Visible to the public Biblio

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Straub, J..  2020.  Modeling Attack, Defense and Threat Trees and the Cyber Kill Chain, ATT CK and STRIDE Frameworks as Blackboard Architecture Networks. 2020 IEEE International Conference on Smart Cloud (SmartCloud). :148—153.

Multiple techniques for modeling cybersecurity attacks and defense have been developed. The use of tree- structures as well as techniques proposed by several firms (such as Lockheed Martin's Cyber Kill Chain, Microsoft's STRIDE and the MITRE ATT&CK frameworks) have all been demonstrated. These approaches model actions that can be taken to attack or stopped to secure infrastructure and other resources, at different levels of detail.This paper builds on prior work on using the Blackboard Architecture for cyberwarfare and proposes a generalized solution for modeling framework/paradigm-based attacks that go beyond the deployment of a single exploit against a single identified target. The Blackboard Architecture Cyber Command Entity attack Route (BACCER) identification system combines rules and facts that implement attack type determination and attack decision making logic with actions that implement reconnaissance techniques and attack and defense actions. BACCER's efficacy to model examples of tree-structures and other models is demonstrated herein.

Koo, J., Kim, Y., Lee, S..  2019.  Security Requirements for Cloud-based C4I Security Architecture. 2019 International Conference on Platform Technology and Service (PlatCon). :1—4.
With the development of cloud computing technology, developed countries including the U.S. are performing the efficiency of national defense and public sector, national innovation, and construction of the infrastructure for cloud computing environment through the policies that apply cloud computing. Korea Military is also considering that apply the cloud computing technology into its national defense command control system. However, only existing security requirements for national defense information system cannot solve the problem related security vulnerabilities of cloud computing. In order to solve this problem, it is necessary to design the secure security architecture of national defense command control system considering security requirements related to cloud computing. This study analyze the security requirements needed when the U.S. military apply the cloud computing system. It also analyze existing security requirements for Korea national defense information system and security requirements for cloud computing system and draw the security requirements needed to Korea national defense information system based on cloud computing.
Kamhoua, C. A..  2018.  Game theoretic modeling of cyber deception in the Internet of Battlefield Things. 2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton). :862—862.

Internet of Battlefield Things (IoBT) devices such as actuators, sensors, wearable devises, robots, drones, and autonomous vehicles, facilitate the Intelligence, Surveillance and Reconnaissance (ISR) to Command and Control and battlefield services. IoBT devices have the ability to collect operational field data, to compute on the data, and to upload its information to the network. Securing the IoBT presents additional challenges compared with traditional information technology (IT) systems. First, IoBT devices are mass produced rapidly to be low-cost commodity items without security protection in their original design. Second, IoBT devices are highly dynamic, mobile, and heterogeneous without common standards. Third, it is imperative to understand the natural world, the physical process(es) under IoBT control, and how these real-world processes can be compromised before recommending any relevant security counter measure. Moreover, unprotected IoBT devices can be used as “stepping stones” by attackers to launch more sophisticated attacks such as advanced persistent threats (APTs). As a result of these challenges, IoBT systems are the frequent targets of sophisticated cyber attack that aim to disrupt mission effectiveness.

Sullivan, Daniel, Colbert, Edward, Cowley, Jennifer.  2018.  Mission Resilience for Future Army Tactical Networks. 2018 Resilience Week (RWS). :11—14.

Cyber-physical systems are an integral component of weapons, sensors and autonomous vehicles, as well as cyber assets directly supporting tactical forces. Mission resilience of tactical networks affects command and control, which is important for successful military operations. Traditional engineering methods for mission assurance will not scale during battlefield operations. Commanders need useful mission resilience metrics to help them evaluate the ability of cyber assets to recover from incidents to fulfill mission essential functions. We develop 6 cyber resilience metrics for tactical network architectures. We also illuminate how psychometric modeling is necessary for future research to identify resilience metrics that are both applicable to the dynamic mission state and meaningful to commanders and planners.

