Biblio
Over the past decade, distributed CSMA, which forms the basis for WiFi, has been deployed ubiquitously to provide seamless and high-speed mobile internet access. However, distributed CSMA might not be ideal for future IoT/M2M applications, where the density of connected devices/sensors/controllers is expected to be orders of magnitude higher than that in present wireless networks. In such high-density networks, the overhead associated with completely distributed MAC protocols will become a bottleneck. Moreover, IoT communications are likely to have strict QoS requirements, for which the `best-effort' scheduling by present WiFi networks may be unsuitable. This calls for a clean-slate redesign of the wireless MAC taking into account the requirements for future IoT/M2M networks. In this paper, we propose a reservation-based (for minimal overhead) wireless MAC designed specifically with IoT/M2M applications in mind.
With the help of technological advancements in the last decade, it has become much easier to extensively and remotely observe medical conditions of the patients through wearable biosensors that act as connected nodes on Body Area Networks (BANs). Sensitive nature of the critical data captured and communicated via wireless medium makes it extremely important to process it as securely as possible. In this regard, lightweight security mechanisms are needed to overcome the hardware resource restrictions of biosensors. Random and secure cryptographic key generation and agreement among the biosensors take place at the core of these security mechanisms. In this paper, we propose the SKA-PSAR (Augmented Randomness for Secure Key Agreement using Physiological Signals) system to produce highly random cryptographic keys for the biosensors to secure communication in BANs. Similar to its predecessor SKA-PS protocol by Karaoglan Altop et al., SKA-PSAR also employs physiological signals, such as heart rate and blood pressure, as inputs for the keys and utilizes the set reconciliation mechanism as basic building block. Novel quantization and binarization methods of the proposed SKA-PSAR system distinguish it from SKA-PS by increasing the randomness of the generated keys. Additionally, SKA-PSAR generated cryptographic keys have distinctive and time variant characteristics as well as long enough bit sizes that provides resistance against cryptographic attacks. Moreover, correct key generation rate is above 98% with respect to most of the system parameters, and false key generation rate of 0% have been obtained for all system parameters.
Anomaly detection is one of the research hotspots in Bitcoin transaction data analysis. In view of the existing research that only considers the transaction as an isolated node when extracting features, but has not yet used the network structure to dig deep into the node information, a bitcoin abnormal transaction detection method that combines the node’s own features and the neighborhood features is proposed. Based on the formation mechanism of the interactive relationship in the transaction network, first of all, according to a certain path selection probability, the features of the neighbohood nodes are extracted by way of random walk, and then the node’s own features and the neighboring features are fused to use the network structure to mine potential node information. Finally, an unsupervised detection algorithm is used to rank the transaction points on the constructed feature set to find abnormal transactions. Experimental results show that, compared with the existing feature extraction methods, feature fusion improves the ability to detect abnormal transactions.
We investigate what we call the "Bitcoin Generator Scam" (BGS), a simple system in which the scammers promise to "generate" new bitcoins using the ones that were sent to them. A typical offer will suggest that, for a small fee, one could receive within minutes twice the amount of bitcoins submitted. BGS is clearly not a very sophisticated attack. The modus operandi is simply to put up some web page on which to find the address to send the money and wait for the payback. The pages are then indexed by search engines, and ready to find for victims looking for free bitcoins. We describe here a generic system to find and analyze scams such as BGS. We have trained a classifier to detect these pages, and we have a crawler searching for instances using a series of search engines. We then monitor the instances that we find to trace payments and bitcoin addresses that are being used over time. Unlike most bitcoin-based scam monitoring systems, we do not rely on analyzing transactions on the blockchain to find scam instances. Instead, we proactively find these instances through the web pages advertising the scam. Thus our system is able to find addresses with very few transactions, or even none at all. Indeed, over half of the addresses that have eventually received funds were detected before receiving any transactions. The data for this paper was collected over four months, from November 2019 to February 2020. We have found more than 1,300 addresses directly associated with the scam, hosted on over 500 domains. Overall, these addresses have received (at least) over 5 million USD to the scam, with an average of 47.3 USD per transaction.
