Biblio

In malware behavior analysis, there are limitations in the analysis method of control flow and data flow. Researchers analyzed data flow by dynamic taint analysis tools, however, it cost a lot. In this paper, we proposed a method of generating malware summary based on semantic behavior graphs (SBGs, Semantic Behavior Graphs) to address this issue. In this paper, we considered various situation where behaviors be capable of being associated, thus an algorithm of generating semantic behavior graphs was given firstly. Semantic behavior graphs are composed of behavior nodes and associated data edges. Then, we extracted behaviors and logical relationships between behaviors from semantic behavior graphs, and finally generated a summary of malware behaviors with true intension. Experimental results showed that our approach can effectively identify and describe malicious behaviors and generate accurate behavior summary.

This paper proposes a basic strategy for Botnet Defense System (BDS). BDS is a cybersecurity system that utilizes white-hat botnets to defend IoT systems against malicious botnets. Once a BDS detects a malicious botnet, it launches white-hat worms in order to drive out the malicious botnet. The proposed strategy aims at the proper use of the worms based on the worms' capability such as lifespan and secondary infectivity. If the worms have high secondary infectivity or a long lifespan, the BDS only has to launch a few worms. Otherwise, it should launch as many worms as possible. The effectiveness of the strategy was confirmed through the simulation evaluation using agent-oriented Petri nets.

This paper proposes a deep learning-based white-hat worm launcher in Botnet Defense System (BDS). BDS uses white-hat botnets to defend an IoT system against malicious botnets. White-hat worm launcher literally launches white-hat worms to create white-hat botnets according to the strategy decided by BDS. The proposed launcher learns with deep learning where is the white-hat worms' right place to successfully drive out malicious botnets. Given a system situation invaded by malicious botnets, it predicts a worms' placement by the learning result and launches them. We confirmed the effect of the proposed launcher through simulating evaluation.

During the Cold War era, countries with asymmetrical relationships often demonstrated how lower-tier nation states required the alliance and support from top-tier nation states. This statement no longer stands true as country such as North Korea has exploited global financial institutions through various malware such as WANNACRY V0, V1, V2, evtsys.exe, and BRAMBUL WORM. Top tier nation states such as the U.S. are unable to use diplomatic clout or to retaliate against the deferrer. Our study examined the affidavit filed against the North Korean hacker, Park Jin Hyok, which was provided by the FBI. Our paper focuses on the operations and campaigns that were carried out by the Lazarus Group by focusing on the key factors of the infrastructure and artifacts. Due to the nature of the cyber deterrence, deterrence in the cyber realm is far complex than the nuclear deterrence. We focused on the Sony Picture Entertainment’s incident for our study. In this study, we discuss how cyber deterrence can be employed when different nation states share an asymmetrical relationship. Furthermore, we focus on contestability and attribution that is a key factor that makes cyber deterrence difficult.

The quantity of Internet of Things (IoT) devices in the marketplace and lack of security is staggering. The interconnectedness of IoT devices has increased the attack surface for hackers. "White Worm" technology has the potential to combat infiltrating malware. Before white worm technology becomes viable, its capabilities must be constrained to specific devices and limited to non-harmful actions. This paper addresses the current problem, international research, and the conflicting interest of individuals, businesses, and governments regarding white worm technology. Proposed is a new perspective on utilizing white worm technology to protect the vulnerability of IoT devices, while overcoming its challenges.

Law enforcement agencies regularly take down botnets as the ultimate defense against global malware operations. By arresting malware authors, and simultaneously infiltrating or shutting down a botnet's network infrastructures (such as C2 servers), defenders stop global threats and mitigate pending infections. In this paper, we propose malware tarpits, an orthogonal defense that does not require seizing botnet infrastructures, and at the same time can also be used to slow down malware spreading and infiltrate its monetization techniques. A tarpit is a network service that causes a client to stay busy with a network operation. Our work aims to automatically identify network operations used by malware that will block the malware either forever or for a significant amount of time. We describe how to non-intrusively exploit such tarpit vulnerabilities in malware to slow down or, ideally, even stop malware. Using dynamic malware analysis, we monitor how malware interacts with the POSIX and Winsock socket APIs. From this, we infer network operations that would have blocked when provided certain network inputs. We augment this vulnerability search with an automated generation of tarpits that exploit the identified vulnerabilities. We apply our prototype MALPITY on six popular malware families and discover 12 previously-unknown tarpit vulnerabilities, revealing that all families are susceptible to our defense. We demonstrate how to, e.g., halt Pushdo's DGA-based C2 communication, hinder SalityP2P peers from receiving commands or updates, and stop Bashlite's spreading engine.

Computer worms pose a major threat to computer and communication networks due to the rapid speed at which they propagate. Biologically based epidemic models have been widely used to analyze the propagation of worms in computer networks. For an air-gapped network with an insider threat, we propose a modified Susceptible-Exposed-Infected-Quarantined-Vaccinated (SEIQV) model called the Susceptible-Exposed-Infected-Quarantined-Patched (SEIQP) model. We describe the assumptions that apply to this model, define a set of differential equations that characterize the system dynamics, and solve for the basic reproduction number. We then simulate and analyze the parameters controlled by the insider threat to determine where resources should be allocated to attain different objectives and results.

