OnionBots: Subverting Privacy Infrastructure for Cyber Attacks

Over the last decade botnets survived by adopting a sequence of increasingly sophisticated strategies to evade detection and take overs, and to monetize their infrastructure. At the same time, the success of privacy infrastructures such as Tor opened the door to illegal activities, including botnets, ransomware, and a marketplace for drugs and contraband. We contend that the next waves of botnets will extensively subvert privacy infrastructure and cryptographic mechanisms. In this work we propose to preemptively investigate the design and mitigation of such botnets. We first, introduce OnionBots, what we believe will be the next generation of resilient, stealthy botnets. OnionBots use privacy infrastructures for cyber attacks by completely decoupling their operation from the infected host IP address and by carrying traffic that does not leak information about its source, destination, and nature. Such bots live symbiotically within the privacy infrastructures to evade detection, measurement, scale estimation, observation, and in general all IP-based current mitigation techniques. Furthermore, we show that with an adequate self-healing network maintenance scheme, that is simple to implement, OnionBots achieve a low diameter and a low degree and are robust to partitioning under node deletions. We developed a mitigation technique, called SOAP, that neutralizes the nodes of the basic OnionBots. We also outline and discuss a set of techniques that can enable subsequent waves of Super OnionBots. In light of the potential of such botnets, we believe that the research community should proactively develop detection and mitigation methods to thwart OnionBots, potentially making adjustments to privacy infrastructure.Comment: 12 pages, 8 figure

Nowhere to Hide: Cross-modal Identity Leakage between Biometrics and Devices

Along with the benefits of Internet of Things (IoT) come potential privacy risks, since billions of the connected devices are granted permission to track information about their users and communicate it to other parties over the Internet. Of particular interest to the adversary is the user identity which constantly plays an important role in launching attacks. While the exposure of a certain type of physical biometrics or device identity is extensively studied, the compound effect of leakage from both sides remains unknown in multi-modal sensing environments. In this work, we explore the feasibility of the compound identity leakage across cyber-physical spaces and unveil that co-located smart device IDs (e.g., smartphone MAC addresses) and physical biometrics (e.g., facial/vocal samples) are side channels to each other. It is demonstrated that our method is robust to various observation noise in the wild and an attacker can comprehensively profile victims in multi-dimension with nearly zero analysis effort. Two real-world experiments on different biometrics and device IDs show that the presented approach can compromise more than 70\% of device IDs and harvests multiple biometric clusters with ~94% purity at the same time

A framework to detect cyber-attacks against networked medical devices (Internet of Medical Things):an attack-surface-reduction by design approach

Most medical devices in the healthcare system are not built-in security concepts. Hence, these devices' built-in vulnerabilities prone them to various cyber-attacks when connected to a hospital network or cloud. Attackers can penetrate devices, tamper, and disrupt services in hospitals and clinics, which results in threatening patients' health and life. A specialist can Manage Cyber-attacks risks by reducing the system's attack surface. Attack surface analysis, either as a potential source for exploiting a potential vulnerability by attackers or as a medium to reduce cyber-attacks play a significant role in mitigating risks. Furthermore, it is necessitated to perform attack surface analysis in the design phase. This research proposes a framework that integrates attack surface concepts into the design and development of medical devices. Devices are classified as high-risk, medium-risk, and low-risk. After risk assessment, the employed classification algorithm detects and analyzes the attack surfaces. Accordingly, the relevant adapted security controls will be prompted to hinder the attack. The simulation and evaluation of the framework is the subject of further research.</p

Machine Learning based Attacks Detection and Countermeasures in IoT

While the IoT offers important benefits and opportunities for users, the technology raises various security issues and threats. These threats may include spreading IoT botnets through IoT devices which are the common and most malicious security threat in the world of internet. Protecting the IoT devices against these threats and attacks requires efficient detection. While we need to take into consideration IoT devices memory capacity limitation and low power processors. In this paper, we will focus in proposing low power consumption Machine Learning (ML) techniques for detecting IoT botnet attacks using Random forest as ML-based detection method and describing IoT common attacks with its countermeasures. The experimental result of our proposed solution shows higher accuracy. From the results, we conclude that IoT botnet detection is possible; achieving a higher accuracy rate as an experimental result indicates an accuracy rate of over 99.99% where the true positive rate is 1.000 and the false-negative rate is 0.000

Integrated Framework for Secure and Energy Efficient Communication System in Heterogeneous Sensory Application

Irrespective of different forms and strategies implementing for securing Wireless Sensor Network (WSN), there are very less strategies that offers cost effective security over heterogeneous network. Therefore, this paper presents an integrated set of different processes that emphasize over secure routing, intellectual and delay-compensated routing, and optimization principle with a sole intention of securing the communication to and from the sensor nodes during data aggregation. The processed system advocates the non-usage of complex cryptography and encourages the usage of probability their and analytical modelling in order to render more practical implementation. The simulated outcome of study shows that proposed system offers reduced delay, more throughputs, and reduced energy consumption in contrast to existing system

The Rise of Crypto Malware: Leveraging Machine Learning Techniques to Understand the Evolution, Impact, and Detection of Cryptocurrency-Related Threats

Crypto malware has become a major threat to the security of cryptocurrency holders and exchanges. As the popularity of cryptocurrency continues to rise, so too does the number and sophistication of crypto malware attacks. This paper leverages machine learning techniques to understand the evolution, impact, and detection of cryptocurrency-related threats. We analyse the different types of crypto malware, including ransomware, crypto jacking, and supply chain attacks, and explore the use of machine learning algorithms for detecting and preventing these threats. Our research highlights the importance of using machine learning for detecting crypto malware and compares the effectiveness of traditional methods with deep learning techniques. Through this analysis, we aim to provide insights into the growing threat of crypto malware and the potential benefits of using machine learning in combating these attacks

Investigating Security for Ubiquitous Sensor Networks

The availability of powerful and sensor-enabled mobile and Internet-connected devices have enabled the advent of the ubiquitous sensor network paradigm which is providing various types of solutions to the community and the individual user in various sectors including environmental monitoring, entertainment, transportation, security, and healthcare. We explore and compare the features of wireless sensor networks and ubiquitous sensor networks and based on the differences between these two types of systems, we classify the security-related challenges of ubiquitous sensor networks. We identify and discuss solutions available to address these challenges. Finally, we briefly discuss open challenges that need to be addressed to design more secure ubiquitous sensor networks in the future