Based on Regular Expression Matching of Evaluation of the Task Performance in WSN: A Queue Theory Approach

Due to the limited resources of wireless sensor network, low efficiency of real-time communication scheduling, poor safety defects, and so forth, a queuing performance evaluation approach based on regular expression match is proposed, which is a method that consists of matching preprocessing phase, validation phase, and queuing model of performance evaluation phase. Firstly, the subset of related sequence is generated in preprocessing phase, guiding the validation phase distributed matching. Secondly, in the validation phase, the subset of features clustering, the compressed matching table is more convenient for distributed parallel matching. Finally, based on the queuing model, the sensor networks of task scheduling dynamic performance are evaluated. Experiments show that our approach ensures accurate matching and computational efficiency of more than 70%; it not only effectively detects data packets and access control, but also uses queuing method to determine the parameters of task scheduling in wireless sensor networks. The method for medium scale or large scale distributed wireless node has a good applicability

Faster Compression of Deterministic Finite Automata

Deterministic finite automata (DFA) are a classic tool for high throughput matching of regular expressions, both in theory and practice. Due to their high space consumption, extensive research has been devoted to compressed representations of DFAs that still support efficient pattern matching queries. Kumar~et~al.~[SIGCOMM 2006] introduced the \emph{delayed deterministic finite automaton} (\ddfa{}) which exploits the large redundancy between inter-state transitions in the automaton. They showed it to obtain up to two orders of magnitude compression of real-world DFAs, and their work formed the basis of numerous subsequent results. Their algorithm, as well as later algorithms based on their idea, have an inherent quadratic-time bottleneck, as they consider every pair of states to compute the optimal compression. In this work we present a simple, general framework based on locality-sensitive hashing for speeding up these algorithms to achieve sub-quadratic construction times for \ddfa{}s. We apply the framework to speed up several algorithms to near-linear time, and experimentally evaluate their performance on real-world regular expression sets extracted from modern intrusion detection systems. We find an order of magnitude improvement in compression times, with either little or no loss of compression, or even significantly better compression in some cases

POIROT: Aligning Attack Behavior with Kernel Audit Records for Cyber Threat Hunting

Cyber threat intelligence (CTI) is being used to search for indicators of attacks that might have compromised an enterprise network for a long time without being discovered. To have a more effective analysis, CTI open standards have incorporated descriptive relationships showing how the indicators or observables are related to each other. However, these relationships are either completely overlooked in information gathering or not used for threat hunting. In this paper, we propose a system, called POIROT, which uses these correlations to uncover the steps of a successful attack campaign. We use kernel audits as a reliable source that covers all causal relations and information flows among system entities and model threat hunting as an inexact graph pattern matching problem. Our technical approach is based on a novel similarity metric which assesses an alignment between a query graph constructed out of CTI correlations and a provenance graph constructed out of kernel audit log records. We evaluate POIROT on publicly released real-world incident reports as well as reports of an adversarial engagement designed by DARPA, including ten distinct attack campaigns against different OS platforms such as Linux, FreeBSD, and Windows. Our evaluation results show that POIROT is capable of searching inside graphs containing millions of nodes and pinpoint the attacks in a few minutes, and the results serve to illustrate that CTI correlations could be used as robust and reliable artifacts for threat hunting.Comment: The final version of this paper is going to appear in the ACM SIGSAC Conference on Computer and Communications Security (CCS'19), November 11-15, 2019, London, United Kingdo

Intrusion detection and management over the world wide web

As the Internet and society become ever more integrated so the number of Internet users continues to grow. Today there are 1.6 billion Internet users. They use its services to work from home, shop for gifts, socialise with friends, research the family holiday and manage their finances. Through generating both wealth and employment the Internet and our economies have also become interwoven. The growth of the Internet has attracted hackers and organised criminals. Users are targeted for financial gain through malware and social engineering attacks. Industry has responded to the growing threat by developing a range defences: antivirus software, firewalls and intrusion detection systems are all readily available. Yet the Internet security problem continues to grow and Internet crime continues to thrive. Warnings on the latest application vulnerabilities, phishing scams and malware epidemics are announced regularly and serve to heighten user anxiety. Not only are users targeted for attack but so too are businesses, corporations, public utilities and even states. Implementing network security remains an error prone task for the modern Internet user. In response this thesis explores whether intrusion detection and management can be effectively offered as a web service to users in order to better protect them and heighten their awareness of the Internet security threat

Combatting Advanced Persistent Threat via Causality Inference and Program Analysis

