Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

Concolic Execution of NMap Scripts for Honeyfarm Generation

Attackers rely upon a vast array of tools for automating attacksagainst vulnerable servers and services. It is often the case thatwhen vulnerabilities are disclosed, scripts for detecting and exploit-ing them in tools such asNmapandMetasploitare released soonafter, leading to the immediate identification and compromise ofvulnerable systems. Honeypots, honeynets, tarpits, and other decep-tive techniques can be used to slow attackers down, however, such approaches have difficulty keeping up with the sheer number of vulnerabilities being discovered and attacking scripts that are being released. To address this issue, this paper describes an approach for applying concolic execution on attacking scripts in Nmap in order to automatically generate lightweight fake versions of the vulnerable services that can fool the scripts. By doing so in an automated and scalable manner, the approach can enable rapid deployment of custom honeyfarms that leverage the results of concolic execution to trick an attacker\u27s script into returning a result chosen by the honeyfarm, making the script unreliable for the use by the attacker

Economic issues in distributed computing

textOn the Internet, one of the essential characteristics of electronic commerce is the integration of large-scale computer networks and business practices. Commercial servers are connected through open and complex communication technologies, and online consumers access the services with virtually unpredictable behavior. Both of them as well as the e-Commerce infrastructure are vulnerable to cyber attacks. Among the various network security problems, the Distributed Denial-of-Service (DDoS) attack is a unique example to illustrate the risk of commercial network applications. Using a massive junk traffic, literally anyone on the Internet can launch a DDoS attack to flood and shutdown an eCommerce website. Cooperative technological solutions for Distributed Denial-of-Service (DDoS) attacks are already available, yet organizations in the best position to implement them lack incentive to do so, and the victims of DDoS attacks cannot find effective methods to motivate the organizations. Chapter 1 discusses two components of the technological solutions to DDoS attacks: cooperative filtering and cooperative traffic smoothing by caching, and then analyzes the broken incentive chain in each of these technological solutions. As a remedy, I propose usage-based pricing and Capacity Provision Networks, which enable victims to disseminate enough incentive along attack paths to stimulate cooperation against DDoS attacks. Chapter 2 addresses possible Distributed Denial-of-Service (DDoS) attacks toward the wireless Internet including the Wireless Extended Internet, the Wireless Portal Network, and the Wireless Ad Hoc network. I propose a conceptual model for defending against DDoS attacks on the wireless Internet, which incorporates both cooperative technological solutions and economic incentive mechanisms built on usage-based fees. Cost-effectiveness is also addressed through an illustrative implementation scheme using Policy Based Networking (PBN). By investigating both technological and economic difficulties in defense of DDoS attacks which have plagued the wired Internet, our aim here is to foster further development of wireless Internet infrastructure as a more secure and efficient platform for mobile commerce. To avoid centralized resources and performance bottlenecks, online peer-to-peer communities and online social network have become increasingly popular. In particular, the recent boost of online peer-to-peer communities has led to exponential growth in sharing of user-contributed content which has brought profound changes to business and economic practices. Understanding the dynamics and sustainability of such peer-to-peer communities has important implications for business managers. In Chapter 3, I explore the structure of online sharing communities from a dynamic process perspective. I build an evolutionary game model to capture the dynamics of online peer-to-peer communities. Using online music sharing data collected from one of the IRC Channels for over five years, I empirically investigate the model which underlies the dynamics of the music sharing community. Our empirical results show strong support for the evolutionary process of the community. I find that the two major parties in the community, namely sharers and downloaders, are influencing each other in their dynamics of evolvement in the community. These dynamics reveal the mechanism through which peer-to-peer communities sustain and thrive in a constant changing environment.Information, Risk, and Operations Management (IROM