Eggendorfer, Tobias, Eiseler, Volker.  2019.  On the Relevance of IT Security in TDL. 2019 International Conference on Computational Science and Computational Intelligence (CSCI). :220–223.
Tactical Data Links (TDL) and Computer Science meet usually when it comes to interoperability andimplementation. However looking at it from an IT security perspective, some interesting issues occur. These become more relevant the more military hard-and software is built using commercial of the shelf (COTS) systems, that are usually implemented using standard Internet technology and software development patterns. This paper looks at Link 16, Link 11 and VMF security considerations and how compatible they are to current IT security standards. Typical security issues are discussed and concepts to mitigate them presented, which however need to be analysed for their suitability to TDL.
Baden, Mathis, Ferreira Torres, Christof, Fiz Pontiveros, Beltran Borja, State, Radu.  2019.  Whispering Botnet Command and Control Instructions. 2019 Crypto Valley Conference on Blockchain Technology (CVCBT). :77—81.
Botnets are responsible for many large scale attacks happening on the Internet. Their weak point, which is usually targeted to take down a botnet, is the command and control infrastructure: the foundation for the diffusion of the botmaster's instructions. Hence, botmasters employ stealthy communication methods to remain hidden and retain control of the botnet. Recent research has shown that blockchains can be leveraged for under the radar communication with bots, however these methods incur fees for transaction broadcasting. This paper discusses the use of a novel technology, Whisper, for command and control instruction dissemination. Whisper allows a botmaster to control bots at virtually zero cost, while providing a peer-to-peer communication infrastructure, as well as privacy and encryption as part of its dark communication strategy. It is therefore well suited for bidirectional botnet command and control operations, and creating a botnet that is very difficult to take down.
Hoffmann, Romuald.  2019.  Markov Models of Cyber Kill Chains with Iterations. 2019 International Conference on Military Communications and Information Systems (ICMCIS). :1–6.
A understanding of the nature of targeted cyber-attack processes is needed to defend against this kind of cyber threats. Generally, the models describing processes of targeted cyber attacks are called in the literature as cyber kill chains or rarely cyber-attacks life cycles. Despite the fact that cyber-attacks have random nature, almost no stochastic models of cyber kill chains bases on the theory of stochastic processes have been proposed so far. This work, attempting to fill this deficiency, proposes to start using Markov processes for modeling some cyber-attack kill chains. In this paper two example theoretical models of cycles of returning cyber-attacks are proposed which have been generally named as the models of cyber kill chains with iterations. Presented models are based on homogeneous continuous time Markov chains.
Walla, Sebastian, Rossow, Christian.  2019.  MALPITY: Automatic Identification and Exploitation of Tarpit Vulnerabilities in Malware. 2019 IEEE European Symposium on Security and Privacy (EuroS P). :590—605.
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.
Xiao, Litian, Xiao, Nan, Li, Mengyuan, Liu, Zhanqing, Wang, Fei, Li, Yuliang, Hou, Kewen.  2019.  Intelligent Architecture and Hybrid Model of Ground and Launch System for Advanced Launch Site. 2019 IEEE Aerospace Conference. :1–12.
This paper proposes an intelligent functional architecture for an advanced launch site system that is composed of five parts: the intelligent technical area, the intelligent launching region, the intelligent flight and landing area, the intelligent command and control system, and the intelligent analysis assessment system. The five parts consist of the infrastructure, facilities, equipment, hardware and software and thus include the whole mission processes of ground and launch systems from flight articles' entry to launch. The architectural framework is designed for the intelligent elements of the parts. The framework is also defined as the interrelationship and the interface of the elements, including the launch vehicle and flight payloads. Based on the Internet of Things (IoT), the framework is integrated on four levels: the physical layer, the perception layer, the network layer, and the application layer. The physical layer includes the physical objects and actuators of the launch site. The perception layer consists of the sensors and data processing system. The network layer supplies the access gateways and backbone network. The application layer serves application systems through the middleware platform. The core of the intelligent system is the controller of the automatic control system crossing the four layers. This study builds the models of the IoT, cloud platform, middleware, integrated access gateway, and automatic control system for actual ground and launch systems. A formal approach describes and defines the architecture, models and autonomous control flows in the paper. The defined models describe the physical objects, intelligent elements, interface relations, status transformation functions, etc. The test operation and launch processes are connected with the intelligent system model. This study has been applied to an individual mission project and achieved good results. The architecture and the models of this study regulate the relationship between the elements of the intelligent system. The study lays a foundation for the architectural construction, the simulation and the verification of the intelligent systems at the launch site.
Madhukar, Anant, Misra, Dinesh Kumar, Zaheer, M M.  2018.  Indigenous Network Monitoring System. 2018 International Conference on Computational and Characterization Techniques in Engineering Sciences (CCTES). :262—266.