Cyber attackers are becoming more and more sophisticated. In particular, Advanced Persistent Threat (APT) is a new class of attack that targets a specifc organization and compromises systems over a long time without being detected. Over the years, we have seen notorious examples of APTs including Stuxnet which disrupted Iranian nuclear centrifuges and data breaches affecting millions of users. Investigating APT is challenging as it occurs over an extended period of time and the attack process is highly sophisticated and stealthy. Also, preventing APTs is diffcult due to ever-expanding attack vectors. In this dissertation, we present proposals for dealing with challenges in attack investigation. Specifcally, we present LDX which conducts precise counter-factual causality inference to determine dependencies between system calls (e.g., between input and output system calls) and allows investigators to determine the origin of an attack (e.g., receiving a spam email) and the propagation path of the attack, and assess the consequences of the attack. LDX is four times more accurate and two orders of magnitude faster than state-of-the-art taint analysis techniques. Moreover, we then present a practical model-based causality inference system, MCI, which achieves precise and accurate causality inference without requiring any modifcation or instrumentation in end-user systems. Second, we show a general protection system against a wide spectrum of attack vectors and methods. Specifcally, we present A2C that prevents a wide range of attacks by randomizing inputs such that any malicious payloads contained in the inputs are corrupted. The protection provided by A2C is both general (e.g., against various attack vectors) and practical (7% runtime overhead)

Towards an efficient vulnerability analysis methodology for better security risk management

2010 Summer.Includes bibliographical references.Risk management is a process that allows IT managers to balance between cost of the protective measures and gains in mission capability. A system administrator has to make a decision and choose an appropriate security plan that maximizes the resource utilization. However, making the decision is not a trivial task. Most organizations have tight budgets for IT security; therefore, the chosen plan must be reviewed as thoroughly as other management decisions. Unfortunately, even the best-practice security risk management frameworks do not provide adequate information for effective risk management. Vulnerability scanning and penetration testing that form the core of traditional risk management, identify only the set of system vulnerabilities. Given the complexity of today's network infrastructure, it is not enough to consider the presence or absence of vulnerabilities in isolation. Materializing a threat strongly requires the combination of multiple attacks using different vulnerabilities. Such a requirement is far beyond the capabilities of current day vulnerability scanners. Consequently, assessing the cost of an attack or cost of implementing appropriate security controls is possible only in a piecemeal manner. In this work, we develop and formalize new network vulnerability analysis model. The model encodes in a concise manner, the contributions of different security conditions that lead to system compromise. We extend the model with a systematic risk assessment methodology to support reasoning under uncertainty in an attempt to evaluate the vulnerability exploitation probability. We develop a cost model to quantify the potential loss and gain that can occur in a system if certain conditions are met (or protected). We also quantify the security control cost incurred to implement a set of security hardening measures. We propose solutions for the system administrator's decision problems covering the area of the risk analysis and risk mitigation analysis. Finally, we extend the vulnerability assessment model to the areas of intrusion detection and forensic investigation

A framework for correlation and aggregation of security alerts in communication networks. A reasoning correlation and aggregation approach to detect multi-stage attack scenarios using elementary alerts generated by Network Intrusion Detection Systems (NIDS) for a global security perspective.

The tremendous increase in usage and complexity of modern communication and network systems connected to the Internet, places demands upon security management to protect organisations¿ sensitive data and resources from malicious intrusion. Malicious attacks by intruders and hackers exploit flaws and weakness points in deployed systems through several sophisticated techniques that cannot be prevented by traditional measures, such as user authentication, access controls and firewalls. Consequently, automated detection and timely response systems are urgently needed to detect abnormal activities by monitoring network traffic and system events. Network Intrusion Detection Systems (NIDS) and Network Intrusion Prevention Systems (NIPS) are technologies that inspect traffic and diagnose system behaviour to provide improved attack protection. The current implementation of intrusion detection systems (commercial and open-source) lacks the scalability to support the massive increase in network speed, the emergence of new protocols and services. Multi-giga networks have become a standard installation posing the NIDS to be susceptible to resource exhaustion attacks. The research focuses on two distinct problems for the NIDS: missing alerts due to packet loss as a result of NIDS performance limitations; and the huge volumes of generated alerts by the NIDS overwhelming the security analyst which makes event observation tedious. A methodology for analysing alerts using a proposed framework for alert correlation has been presented to provide the security operator with a global view of the security perspective. Missed alerts are recovered implicitly using a contextual technique to detect multi-stage attack scenarios. This is based on the assumption that the most serious intrusions consist of relevant steps that temporally ordered. The pre- and post- condition approach is used to identify the logical relations among low level alerts. The alerts are aggregated, verified using vulnerability modelling, and correlated to construct multi-stage attacks. A number of algorithms have been proposed in this research to support the functionality of our framework including: alert correlation, alert aggregation and graph reduction. These algorithms have been implemented in a tool called Multi-stage Attack Recognition System (MARS) consisting of a collection of integrated components. The system has been evaluated using a series of experiments and using different data sets i.e. publicly available datasets and data sets collected using real-life experiments. The results show that our approach can effectively detect multi-stage attacks. The false positive rates are reduced due to implementation of the vulnerability and target host information