Internet of Things (IoT) is a revolutionary expandable network which has brought many advantages, improving the Quality of Life (QoL) of individuals. However, IoT carries dangers, due to the fact that hackers have the ability to find security gaps in users' IoT devices, which are not still secure enough and hence, intrude into them for malicious activities. As a result, they can control many connected devices in an IoT network, turning IoT into Botnet of Things (BoT). In a botnet, hackers can launch several types of attacks, such as the well known attacks of Distributed Denial of Service (DDoS) and Man in the Middle (MitM), and/or spread various types of malicious software (malware) to the compromised devices of the IoT network. In this paper, we propose a novel hybrid Artificial Intelligence (AI)-powered honeynet for enhanced IoT botnet detection rate with the use of Cloud Computing (CC). This upcoming security mechanism makes use of Machine Learning (ML) techniques like the Logistic Regression (LR) in order to predict potential botnet existence. It can also be adopted by other conventional security architectures in order to intercept hackers the creation of large botnets for malicious actions.
Through analysis of sessions in which files were created and downloaded on three Cowrie SSH/Telnet honeypots, we find that IoT botnets are by far the most common source of malware on connected systems with weak credentials. We detail our honeypot configuration and describe a simple method for listing near-identical malicious login sessions using edit distance. A large number of IoT botnets attack our honeypots, but the malicious sessions which download botnet software to the honeypot are almost all nearly identical to one of two common attack patterns. It is apparent that the Mirai worm is still the dominant botnet software, but has been expanded and modified by other hackers. We also find that the same loader devices deploy several different botnet malware strains to the honeypot over the course of a 40 day period, suggesting multiple botnet deployments from the same source. We conclude that Mirai continues to be adapted but can be effectively tracked using medium interaction honeypots such as Cowrie.
In recent years, attacks against cyber-physical systems have become increasingly frequent and widespread. The inventiveness of such attacks increases significantly. In particular, zero-day attacks are widely used. The rapid development of the industrial Internet of things, the expansion of the application areas of service robots, the advent of the Internet of vehicles and the Internet of military things have led to a significant increase of attention to deceptive attacks. Especially great threat is posed by deceptive attacks that do not use hiding malicious components. Such attacks can naturally be used against robotic systems. In this paper, we consider an approach to the development of an intrusion detection system for closed-loop robotic systems. The system is based on an abnormal behavioral pattern detection technique. The system can be used for detection of zero-day deceptive attacks. We provide an experimental comparison of our approach and other behavior-based intrusion detection systems.
Malware is any software that causes harm to the user information, computer systems or network. Modern computing and internet systems are facing increase in malware threats from the internet. It is observed that different malware follows the same patterns in their structure with minimal alterations. The type of threats has evolved, from file-based malware to fileless malware, such kind of threats are also known as Advance Volatile Threat (AVT). Fileless malware is complex and evasive, exploiting pre-installed trusted programs to infiltrate information with its malicious intent. Fileless malware is designed to run in system memory with a very small footprint, leaving no artifacts on physical hard drives. Traditional antivirus signatures and heuristic analysis are unable to detect this kind of malware due to its sophisticated and evasive nature. This paper provides information relating to detection, mitigation and analysis for such kind of threat.
The relevance of data protection is related to the intensive informatization of various aspects of society and the need to prevent unauthorized access to them. World spending on ensuring information security (IS) for the current state: expenses in the field of IS today amount to \$81.7 billion. Expenditure forecast by 2020: about \$105 billion [1]. Information protection of military facilities is the most critical in the public sector, in the non-state - financial organizations is one of the leaders in spending on information protection. An example of the importance of IS research is the Trojan encoder WannaCry, which infected hundreds of thousands of computers around the world, attacks are recorded in more than 116 countries. The attack of the encoder of WannaCry (Wana Decryptor) happens through a vulnerability in service Server Message Block (protocol of network access to file systems) of Windows OS. Then, a rootkit (a set of malware) was installed on the infected system, using which the attackers launched an encryption program. Then each vulnerable computer could become infected with another infected device within one local network. Due to these attacks, about \$70,000 was lost (according to data from 18.05.2017) [2]. It is assumed in the presented work, that the software level of information protection is fundamentally insufficient to ensure the stable functioning of critical objects. This is due to the possible hardware implementation of undocumented instructions, discussed later. The complexity of computing systems and the degree of integration of their components are constantly growing. Therefore, monitoring the operation of the computer hardware is necessary to achieve the maximum degree of protection, in particular, data processing methods.