Vulnerability Assessment and Privacy-preserving Computations in Smart Grid

Modern advances in sensor, computing, and communication technologies enable various smart grid applications which highlight the vulnerability that requires novel approaches to the field of cybersecurity. While substantial numbers of technologies have been adopted to protect cyber attacks in smart grid, there lacks a comprehensive review of the implementations, impacts, and solutions of cyber attacks specific to the smart grid.In this dissertation, we are motivated to evaluate the security requirements for the smart grid which include three main properties: confidentiality, integrity, and availability. First, we review the cyber-physical security of the synchrophasor network, which highlights all three aspects of security issues. Taking the synchrophasor network as an example, we give an overview of how to attack a smart grid network. We test three types of attacks and show the impact of each attack consisting of denial-of-service attack, sniffing attack, and false data injection attack.Next, we discuss how to protect against each attack. For protecting availability, we examine possible defense strategies for the associated vulnerabilities.For protecting data integrity, a small-scale prototype of secure synchrophasor network is presented with different cryptosystems. Besides, a deep learning based time-series anomaly detector is proposed to detect injected measurement. Our approach observes both data measurements and network traffic features to jointly learn system states and can detect attacks when state vector estimator fails.For protecting data confidentiality, we propose privacy-preserving algorithms for two important smart grid applications. 1) A distributed privacy-preserving quadratic optimization algorithm to solve Security Constrained Optimal Power Flow (SCOPF) problem. The SCOPF problem is decomposed into small subproblems using the Alternating Direction Method of Multipliers (ADMM) and gradient projection algorithms. 2) We use Paillier cryptosystem to secure the computation of the power system dynamic simulation. The IEEE 3-Machine 9-Bus System is used to implement and demonstrate the proposed scheme. The security and performance analysis of our implementations demonstrate that our algorithms can prevent chosen-ciphertext attacks at a reasonable cost

Towards understanding and mitigating attacks leveraging zero-day exploits

Zero-day vulnerabilities are unknown and therefore not addressed with the result that they can be exploited by attackers to gain unauthorised system access. In order to understand and mitigate against attacks leveraging zero-days or unknown techniques, it is necessary to study the vulnerabilities, exploits and attacks that make use of them. In recent years there have been a number of leaks publishing such attacks using various methods to exploit vulnerabilities. This research seeks to understand what types of vulnerabilities exist, why and how these are exploited, and how to defend against such attacks by either mitigating the vulnerabilities or the method / process of exploiting them. By moving beyond merely remedying the vulnerabilities to defences that are able to prevent or detect the actions taken by attackers, the security of the information system will be better positioned to deal with future unknown threats. An interesting finding is how attackers exploit moving beyond the observable bounds to circumvent security defences, for example, compromising syslog servers, or going down to lower system rings to gain access. However, defenders can counter this by employing defences that are external to the system preventing attackers from disabling them or removing collected evidence after gaining system access. Attackers are able to defeat air-gaps via the leakage of electromagnetic radiation as well as misdirect attribution by planting false artefacts for forensic analysis and attacking from third party information systems. They analyse the methods of other attackers to learn new techniques. An example of this is the Umbrage project whereby malware is analysed to decide whether it should be implemented as a proof of concept. Another important finding is that attackers respect defence mechanisms such as: remote syslog (e.g. firewall), core dump files, database auditing, and Tripwire (e.g. SlyHeretic). These defences all have the potential to result in the attacker being discovered. Attackers must either negate the defence mechanism or find unprotected targets. Defenders can use technologies such as encryption to defend against interception and man-in-the-middle attacks. They can also employ honeytokens and honeypots to alarm misdirect, slow down and learn from attackers. By employing various tactics defenders are able to increase their chance of detecting and time to react to attacks, even those exploiting hitherto unknown vulnerabilities. To summarize the information presented in this thesis and to show the practical importance thereof, an examination is presented of the NSA's network intrusion of the SWIFT organisation. It shows that the firewalls were exploited with remote code execution zerodays. This attack has a striking parallel in the approach used in the recent VPNFilter malware. If nothing else, the leaks provide information to other actors on how to attack and what to avoid. However, by studying state actors, we can gain insight into what other actors with fewer resources can do in the future