Wireless Sensor Network (WSN) faces critical security challenges due to malware(worm, virus, malicious code etc.) attack. When a single node gets compromised by malware then start to spread in entire sensor network through neighboring sensor nodes. To understand the dynamics of malware propagation in WSN proposed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model. This model used the concept of epidemiology. The model focused on early detection of malicious signals presence in the network and accordingly application of security mechanism for its removal. The early detection method helps in controlling of malware spread and reduce battery consumption of sensor nodes. In this paper study the dynamics of malware propagation and stability analysis of the system. In epidemiology basic reproduction number is a crucial parameter which is used for the determination of malware status in the system. The expression of basic reproduction number has been obtained. Analyze the propagation dynamics and compared with previous model. The proposed model provides improved security mechanism in comparison to previous one. The extensive simulation results conform the analytical investigation and accuracy of proposed model.

Peer-to-peer computing (P2P) refers to the famous technology that provides peers an equal spontaneous collaboration in the network by using appropriate information and communication systems without the need for a central server coordination. Today, the interconnection of several P2P networks has become a genuine solution for increasing system reliability, fault tolerance and resource availability. However, the existence of security threats in such networks, allows us to investigate the safety of users from P2P threats by studying the effects of competition between these interconnected networks. In this paper, we present an e-epidemic model to characterize the worm propagation in an interconnected peer-to-peer network. Here, we address this issue by introducing a model of network competition where an unprotected network is willing to partially weaken its own safety in order to more severely damage a more protected network. The unprotected network can infect all peers in the competitive networks after their non react against the passive worm propagation. Our model also evaluated the effect of an immunization strategies adopted by the protected network to resist against attacking networks. The launch time of immunization strategies in the protected network, the number of peers synapse connected to the both networks, and other effective parameters have also been investigated in this paper.

With the continuous development of mobile based Wireless technologies, Bluetooth plays a vital role in smart-phone Era. In such scenario, the security measures are needed to be enhanced for Bluetooth. We propose a Node Energy Based Virus Propagation Model (NBV) for Bluetooth. The algorithm works with key features of node capacity and node energy in Bluetooth network. This proposed NBV model works along with E-mail worm Propagation model. Finally, this work simulates and compares the virus propagation with respect to Node Energy and network traffic.

Malware technology makes it difficult for malware analyst to detect same malware files with different obfuscation technique. In this paper we are trying to tackle that problem by analyzing the sequence of system call from an executable file. Malware files which actually are the same should have almost identical or at least a similar sequence of system calls. In this paper, we are going to create a model for each malware class consists of malwares from different families based on its sequence of system calls. Method/algorithm that's used in this paper is profile hidden markov model which is a very well-known tool in the biological informatics field for comparing DNA and protein sequences. Malware classes that we are going to build are trojan and worm class. Accuracy for these classes are pretty high, it's above 90% with also a high false positive rate around 37%.

As the centers of knowledge, discovery, and intellectual exploration, US universities provide appealing cybersecurity targets. Cyberattack origin patterns and relationships are not evident until data is visualized in maps and tested with statistical models. The current cybersecurity threat detection software utilized by University of North Florida's IT department records large amounts of attacks and attempted intrusions by the minute. This paper presents GIS mapping and spatial analysis of cybersecurity attacks on UNF. First, locations of cyberattack origins were detected by geographic Internet Protocol (GEO-IP) software. Second, GIS was used to map the cyberattack origin locations. Third, we used advanced spatial statistical analysis functions (exploratory spatial data analysis and spatial point pattern analysis) and R software to explore cyberattack patterns. The spatial perspective we promote is novel because there are few studies employing location analytics and spatial statistics in cyber-attack detection and prevention research.

A fundamental drawback of current anomaly detection systems (ADSs) is the ability of a skilled attacker to evade detection. This is due to the flawed assumption that an attacker does not have any information about an ADS. Advanced persistent threats that are capable of monitoring network behavior can always estimate some information about ADSs which makes these ADSs susceptible to evasion attacks. Hence in this paper, we first assume the role of an attacker to launch evasion attacks on anomaly detection systems. We show that the ADSs can be completely paralyzed by parameter estimation attacks. We then present a mathematical model to measure evasion margin with the aim to understand the science of evasion due to ADS design. Finally, to minimize the evasion margin, we propose a key-based randomization scheme for existing ADSs and discuss its robustness against evasion attacks. Case studies are presented to illustrate the design methodology and extensive experimentation is performed to corroborate the results.

Zero-day polymorphic worms pose a serious threat to the Internet security. With their ability to rapidly propagate, these worms increasingly threaten the Internet hosts and services. Not only can they exploit unknown vulnerabilities but can also change their own representations on each new infection or can encrypt their payloads using a different key per infection. They have many variations in the signatures of the same worm thus, making their fingerprinting very difficult. Therefore, signature-based defenses and traditional security layers miss these stealthy and persistent threats. This paper provides a detailed survey to outline the research efforts in relation to detection of modern zero-day malware in form of zero-day polymorphic worms.

