A Practical Approach to Protect IoT Devices against Attacks and Compile Security Incident Datasets

open access articleThe Internet of Things (IoT) introduced the opportunity of remotely manipulating home appliances (such as heating systems, ovens, blinds, etc.) using computers and mobile devices. This idea fascinated people and originated a boom of IoT devices together with an increasing demand that was difficult to support. Many manufacturers quickly created hundreds of devices implementing functionalities but neglected some critical issues pertaining to device security. This oversight gave rise to the current situation where thousands of devices remain unpatched having many security issues that manufacturers cannot address after the devices have been produced and deployed. This article presents our novel research protecting IOT devices using Berkeley Packet Filters (BPFs) and evaluates our findings with the aid of our Filter.tlk tool, which is able to facilitate the development of BPF expressions that can be executed by GNU/Linux systems with a low impact on network packet throughput

The Dynamics of Internet Traffic: Self-Similarity, Self-Organization, and Complex Phenomena

The Internet is the most complex system ever created in human history. Therefore, its dynamics and traffic unsurprisingly take on a rich variety of complex dynamics, self-organization, and other phenomena that have been researched for years. This paper is a review of the complex dynamics of Internet traffic. Departing from normal treatises, we will take a view from both the network engineering and physics perspectives showing the strengths and weaknesses as well as insights of both. In addition, many less covered phenomena such as traffic oscillations, large-scale effects of worm traffic, and comparisons of the Internet and biological models will be covered.Comment: 63 pages, 7 figures, 7 tables, submitted to Advances in Complex System

Securing Enterprise Networks with Statistical Node Behavior Profiling

The substantial proliferation of the Internet has made it the most critical infrastructure in today\u27s world. However, it is still vulnerable to various kinds of attacks/malwares and poses a number of great security challenges. Furthermore, we have also witnessed in the past decade that there is always a fast self-evolution of attacks/malwares (e.g. from worms to botnets) against every success in network security. Network security thereby remains a hot topic in both research and industry and requires both continuous and great attention. In this research, we consider two fundamental areas in network security, malware detection and background traffic modeling, from a new view point of node behavior profiling under enterprise network environments. Our main objectives are to extend and enhance the current research in these two areas. In particular, central to our research is the node behavior profiling approach that groups the behaviors of different nodes by jointly considering time and spatial correlations. We also present an extensive study on botnets, which are believed to be the largest threat to the Internet. To better understand the botnet, we propose a botnet framework and predict a new P2P botnet that is much stronger and stealthier than the current ones. We then propose anomaly malware detection approaches based directly on the insights (statistical characteristics) from the node behavior study and apply them on P2P botnet detection. Further, by considering the worst case attack model where the botmaster knows all the parameter values used in detection, we propose a fast and optimized anomaly detection approach by formulating the detection problem as an optimization problem. In addition, we propose a novel traffic modeling structure using behavior profiles for NIDS evaluations. It is efficient and takes into account the node heterogeneity in traffic modeling. It is also compatible with most current modeling schemes and helpful in generating better realistic background traffic. Last but not least, we evaluate the proposed approaches using real user trace from enterprise networks and achieve encouraging results. Our contributions in this research include: 1) a new node behavior profiling approach to study the normal node behavior; 2) a framework for botnets; 3) a new P2P botnet and performance comparisons with other P2P botnets; 4) two anomaly detection approaches based on node behavior profiles; 4) a fast and optimized anomaly detection approach under the worst case attack model; 5) a new traffic modeling structure and 6) simulations and evaluations of the above approaches under real user data from enterprise networks. To the best of our knowledge, we are the first to propose the botnet framework, consider the worst case attack model and propose corresponding fast and optimized solution in botnet related research. We are also the first to propose efficient solutions in traffic modeling without the assumption of node homogeneity

Malware Propagation Modelling in Peer-to-Peer Networks: A Review

yesPeer-to-Peer (P2P) network is increasingly becoming the most important means of trading content throughout the last years due to the constant evolvement of the cyber world. This popularity made the P2P network susceptible to the spread of malware. The detection of the cause of malware propagation is now critical to the survival of P2P networks. This paper offers a review of the current relevant mathematical propagation models that have been proposed to date to predict the propagation behavior of a malware in a P2P network. We analyzed the models proposed by researchers and experts in the field by evaluating their limitations and a possible alternative for improving the analysis of the expected behavior of a malware spread

On Detection of Current and Next-Generation Botnets.