Military reconnaissance in 1999 has paved the way to establish its own, self-reliant and indigenous navigation system. The strategic necessity has been accomplished in 2013 by launching seven satellites in Geo-orbit and underlying Network control center in Bangalore and a new NavIC control center at Lucknow, later in 2016. ISTRAC is one of the premier and amenable center to track the Indian as well as external network satellite launch vehicle and provide house-keeping and inertial navigation (INC) data to launch control center in real time and to project team in off-line. Over the ISTRAC Launch network, Simple Network Management Protocol (SNMP) was disabled due to security and bandwidth reasons. The cons of SNMP comprise security risks that are normal trait whenever applied as an open standard. There is "security through obscurity" linked with any slight-used communications standard in SNMP. Detailed messages are being sent between devices, not just miniature pre-set codes. These cons in the SNMP are found in majority applications and more bandwidth seizure is another contention. Due to the above pros and cones in SNMP in form of open source, available network monitoring system (NMS) could not be employed for link monitoring and immediate decision making in ISTRAC network. The situation has made requisitions to evolve an in-house network monitoring system (NMS). It was evolved for real-time network monitoring as well as communication link performance explication. The evolved system has the feature of Internet control message protocol (ICMP) based link monitoring, 24/7 monitoring of all the nodes, GUI based real-time link status, Summary and individual link statistics on the GUI. It also identifies total downtime and generates summary reports. It does identification for out of order or looped packets, Email and SMS alert to Prime and Redundant system which one is down and repeat alert if the link is failed for more than 30 minutes. It has easy file based configuration and no application restart required. Generation of daily and monthly link status, offline link analysis plot of any day, less consumption of system resources are add-on features. It is fully secured in-house development, calculates total data flow over a network and co-relate data vs link percentage.

Thomopoulos, Stelios C. A..  2019.  Maritime Situational Awareness Forensics Tools for a Common Information Sharing Environment (CISE). 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech). :1–5.
CISE stands for Common Information Sharing Environment and refers to an architecture and set of protocols, procedures and services for the exchange of data and information across Maritime Authorities of EU (European Union) Member States (MS's). In the context of enabling the implementation and adoption of CISE by different MS's, EU has funded a number of projects that enable the development of subsystems and adaptors intended to allow MS's to connect and make use of CISE. In this context, the Integrated Systems Laboratory (ISL) has led the development of the corresponding Hellenic and Cypriot CISE by developing a Control, Command & Information (C2I) system that unifies all partial maritime surveillance systems into one National Situational Picture Management (NSPM) system, and adaptors that allow the interconnection of the corresponding national legacy systems to CISE and the exchange of data, information and requests between the two MS's. Furthermore, a set of forensics tools that allow geospatial & time filtering and detection of anomalies, risk incidents, fake MMSIs, suspicious speed changes, collision paths, and gaps in AIS (Automatic Identification System), have been developed by combining motion models, AI, deep learning and fusion algorithms using data from different databases through CISE. This paper briefly discusses these developments within the EU CISE-2020, Hellenic CISE and CY-CISE projects and the benefits from the sharing of maritime data across CISE for both maritime surveillance and security. The prospect of using CISE for the creation of a considerably rich database that could be used for forensics analysis and detection of suspicious maritime traffic and maritime surveillance is discussed.
Hagan, Matthew, Kang, BooJoong, McLaughlin, Kieran, Sezer, Sakir.  2018.  Peer Based Tracking Using Multi-Tuple Indexing for Network Traffic Analysis and Malware Detection. 2018 16th Annual Conference on Privacy, Security and Trust (PST). :1–5.

Traditional firewalls, Intrusion Detection Systems(IDS) and network analytics tools extensively use the `flow' connection concept, consisting of five `tuples' of source and destination IP, ports and protocol type, for classification and management of network activities. By analysing flows, information can be obtained from TCP/IP fields and packet content to give an understanding of what is being transferred within a single connection. As networks have evolved to incorporate more connections and greater bandwidth, particularly from ``always on'' IoT devices and video and data streaming, so too have malicious network threats, whose communication methods have increased in sophistication. As a result, the concept of the 5 tuple flow in isolation is unable to detect such threats and malicious behaviours. This is due to factors such as the length of time and data required to understand the network traffic behaviour, which cannot be accomplished by observing a single connection. To alleviate this issue, this paper proposes the use of additional, two tuple and single tuple flow types to associate multiple 5 tuple communications, with generated metadata used to profile individual connnection behaviour. This proposed approach enables advanced linking of different connections and behaviours, developing a clearer picture as to what network activities have been taking place over a prolonged period of time. To demonstrate the capability of this approach, an expert system rule set has been developed to detect the presence of a multi-peered ZeuS botnet, which communicates by making multiple connections with multiple hosts, thus undetectable to standard IDS systems observing 5 tuple flow types in isolation. Finally, as the solution is rule based, this implementation operates in realtime and does not require post-processing and analytics of other research solutions. This paper aims to demonstrate possible applications for next generation firewalls and methods to acquire additional information from network traffic.

