On the Adaptive Real-Time Detection of Fast-Propagating Network Worms

We present two light-weight worm detection algorithms thatoffer significant advantages over fixed-threshold methods.The first algorithm, RBS (rate-based sequential hypothesis testing)aims at the large class of worms that attempts to quickly propagate, thusexhibiting abnormal levels of the rate at which hosts initiateconnections to new destinations. The foundation of RBS derives fromthe theory of sequential hypothesis testing, the use of which fordetecting randomly scanning hosts was first introduced by our previouswork with the TRW (Threshold Random Walk) scan detection algorithm. The sequential hypothesistesting methodology enables engineering the detectors to meet falsepositives and false negatives targets, rather than triggering whenfixed thresholds are crossed. In this sense, the detectors that weintroduce are truly adaptive.We then introduce RBS+TRW, an algorithm that combines fan-out rate (RBS)and probability of failure (TRW) of connections to new destinations.RBS+TRW provides a unified framework that at one end acts as a pure RBSand at the other end as pure TRW, and extends RBS's power in detectingworms that scan randomly selected IP addresses

A Multi-level Security Based Autonomic Parameter Selection Approach for an Effective and Early Detection of Internet Worms

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

An Innovative Signature Detection System for Polymorphic and Monomorphic Internet Worms Detection and Containment

Most current anti-worm systems and intrusion-detection systems use signature-based technology instead of anomaly-based technology. Signature-based technology can only detect known attacks with identified signatures. Existing anti-worm systems cannot detect unknown Internet scanning worms automatically because these systems do not depend upon worm behaviour but upon the worm’s signature. Most detection algorithms used in current detection systems target only monomorphic worm payloads and offer no defence against polymorphic worms, which changes the payload dynamically. Anomaly detection systems can detect unknown worms but usually suffer from a high false alarm rate. Detecting unknown worms is challenging, and the worm defence must be automated because worms spread quickly and can flood the Internet in a short time. This research proposes an accurate, robust and fast technique to detect and contain Internet worms (monomorphic and polymorphic). The detection technique uses specific failure connection statuses on specific protocols such as UDP, TCP, ICMP, TCP slow scanning and stealth scanning as characteristics of the worms. Whereas the containment utilizes flags and labels of the segment header and the source and destination ports to generate the traffic signature of the worms. Experiments using eight different worms (monomorphic and polymorphic) in a testbed environment were conducted to verify the performance of the proposed technique. The experiment results showed that the proposed technique could detect stealth scanning up to 30 times faster than the technique proposed by another researcher and had no false-positive alarms for all scanning detection cases. The experiments showed the proposed technique was capable of containing the worm because of the traffic signature’s uniqueness

Cybersecurity Games: Mathematical Approaches for Cyber Attack and Defense Modeling

Cyber-attacks targeting individuals and enterprises have become a predominant part of the computer/information age. Such attacks are becoming more sophisticated and prevalent on a day-to-day basis. The exponential growth of cyber plays and cyber players necessitate the inauguration of new methods and research for better understanding the cyber kill chain, particularly with the rise of advanced and novel malware and the extraordinary growth in the population of Internet residents, especially connected Internet of Things (IoT) devices. Mathematical modeling could be used to represent real-world cyber-attack situations. Such models play a beneficial role when it comes to the secure design and evaluation of systems/infrastructures by providing a better understanding of the threat itself and the attacker\u27s conduct during the lifetime of a cyber attack. Therefore, the main goal of this dissertation is to construct a proper theoretical framework to be able to model and thus evaluate the defensive strategies/technologies\u27 effectiveness from a security standpoint. To this end, we first present a Markov-based general framework to model the interactions between the two famous players of (network) security games, i.e., a system defender and an attacker taking actions to reach its attack objective(s) in the game. We mainly focus on the most significant and tangible aspects of sophisticated cyber attacks: (1) the amount of time it takes for the adversary to accomplish its mission and (2) the success probabilities of fulfilling the attack objective(s) by translating attacker-defender interactions into well-defined games and providing rigorous cryptographic security guarantees for a system given both players\u27 tactics and strategies. We study various attack-defense scenarios, including Moving Target Defense (MTD) strategies, multi-stage attacks, and Advanced Persistent Threats (APT). We provide general theorems about how the probability of a successful adversary defeating a defender’s strategy is related to the amount of time (or any measure of cost) spent by the adversary in such scenarios. We also introduce the notion of learning in cybersecurity games and describe a general game of consequences meaning that each player\u27s chances of making a progressive move in the game depend on its previous actions. Finally, we walk through a malware propagation and botnet construction game in which we investigate the importance of defense systems\u27 learning rates to fight against the self-propagating class of malware such as worms and bots. We introduce a new propagation modeling and containment strategy called the learning-based model and study the containment criterion for the propagation of the malware based on theoretical and simulation analysis