Botnets are one of the most serious security threats to the Internet and its end users. A botnet consists of compromised computers that are remotely coordinated by a botmaster under a Command and Control (C&C) infrastructure. Driven by financial incentives, botmasters leverage botnets to conduct various cybercrimes such as spamming, phishing, identity theft and Distributed-Denial-of-Service (DDoS) attacks. There are three main challenges facing botnet detection. First, code obfuscation is widely employed by current botnets, so signature-based detection is insufficient. Second, the C&C infrastructure of botnets has evolved rapidly. Any detection solution targeting one botnet instance can hardly keep up with this change. Third, the proliferation of powerful smartphones presents a new platform for future botnets. Defense techniques designed for existing botnets may be outsmarted when botnets invade smartphones. Recognizing these challenges, this dissertation proposes behavior-based botnet detection solutions at three different levels---the end host, the edge network and the Internet infrastructure---from a small scale to a large scale, and investigates the next-generation botnet targeting smartphones. It (1) addresses the problem of botnet seeding by devising a per-process containment scheme for end-host systems; (2) proposes a hybrid botnet detection framework for edge networks utilizing combined host- and network-level information; (3) explores the structural properties of botnet topologies and measures network components' capabilities of large-scale botnet detection at the Internet infrastructure level; and (4) presents a proof-of-concept mobile botnet employing SMS messages as the C&C and P2P as the topology to facilitate future research on countermeasures against next-generation botnets. The dissertation makes three primary contributions. First, the detection solutions proposed utilize intrinsic and fundamental behavior of botnets and are immune to malware obfuscation and traffic encryption. Second, the solutions are general enough to identify different types of botnets, not a specific botnet instance. They can also be extended to counter next-generation botnet threats. Third, the detection solutions function at multiple levels to meet various detection needs. They each take a different perspective but are highly complementary to each other, forming an integrated botnet detection framework.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91382/1/gracez_1.pd

Security Engineering of Patient-Centered Health Care Information Systems in Peer-to-Peer Environments: Systematic Review

Background: Patient-centered health care information systems (PHSs) enable patients to take control and become knowledgeable about their own health, preferably in a secure environment. Current and emerging PHSs use either a centralized database, peer-to-peer (P2P) technology, or distributed ledger technology for PHS deployment. The evolving COVID-19 decentralized Bluetooth-based tracing systems are examples of disease-centric P2P PHSs. Although using P2P technology for the provision of PHSs can be flexible, scalable, resilient to a single point of failure, and inexpensive for patients, the use of health information on P2P networks poses major security issues as users must manage information security largely by themselves. Objective: This study aims to identify the inherent security issues for PHS deployment in P2P networks and how they can be overcome. In addition, this study reviews different P2P architectures and proposes a suitable architecture for P2P PHS deployment. Methods: A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. Thematic analysis was used for data analysis. We searched the following databases: IEEE Digital Library, PubMed, Science Direct, ACM Digital Library, Scopus, and Semantic Scholar. The search was conducted on articles published between 2008 and 2020. The Common Vulnerability Scoring System was used as a guide for rating security issues. Results: Our findings are consolidated into 8 key security issues associated with PHS implementation and deployment on P2P networks and 7 factors promoting them. Moreover, we propose a suitable architecture for P2P PHSs and guidelines for the provision of PHSs while maintaining information security. Conclusions: Despite the clear advantages of P2P PHSs, the absence of centralized controls and inconsistent views of the network on some P2P systems have profound adverse impacts in terms of security. The security issues identified in this study need to be addressed to increase patients\u27 intention to use PHSs on P2P networks by making them safe to use

Distributed Early Worm Detection Based on Payload Histograms

科研費報告書収録論文(課題番号:18300017/研究代表者:根元義章/通信データ列特徴量の類似性に基づいた不正アクセス逆探知方式)68

Modeling and defense against propagation of worms in networks

Worms are widely believed to be one of the most serious challenges in network security research. In order to prevent worms from propagating, we present a microcosmic model, which can benefit the security industry by allowing them to save significant money in the deployment of their security patching schemes