that analyzing companion apps to these devices for clues to security vulnerabilities can be an effective strategy. Compared to device hardware and firmware, these apps are easy to download and analyze. A key finding of this study is that…
Some of the main challenges towards utilizing conventional cryptographic techniques in Internet of Things (IoT) include the need for generating secret keys for such a large-scale network, distributing the generated keys to all the devices, key storage as well as the vulnerability to security attacks when an adversary gets physical access to the devices. In this paper, a novel secret key generation method is proposed for IoTs that utilize the intrinsic randomness embedded in the devices' memories introduced in the manufacturing process. A fuzzy extractor structure using serially concatenated BC…
As a solution to protect and defend a system against inside attacks, many intrusion detection systems (IDSs) have been developed to identify and react to them for protecting a system. However, the core idea of an IDS is a reactive mechanism in nature even though it detects intrusions which have already been in the system. Hence, the reactive mechanisms would be way behind and not effective for the actions taken by agile and smart attackers. Due to the inherent limitation of an IDS with the reactive nature, intrusion prevention systems (IPSs) have been developed to thwart potential attackers an…
Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security
University of New Mexico（新墨西哥大学）；Florida International University（佛罗里达国际大学）；Florida Atlantic University（佛罗里达大西洋大学）；University of Washington（华盛顿大学）；Islamic Azad University（伊斯兰阿扎德大学）
Penetration testing is a well-established practical concept for the identification of potentially exploitable security weaknesses and an important component of a security audit. Providing a holistic security assessment for networks consisting of several hundreds hosts is hardly feasible though without some sort of mechanization. Mitigation, prioritizing counter-measures subject to a given budget, currently lacks a solid theoretical understanding and is hence more art than science. In this work, we propose the first approach for conducting comprehensive what-if analyses in order to reason about…
The Internet of Things, also known as the IoT, refers to the billions of devices around the world that are now connected to the Internet, collecting and sharing data. The amount of data collected through IoT sensors must be completely securely controlled. To protect the information collected by IoT sensors, a lightweight method called Discover the Flooding Attack-RPL (DFA-RPL) has been proposed. The proposed DFA-RPL method identifies intrusive nodes in several steps to exclude them from continuing routing operations. Thus, in the DFA-RPL method, it first builds a cluster and selects the most a…