Elfar, Mahmoud, Zhu, Haibei, Cummings, M. L., Pajic, Miroslav.  2019.  Security-Aware Synthesis of Human-UAV Protocols. 2019 International Conference on Robotics and Automation (ICRA). :8011–8017.
In this work, we synthesize collaboration protocols for human-unmanned aerial vehicle (H-UAV) command and control systems, where the human operator aids in securing the UAV by intermittently performing geolocation tasks to confirm its reported location. We first present a stochastic game-based model for the system that accounts for both the operator and an adversary capable of launching stealthy false-data injection attacks, causing the UAV to deviate from its path. We also describe a synthesis challenge due to the UAV's hidden-information constraint. Next, we perform human experiments using a developed RESCHU-SA testbed to recognize the geolocation strategies that operators adopt. Furthermore, we deploy machine learning techniques on the collected experimental data to predict the correctness of a geolocation task at a given location based on its geographical features. By representing the model as a delayed-action game and formalizing the system objectives, we utilize off-the-shelf model checkers to synthesize protocols for the human-UAV coalition that satisfy these objectives. Finally, we demonstrate the usefulness of the H-UAV protocol synthesis through a case study where the protocols are experimentally analyzed and further evaluated by human operators.
Nan, Z., Zhai, L., Zhai, L., Liu, H..  2018.  Botnet Homology Method Based on Symbolic Approximation Algorithm of Communication Characteristic Curve. 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). :1-6.

The IRC botnet is the earliest and most significant botnet group that has a significant impact. Its characteristic is to control multiple zombies hosts through the IRC protocol and constructing command control channels. Relevant research analyzes the large amount of network traffic generated by command interaction between the botnet client and the C&C server. Packet capture traffic monitoring on the network is currently a more effective detection method, but this information does not reflect the essential characteristics of the IRC botnet. The increase in the amount of erroneous judgments has often occurred. To identify whether the botnet control server is a homogenous botnet, dynamic network communication characteristic curves are extracted. For unequal time series, dynamic time warping distance clustering is used to identify the homologous botnets by category, and in order to improve detection. Speed, experiments will use SAX to reduce the dimension of the extracted curve, reducing the time cost without reducing the accuracy.

Leemaster, J., Vai, M., Whelihan, D., Whitman, H., Khazan, R..  2018.  Functionality and Security Co-Design Environment for Embedded Systems. 2018 IEEE High Performance Extreme Computing Conference (HPEC). :1-5.

For decades, embedded systems, ranging from intelligence, surveillance, and reconnaissance (ISR) sensors to electronic warfare and electronic signal intelligence systems, have been an integral part of U.S. Department of Defense (DoD) mission systems. These embedded systems are increasingly the targets of deliberate and sophisticated attacks. Developers thus need to focus equally on functionality and security in both hardware and software development. For critical missions, these systems must be entrusted to perform their intended functions, prevent attacks, and even operate with resilience under attacks. The processor in a critical system must thus provide not only a root of trust, but also a foundation to monitor mission functions, detect anomalies, and perform recovery. We have developed a Lincoln Asymmetric Multicore Processing (LAMP) architecture, which mitigates adversarial cyber effects with separation and cryptography and provides a foundation to build a resilient embedded system. We will describe a design environment that we have created to enable the co-design of functionality and security for mission assurance.

Ayoade, G., Chandra, S., Khan, L., Hamlen, K., Thuraisingham, B..  2018.  Automated Threat Report Classification over Multi-Source Data. 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC). :236–245.

With an increase in targeted attacks such as advanced persistent threats (APTs), enterprise system defenders require comprehensive frameworks that allow them to collaborate and evaluate their defense systems against such attacks. MITRE has developed a framework which includes a database of different kill-chains, tactics, techniques, and procedures that attackers employ to perform these attacks. In this work, we leverage natural language processing techniques to extract attacker actions from threat report documents generated by different organizations and automatically classify them into standardized tactics and techniques, while providing relevant mitigation advisories for each attack. A naïve method to achieve this is by training a machine learning model to predict labels that associate the reports with relevant categories. In practice, however, sufficient labeled data for model training is not always readily available, so that training and test data come from different sources, resulting in bias. A naïve model would typically underperform in such a situation. We address this major challenge by incorporating an importance weighting scheme called bias correction that efficiently utilizes available labeled data, given threat reports, whose categories are to be automatically predicted. We empirically evaluated our approach on 18,257 real-world threat reports generated between year 2000 and 2018 from various computer security organizations to demonstrate its superiority by comparing its performance with an existing approach.