The use of a very wide windows operating system is undeniably also followed by increasing attacks on the operating system. Universal Serial Bus (USB) is one of the mechanisms used by many people with plug and play functionality that is very easy to use, making data transfers fast and easy compared to other hardware. Some research shows that the Windows operating system has weaknesses so that it is often exploited by using various attacks and malware. There are various methods used to exploit the Windows operating system, one of them by using a USB device. By using a USB device, a criminal can plant a backdoor reverse shell to exploit the victim's computer just by connecting the USB device to the victim's computer without being noticed. This research was conducted by planting a reverse shell backdoor through a USB device to exploit the victim's device, especially the webcam and microphone device on the target computer. From 35 experiments that have been carried out, it was found that 83% of spying attacks using USB devices on the Windows operating system were successfully carried out.
Cloud-based payments, virtual car keys, and digital rights management are examples of consumer electronics applications that use secure software. White-box implementations of the Advanced Encryption Standard (AES) are important building blocks of secure software systems, and the attack of Billet, Gilbert, and Ech-Chatbi (BGE) is a well-known attack on such implementations. A drawback from the adversary’s or security tester’s perspective is that manual reverse engineering of the implementation is required before the BGE attack can be applied. This paper presents a method to automate the BGE attack on a class of white-box AES implementations with a specific type of external encoding. The new method was implemented and applied successfully to a CHES 2016 capture the flag challenge.
We consider different models of malicious multiple access channels, especially for binary adder channel and for A-channel, and show how they can be used for the reformulation of digital fingerprinting coding problems. In particular, we propose a new model of multimedia fingerprinting coding. In the new model, not only zeroes and plus/minus ones but arbitrary coefficients of linear combinations of noise-like signals for forming watermarks (digital fingerprints) can be used. This modification allows dramatically increase the possible number of users with the property that if t or less malicious users create a forge digital fingerprint then a dealer of the system can find all of them with zero-error probability. We show how arisen problems are related to the compressed sensing problem.
The open-source nature of the Android OS makes it possible for manufacturers to ship custom versions of the OS along with a set of pre-installed apps, often for product differentiation. Some device vendors have recently come under scrutiny for potentially invasive private data collection practices and other potentially harmful or unwanted behavior of the preinstalled apps on their devices. Yet, the landscape of preinstalled software in Android has largely remained unexplored, particularly in terms of the security and privacy implications of such customizations. In this paper, we present the first large- scale study of pre-installed software on Android devices from more than 200 vendors. Our work relies on a large dataset of real-world Android firmware acquired worldwide using crowd-sourcing methods. This allows us to answer questions related to the stakeholders involved in the supply chain, from device manufacturers and mobile network operators to third- party organizations like advertising and tracking services, and social network platforms. Our study allows us to also uncover relationships between these actors, which seem to revolve primarily around advertising and data-driven services. Overall, the supply chain around Android's open source model lacks transparency and has facilitated potentially harmful behaviors and backdoored access to sensitive data and services without user consent or awareness. We conclude the paper with recommendations to improve transparency, attribution, and accountability in the Android ecosystem.
The push for data sharing and data processing across organisational boundaries creates challenges at many levels of the software stack. Data sharing and processing rely on the participating parties agreeing on the permissible operations and expressing them into actionable contracts and policies. Converting these contracts and policies into a operational infrastructure is still a matter of research and therefore begs the question how should a digital data market place infrastructure look like? In this paper we investigate how communication fabric and applications can be tightly coupled into a multi-domain overlay network which enforces accountability. We prove our concepts with a prototype which shows how a simple workflow can run across organisational boundaries.
Black-box web application scanners are used to detect vulnerabilities in the web application without any knowledge of the source code. Recent research had shown their poor performance in detecting stored Cross-Site Scripting (XSS) and stored SQL Injection (SQLI). The detection efficiency of four black-box scanners on two testbeds, Wackopicko and Custom testbed Scanit (obtained from [5]), have been analyzed in this paper. The analysis showed that the scanners need to be improved for better detection of multi-step stored XSS and stored SQLI. This study involves the interaction between the selected scanners and the web application to measure their efficiency of inserting proper attack vectors in appropriate fields. The results of this research paper indicate that there is not much difference in terms of performance between open-source and commercial black-box scanners used in this research. However, it may depend on the policies and trust issues of the companies using them according to their needs. Some of the possible recommendations are provided to improve the detection rate of stored SQLI and stored XSS vulnerabilities in this paper. The study concludes that the state-of-the-art of automated black-box web application scanners in 2020 needs to be improved to detect stored XSS and stored SQLI more effectively.