Collaborative internet worm containment

Large-scale worm outbrakes that leads to distributed denial-of-dervice attacks pose a major threat to internet infrastructure security. To prevent computers from such attacks deployment of fast, scalable security overlay networks based on distributed hash tables to facilitate high-speed intrusion detection and alert-information exchange are proposed. An effective system for worm detection and cyberspace defence must have robustness, cooperation among multiple sites, responsiveness to unexpected worms and efficiency and scalability. Deployment of collaborative WormShield monitors on just 1 percent of the vulnerable edge networks can detect worm signatures roughly 10 times faster than with independent monitors.published_or_final_versio

SPECTRAL GRAPH-BASED CYBER DETECTION AND CLASSIFICATION SYSTEM WITH PHANTOM COMPONENTS

With cyber attacks on the rise, cyber defenders require new, innovative solutions to provide network protection. We propose a spectral graph-based cyber detection and classification (SGCDC) system using phantom components, the strong node concept, and the dual-degree matrix to detect, classify, and respond to worm and distributed denial-of-service (DDoS) attacks. The system is analyzed using absorbing Markov chains and a novel Levy-impulse model that characterizes network SYN traffic to determine the theoretical false-alarm rates of the system. The detection mechanism is analyzed in the face of network noise and congestion using Weyl’s theorem, the Davis-Kahan theorem, and a novel application of the n-dimensional Euclidean metric. The SGCDC system is validated using real-world and synthetic datasets, including the WannaCry and Blaster worms and a SYN flood attack. The system accurately detected and classified the attacks in all but one case studied. The known attacking nodes were identified in less than 0.27 sec for the DDoS attack, and the worm-infected nodes were identified in less than one second after the second infected node began the target search and discovery process for the WannaCry and Blaster worm attacks. The system also produced a false-alarm rate of less than 0.005 under a scenario. These results improve upon other non-spectral graph systems that have detection rates of less than 0.97 sec and false alarm rates as high as 0.095 sec for worm and DDoS attacks.Lieutenant Commander, United States NavyApproved for public release. distribution is unlimite

Anagram: A Content Anomaly Detector Resistant to Mimicry Attack

In this paper, we present Anagram, a content anomaly detector that models a mixture of high-order n-grams (n > 1) designed to detect anomalous and suspicious network packet payloads. By using higher- order n-grams, Anagram can detect significant anomalous byte sequences and generate robust signatures of validated malicious packet content. The Anagram content models are implemented using highly efficient Bloom filters, reducing space requirements and enabling privacy-preserving cross-site correlation. The sensor models the distinct content flow of a network or host using a semi- supervised training regimen. Previously known exploits, extracted from the signatures of an IDS, are likewise modeled in a Bloom filter and are used during training as well as detection time. We demonstrate that Anagram can identify anomalous traffic with high accuracy and low false positive rates. Anagram’s high-order n-gram analysis technique is also resilient against simple mimicry attacks that blend exploits with normal appearing byte padding, such as the blended polymorphic attack recently demonstrated in. We discuss randomized n-gram models, which further raises the bar and makes it more difficult for attackers to build precise packet structures to evade Anagram even if they know the distribution of the local site content flow. Finally, Anagram-’s speed and high detection rate makes it valuable not only as a standalone sensor, but also as a network anomaly flow classifier in an instrumented fault-tolerant host-based environment; this enables significant cost amortization and the possibility of a symbiotic feedback loop that can improve accuracy and reduce false positive rates over time

Improved Worm Simulator and Simulations

According to the latest Microsoft Security Intelligence Report (SIR), worms were the second most prevalent information security threat detected in the first half of 2010 – the top threat being Trojans. Given the prevalence and damaging effects of worms, research and development of worm counter strategies are garnering an increased level of attention. However, it is extremely risky to test and observe worm spread behavior on a public network. What is needed is a packet level worm simulator that would allow researchers to develop and test counter strategies against rapidly spreading worms in a controlled and isolated environment. Jyotsna Krishnaswamy, a recent SJSU graduate student, successfully implemented a packet level worm simulator called the Wormulator. The Wormulator was specifically designed to simulate the behavior of the SQL Slammer worm. This project aims to improve the Wormulator by addressing some of its limitations. The resulting implementation will be called the Improved Worm Simulator