Cho, S., Han, I., Jeong, H., Kim, J., Koo, S., Oh, H., Park, M..  2018.  Cyber Kill Chain based Threat Taxonomy and its Application on Cyber Common Operational Picture. 2018 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA). :1–8.

Over a decade, intelligent and persistent forms of cyber threats have been damaging to the organizations' cyber assets and missions. In this paper, we analyze current cyber kill chain models that explain the adversarial behavior to perform advanced persistent threat (APT) attacks, and propose a cyber kill chain model that can be used in view of cyber situation awareness. Based on the proposed cyber kill chain model, we propose a threat taxonomy that classifies attack tactics and techniques for each attack phase using CAPEC, ATT&CK that classify the attack tactics, techniques, and procedures (TTPs) proposed by MITRE. We also implement a cyber common operational picture (CyCOP) to recognize the situation of cyberspace. The threat situation can be represented on the CyCOP by applying cyber kill chain based threat taxonomy.

Das, A., Shen, M. Y., Shashanka, M., Wang, J..  2017.  Detection of Exfiltration and Tunneling over DNS. 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). :737–742.

This paper proposes a method to detect two primary means of using the Domain Name System (DNS) for malicious purposes. We develop machine learning models to detect information exfiltration from compromised machines and the establishment of command & control (C&C) servers via tunneling. We validate our approach by experiments where we successfully detect a malware used in several recent Advanced Persistent Threat (APT) attacks [1]. The novelty of our method is its robustness, simplicity, scalability, and ease of deployment in a production environment.

McLaren, P., Russell, G., Buchanan, B..  2017.  Mining Malware Command and Control Traces. 2017 Computing Conference. :788–794.

Detecting botnets and advanced persistent threats is a major challenge for network administrators. An important component of such malware is the command and control channel, which enables the malware to respond to controller commands. The detection of malware command and control channels could help prevent further malicious activity by cyber criminals using the malware. Detection of malware in network traffic is traditionally carried out by identifying specific patterns in packet payloads. Now bot writers encrypt the command and control payloads, making pattern recognition a less effective form of detection. This paper focuses instead on an effective anomaly based detection technique for bot and advanced persistent threats using a data mining approach combined with applied classification algorithms. After additional tuning, the final test on an unseen dataset, false positive rates of 0% with malware detection rates of 100% were achieved on two examined malware threats, with promising results on a number of other threats.

Gutzwiller, R. S., Reeder, J..  2017.  Human interactive machine learning for trust in teams of autonomous robots. 2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA). :1–3.

Unmanned systems are increasing in number, while their manning requirements remain the same. To decrease manpower demands, machine learning techniques and autonomy are gaining traction and visibility. One barrier is human perception and understanding of autonomy. Machine learning techniques can result in “black box” algorithms that may yield high fitness, but poor comprehension by operators. However, Interactive Machine Learning (IML), a method to incorporate human input over the course of algorithm development by using neuro-evolutionary machine-learning techniques, may offer a solution. IML is evaluated here for its impact on developing autonomous team behaviors in an area search task. Initial findings show that IML-generated search plans were chosen over plans generated using a non-interactive ML technique, even though the participants trusted them slightly less. Further, participants discriminated each of the two types of plans from each other with a high degree of accuracy, suggesting the IML approach imparts behavioral characteristics into algorithms, making them more recognizable. Together the results lay the foundation for exploring how to team humans successfully with ML behavior.

Alejandre, F. V., Cortés, N. C., Anaya, E. A..  2017.  Feature selection to detect botnets using machine learning algorithms. 2017 International Conference on Electronics, Communications and Computers (CONIELECOMP). :1–7.