Achieving network resiliency using sound theoretical and practical methods

Computer networks have revolutionized the life of every citizen in our modern intercon- nected society. The impact of networked systems spans every aspect of our lives, from financial transactions to healthcare and critical services, making these systems an attractive target for malicious entities that aim to make financial or political profit. Specifically, the past decade has witnessed an astounding increase in the number and complexity of sophisti- cated and targeted attacks, known as advanced persistent threats (APT). Those attacks led to a paradigm shift in the security and reliability communities’ perspective on system design; researchers and government agencies accepted the inevitability of incidents and malicious attacks, and marshaled their efforts into the design of resilient systems. Rather than focusing solely on preventing failures and attacks, resilient systems are able to maintain an acceptable level of operation in the presence of such incidents, and then recover gracefully into normal operation. Alongside prevention, resilient system design focuses on incident detection as well as timely response. Unfortunately, the resiliency efforts of research and industry experts have been hindered by an apparent schism between theory and practice, which allows attackers to maintain the upper hand advantage. This lack of compatibility between the theory and practice of system design is attributed to the following challenges. First, theoreticians often make impractical and unjustifiable assumptions that allow for mathematical tractability while sacrificing accuracy. Second, the security and reliability communities often lack clear definitions of success criteria when comparing different system models and designs. Third, system designers often make implicit or unstated assumptions to favor practicality and ease of design. Finally, resilient systems are tested in private and isolated environments where validation and reproducibility of the results are not publicly accessible. In this thesis, we set about showing that the proper synergy between theoretical anal- ysis and practical design can enhance the resiliency of networked systems. We illustrate the benefits of this synergy by presenting resiliency approaches that target the inter- and intra-networking levels. At the inter-networking level, we present CPuzzle as a means to protect the transport control protocol (TCP) connection establishment channel from state- exhaustion distributed denial of service attacks (DDoS). CPuzzle leverages client puzzles to limit the rate at which misbehaving users can establish TCP connections. We modeled the problem of determining the puzzle difficulty as a Stackleberg game and solve for the equilibrium strategy that balances the users’ utilizes against CPuzzle’s resilience capabilities. Furthermore, to handle volumetric DDoS attacks, we extend CPuzzle and implement Midgard, a cooperative approach that involves end-users in the process of tolerating and neutralizing DDoS attacks. Midgard is a middlebox that resides at the edge of an Internet service provider’s network and uses client puzzles at the IP level to allocate bandwidth to its users. At the intra-networking level, we present sShield, a game-theoretic network response engine that manipulates a network’s connectivity in response to an attacker who is moving laterally to compromise a high-value asset. To implement such decision making algorithms, we leverage the recent advances in software-defined networking (SDN) to collect logs and security alerts about the network and implement response actions. However, the programma- bility offered by SDN comes with an increased chance for design-time bugs that can have drastic consequences on the reliability and security of a networked system. We therefore introduce BiFrost, an open-source tool that aims to verify safety and security proper- ties about data-plane programs. BiFrost translates data-plane programs into functionally equivalent sequential circuits, and then uses well-established hardware reduction, abstrac- tion, and verification techniques to establish correctness proofs about data-plane programs. By focusing on those four key efforts, CPuzzle, Midgard, sShield, and BiFrost, we believe that this work illustrates the benefits that the synergy between theory and practice can bring into the world of resilient system design. This thesis is an attempt to pave the way for further cooperation and coordination between theoreticians and practitioners, in the hope of designing resilient networked systems

Fast Packet Processing on High Performance Architectures

The rapid growth of Internet and the fast emergence of new network applications have brought great challenges and complex issues in deploying high-speed and QoS guaranteed IP network. For this reason packet classication and network intrusion detection have assumed a key role in modern communication networks in order to provide Qos and security. In this thesis we describe a number of the most advanced solutions to these tasks. We introduce NetFPGA and Network Processors as reference platforms both for the design and the implementation of the solutions and algorithms described in this thesis. The rise in links capacity reduces the time available to network devices for packet processing. For this reason, we show different solutions which, either by heuristic and randomization or by smart construction of state machine, allow IP lookup, packet classification and deep packet inspection to be fast in real devices based on high speed platforms such as NetFPGA or Network Processors