Advanced Threat Intelligence: Interpretation of Anomalous Behavior in Ubiquitous Kernel Processes

Targeted attacks on digital infrastructures are a rising threat against the confidentiality, integrity, and availability of both IT systems and sensitive data. With the emergence of advanced persistent threats (APTs), identifying and understanding such attacks has become an increasingly difficult task. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. This thesis presents a multi-stage system able to detect and classify anomalous behavior within a user session by observing and analyzing ubiquitous kernel processes. Application candidates suitable for monitoring are initially selected through an adapted sentiment mining process using a score based on the log likelihood ratio (LLR). For transparent anomaly detection within a corpus of associated events, the author utilizes star structures, a bipartite representation designed to approximate the edit distance between graphs. Templates describing nominal behavior are generated automatically and are used for the computation of both an anomaly score and a report containing all deviating events. The extracted anomalies are classified using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Ultimately, the newly labeled patterns are mapped to a dedicated APT attacker–defender model that considers objectives, actions, actors, as well as assets, thereby bridging the gap between attack indicators and detailed threat semantics. This enables both risk assessment and decision support for mitigating targeted attacks. Results show that the prototype system is capable of identifying 99.8% of all star structure anomalies as benign or malicious. In multi-class scenarios that seek to associate each anomaly with a distinct attack pattern belonging to a particular APT stage we achieve a solid accuracy of 95.7%. Furthermore, we demonstrate that 88.3% of observed attacks could be identified by analyzing and classifying a single ubiquitous Windows process for a mere 10 seconds, thereby eliminating the necessity to monitor each and every (unknown) application running on a system. With its semantic take on threat detection and classification, the proposed system offers a formal as well as technical solution to an information security challenge of great significance.The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, and the National Foundation for Research, Technology and Development is gratefully acknowledged

Detecting malicious VBscripts using anomaly host based IDS based on principal component analysis (PCA)

Intrusion detection research over the last twenty years has focused on the threat of individuals illegally hacking into systems. Nowadays, intrusion threat to computer systems has changed radically. Instead of dealing with hackers, most current works focus on defending the system against code-driven attacks. Today’s web script codes such as VBScript are receiving increasing focus as a backdoor for attacking many computers through e-mail attachments or infected web sites. The nature of these malicious codes is that they can spread widely causing serious damages to many applications. Moreover, the majority of anti-virus tools used today are able to detect known attacks but are unable to detect new and unknown attacks. The work in this thesis presents an Anomaly host based Intrusion Detection System (IDS) that provides protection against web attacks from malicious VBScripts. The core of the system treats anomalies as outliers and this IDS model uses a Multivariate Statistical technique, Principal Component Analysis (PCA) to reduce the dimensionality of the problem while keeping the major principal components of benign instances. Hence, the system can easily filter malicious scripts that deviate from normal behavior and allow for normal scripts to bypass; so any future or unknown VBScript attacks are effectively captured while maintaining a low rate of false alarms

Detecting kernel rootkits

Kernel rootkits are a special category of malware that are deployed directly in the kernel and hence have unmitigated reign over the functionalities of the kernel itself. We seek to detect such rootkits that are deployed in the real world by first observing how the majority of kernel rootkits operate. To this end, comparable to how rootkits function in the real world, we write our own kernel rootkit that manipulates the network driver, thus giving us control over all packets sent into the network. We then implement a mechanism to thwart the attacks of such rootkits by noticing that a large number of the rootkits deployed today rely heavily on the redirection of function pointers within the kernel. By overwriting the desired function pointer to its own function, a rootkit can perform a proverbial man-in-the-middle attack. Our goal is not just the detection of kernel rootkits, but also to levy as little an impact on system performance as possible. Hence our technique is to leverage existing kernel functionalities (in the case of Linux) such as kprobes to identify potential attack scenarios from within the sytem rather than from outside it (such as a VMM). We hope to introduce real-world security in devices where performance and resource constraints are tantamount to security considerations