Deadlock freedom is a key challenge in the design of communication networks. Wormhole switching is a popular switching technique, which is also prone to deadlocks. Deadlock analysis of routing functions is a manual and complex task. We propose an algorithm that automatically proves routing functions deadlock-free or outputs a minimal counter-example explaining the source of the deadlock. Our algorithm is the first to automatically check a necessary and sufficient condition for deadlock-free routing. We illustrate its efficiency in a complex adaptive routing function for torus topologies. Results are encouraging. Deciding deadlock freedom is co-NP-Complete for wormhole networks. Nevertheless, our tool proves a 13 × 13 torus deadlock-free within seconds. Finding minimal deadlocks is more difficult. Our tool needs four minutes to find a minimal deadlock in a 11 × 11 torus while it needs nine hours for a 12 × 12 network.

This work proposes a novel approach to infer and characterize Internet-scale DNS amplification DDoS attacks by leveraging the darknet space. Complementary to the pioneer work on inferring Distributed Denial of Service (DDoS) using darknet, this work shows that we can extract DDoS activities without relying on backscattered analysis. The aim of this work is to extract cyber security intelligence related to DNS Amplification DDoS activities such as detection period, attack duration, intensity, packet size, rate and geo- location in addition to various network-layer and flow-based insights. To achieve this task, the proposed approach exploits certain DDoS parameters to detect the attacks. We empirically evaluate the proposed approach using 720 GB of real darknet data collected from a /13 address space during a recent three months period. Our analysis reveals that the approach was successful in inferring significant DNS amplification DDoS activities including the recent prominent attack that targeted one of the largest anti-spam organizations. Moreover, the analysis disclosed the mechanism of such DNS amplification DDoS attacks. Further, the results uncover high-speed and stealthy attempts that were never previously documented. The case study of the largest DDoS attack in history lead to a better understanding of the nature and scale of this threat and can generate inferences that could contribute in detecting, preventing, assessing, mitigating and even attributing of DNS amplification DDoS activities.
 

A slow-paced persistent attack, such as slow worm or bot, can bewilder the detection system by slowing down their attack. Detecting such attacks based on traditional anomaly detection techniques may yield high false alarm rates. In this paper, we frame our problem as detecting slow-paced persistent attacks from a time series obtained from network trace. We focus on time series spectrum analysis to identify peculiar spectral patterns that may represent the occurrence of a persistent activity in the time domain. We propose a method to adaptively detect slow-paced persistent attacks in a time series and evaluate the proposed method by conducting experiments using both synthesized traffic and real-world traffic. The results show that the proposed method is capable of detecting slow-paced persistent attacks even in a noisy environment mixed with legitimate traffic.

Wireless sensor networks (WSNs) are prone to propagating malware because of special characteristics of sensor nodes. Considering the fact that sensor nodes periodically enter sleep mode to save energy, we develop traditional epidemic theory and construct a malware propagation model consisting of seven states. We formulate differential equations to represent the dynamics between states. We view the decision-making problem between system and malware as an optimal control problem; therefore, we formulate a malware-defense differential game in which the system can dynamically choose its strategies to minimize the overall cost whereas the malware intelligently varies its strategies over time to maximize this cost. We prove the existence of the saddle-point in the game. Further, we attain optimal dynamic strategies for the system and malware, which are bang-bang controls that can be conveniently operated and are suitable for sensor nodes. Experiments identify factors that influence the propagation of malware. We also determine that optimal dynamic strategies can reduce the overall cost to a certain extent and can suppress the malware propagation. These results support a theoretical foundation to limit malware in WSNs.

This work proposes a novel approach to infer and characterize Internet-scale DNS amplification DDoS attacks by leveraging the darknet space. Complementary to the pioneer work on inferring Distributed Denial of Service (DDoS) using darknet, this work shows that we can extract DDoS activities without relying on backscattered analysis. The aim of this work is to extract cyber security intelligence related to DNS Amplification DDoS activities such as detection period, attack duration, intensity, packet size, rate and geo- location in addition to various network-layer and flow-based insights. To achieve this task, the proposed approach exploits certain DDoS parameters to detect the attacks. We empirically evaluate the proposed approach using 720 GB of real darknet data collected from a /13 address space during a recent three months period. Our analysis reveals that the approach was successful in inferring significant DNS amplification DDoS activities including the recent prominent attack that targeted one of the largest anti-spam organizations. Moreover, the analysis disclosed the mechanism of such DNS amplification DDoS attacks. Further, the results uncover high-speed and stealthy attempts that were never previously documented. The case study of the largest DDoS attack in history lead to a better understanding of the nature and scale of this threat and can generate inferences that could contribute in detecting, preventing, assessing, mitigating and even attributing of DNS amplification DDoS activities.