In this paper, a novel method to do feature selection to detect botnets at their phase of Command and Control (C&C) is presented. A major problem is that researchers have proposed features based on their expertise, but there is no a method to evaluate these features since some of these features could get a lower detection rate than other. To this aim, we find the feature set based on connections of botnets at their phase of C&C, that maximizes the detection rate of these botnets. A Genetic Algorithm (GA) was used to select the set of features that gives the highest detection rate. We used the machine learning algorithm C4.5, this algorithm did the classification between connections belonging or not to a botnet. The datasets used in this paper were extracted from the repositories ISOT and ISCX. Some tests were done to get the best parameters in a GA and the algorithm C4.5. We also performed experiments in order to obtain the best set of features for each botnet analyzed (specific), and for each type of botnet (general) too. The results are shown at the end of the paper, in which a considerable reduction of features and a higher detection rate than the related work presented were obtained.

Weckstén, M., Frick, J., Sjöström, A., Järpe, E..  2016.  A novel method for recovery from Crypto Ransomware infections. 2016 2nd IEEE International Conference on Computer and Communications (ICCC). :1354–1358.

Extortion using digital platforms is an increasing form of crime. A commonly seen problem is extortion in the form of an infection of a Crypto Ransomware that encrypts the files of the target and demands a ransom to recover the locked data. By analyzing the four most common Crypto Ransomwares, at writing, a clear vulnerability is identified; all infections rely on tools available on the target system to be able to prevent a simple recovery after the attack has been detected. By renaming the system tool that handles shadow copies it is possible to recover from infections from all four of the most common Crypto Ransomwares. The solution is packaged in a single, easy to use script.

K. F. Hong, C. C. Chen, Y. T. Chiu, K. S. Chou.  2015.  "Scalable command and control detection in log data through UF-ICF analysis". 2015 International Carnahan Conference on Security Technology (ICCST). :293-298.

During an advanced persistent threat (APT), an attacker group usually establish more than one C&C server and these C&C servers will change their domain names and corresponding IP addresses over time to be unseen by anti-virus software or intrusion prevention systems. For this reason, discovering and catching C&C sites becomes a big challenge in information security. Based on our observations and deductions, a malware tends to contain a fixed user agent string, and the connection behaviors generated by a malware is different from that by a benign service or a normal user. This paper proposed a new method comprising filtering and clustering methods to detect C&C servers with a relatively higher coverage rate. The experiments revealed that the proposed method can successfully detect C&C Servers, and the can provide an important clue for detecting APT.

J. Vukalović, D. Delija.  2015.  "Advanced Persistent Threats - detection and defense". 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). :1324-1330.

The term “Advanced Persistent Threat” refers to a well-organized, malicious group of people who launch stealthy attacks against computer systems of specific targets, such as governments, companies or military. The attacks themselves are long-lasting, difficult to expose and often use very advanced hacking techniques. Since they are advanced in nature, prolonged and persistent, the organizations behind them have to possess a high level of knowledge, advanced tools and competent personnel to execute them. The attacks are usually preformed in several phases - reconnaissance, preparation, execution, gaining access, information gathering and connection maintenance. In each of the phases attacks can be detected with different probabilities. There are several ways to increase the level of security of an organization in order to counter these incidents. First and foremost, it is necessary to educate users and system administrators on different attack vectors and provide them with knowledge and protection so that the attacks are unsuccessful. Second, implement strict security policies. That includes access control and restrictions (to information or network), protecting information by encrypting it and installing latest security upgrades. Finally, it is possible to use software IDS tools to detect such anomalies (e.g. Snort, OSSEC, Sguil).

K. F. Hong, C. C. Chen, Y. T. Chiu, K. S. Chou.  2015.  "Ctracer: Uncover C amp;amp;C in Advanced Persistent Threats Based on Scalable Framework for Enterprise Log Data". 2015 IEEE International Congress on Big Data. :551-558.

Advanced Persistent Threat (APT), unlike traditional hacking attempts, carries out specific attacks on a specific target to illegally collect information and data from it. These targeted attacks use special-crafted malware and infrequent activity to avoid detection, so that hackers can retain control over target systems unnoticed for long periods of time. In order to detect these stealthy activities, a large-volume of traffic data generated in a period of time has to be analyzed. We proposed a scalable solution, Ctracer to detect stealthy command and control channel in a large-volume of traffic data. APT uses multiple command and control (C&C) channel and change them frequently to avoid detection, but there are common signatures in those C&C sessions. By identifying common network signature, Ctracer is able to group the C&C sessions. Therefore, we can detect an APT and all the C&C session used in an APT attack. The Ctracer is evaluated in a large enterprise for four months, twenty C&C servers, three APT attacks are reported. After investigated by the enterprise's Security Operations Center (SOC), the forensic report shows that there is specific enterprise targeted APT cases and not ever discovered for over 120 days.