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1. Active and passive mechanics for rugose terrain traversal in centipedes

   https://doi.org/10.1242/jeb.244688  

   Diaz, Kelimar; Erickson, Eva; Chong, Baxi; Soto, Daniel; Goldman, Daniel I. (February 2023, Journal of Experimental Biology)

   ABSTRACT Centipedes coordinate body and limb flexion to generate propulsion. On flat, solid surfaces, the limb-stepping patterns can be characterized according to the direction in which limb-aggregates propagate, opposite to (retrograde) or with the direction of motion (direct). It is unknown how limb and body dynamics are modified in terrain with terradynamic complexity more representative of these animal's natural heterogeneous environments. Here, we investigated how centipedes that use retrograde and direct limb-stepping patterns, Scolopendra polymorpha and Scolopocryptops sexspinosus, respectively, coordinate their body and limbs to navigate laboratory environments which present footstep challenges and terrain rugosity. We recorded the kinematics and measured the locomotive performance of these animals traversing two rugose terrains with randomly distributed step heights and compared the kinematics with those on a flat frictional surface. Scolopendra polymorpha exhibited similar body and limb dynamics across all terrains and a decrease in speed with increased terrain rugosity. Unexpectedly, when placed in a rugose terrain, S. sexspinosus changed the direction of the limb-stepping pattern from direct to retrograde. Further, for both species, traversal of these rugose terrains was facilitated by hypothesized passive mechanics: upon horizontal collision of a limb with a block, the limb bent and later continued the stepping pattern. Although centipedes have many degrees of freedom, our results suggest these animals negotiate limb–substrate interactions and navigate complex terrains leveraging the innate flexibility of their limbs to simplify control. 

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2. Adversarial Anomaly Detection Using Centroid-Based Clustering

   https://doi.org/10.1109/IRI.2018.00009  

   Anindya, Imrul Chowdhury; Kantarcioglu, Murat (July 2018, 2018 IEEE International Conference on Information Reuse and Integration (IRI))

   As cyber attacks are growing with an unprecedented rate in the recent years, organizations are seeking an efficient and scalable solution towards a holistic protection system. As the adversaries are becoming more skilled and organized, traditional rule based detection systems have been proved to be quite ineffective against the continuously evolving cyber attacks. Consequently, security researchers are focusing on applying machine learning techniques and big data analytics to defend against cyber attacks. Over the recent years, several anomaly detection systems have been claimed to be quite successful against the sophisticated cyber attacks including the previously unseen zero-day attacks. But often, these systems do not consider the adversary's adaptive attacking behavior for bypassing the detection procedure. As a result, deploying these systems in active real-world scenarios fails to provide significant benefits in the presence of intelligent adversaries that are carefully manipulating the attack vectors. In this work, we analyze the adversarial impact on anomaly detection models that are built upon centroid-based clustering from game-theoretic aspect and propose adversarial anomaly detection technique for these models. The experimental results show that our game-theoretic anomaly detection models can withstand attacks more effectively compared to the traditional models. 

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3. Deceiving Cyber Adversaries: A Game Theoretic Approach

   Schlenker, Aaron; Thakoor, Omkar; Xu, Haifeng; Tambe, Milind; Vayanos, Phebe; Fang, Fei; Tran-Thanh, Long; Vorobeychik, Yevgeniy (January 2018, International Conference on Autonomous Agents and Multiagent Systems)

   An important way cyber adversaries ind vulnerabilities in mod- ern networks is through reconnaissance, in which they attempt to identify coniguration speciics of network hosts. To increase un- certainty of adversarial reconnaissance, the network administrator (henceforth, defender) can introduce deception into responses to network scans, such as obscuring certain system characteristics. We introduce a novel game theoretic model of deceptive interac- tions of this kind between a defender and a cyber attacker, which we call the Cyber Deception Game. We consider both a powerful (rational) attacker, who is aware of the defender’s exact deception strategy, and a naive attacker who is not. We show that computing the optimal deception strategy is NP-hard for both types of attackers. For the case with a powerful attacker, we provide a mixed-integer linear program solution as well as a fast and efective greedy algo- rithm. Similarly, we provide complexity results and propose exact and heuristic approaches when the attacker is naive. Our exten- sive experimental analysis demonstrates the efectiveness of our approaches. 

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4. Survey and Taxonomy of Adversarial Reconnaissance Techniques

   https://doi.org/10.1145/3538704  

   Roy, Shanto; Sharmin, Nazia; Acosta, Jaime C.; Kiekintveld, Christopher; Laszka, Aron (July 2023, ACM Computing Surveys)

   Adversaries are often able to penetrate networks and compromise systems by exploiting vulnerabilities in people and systems. The key to the success of these attacks is information that adversaries collect throughout the phases of the cyber kill chain. We summarize and analyze the methods, tactics, and tools that adversaries use to conduct reconnaissance activities throughout the attack process. First, we discuss what types of information adversaries seek and how and when they can obtain this information. Then, we provide a taxonomy and detailed overview of adversarial reconnaissance techniques. The taxonomy introduces a categorization of reconnaissance techniques based on the source as third-party and human-, and system-based information gathering. This article provides a comprehensive view of adversarial reconnaissance that can help in understanding and modeling this complex but vital aspect of cyber attacks as well as insights that can improve defensive strategies, such as cyber deception. 

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5. Generating Fake Cyber Threat Intelligence Using Transformer-Based Models

   Ranade, P.; Piplai, A.; Mittal, S.; Joshi, A.; Finin, T (July 2021, International Joint Conference on Neural Networks 2021 (IJCNN 2021))

   Cyber-defense systems are being developed to automatically ingest Cyber Threat Intelligence (CTI) that contains semi-structured data and/or text to populate knowledge graphs. A potential risk is that fake CTI can be generated and spread through Open-Source Intelligence (OSINT) communities or on the Web to effect a data poisoning attack on these systems. Adversaries can use fake CTI examples as training input to subvert cyber defense systems, forcing the model to learn incorrect inputs to serve their malicious needs. In this paper, we automatically generate fake CTI text descriptions using transformers. We show that given an initial prompt sentence, a public language model like GPT-2 with fine-tuning, can generate plausible CTI text with the ability of corrupting cyber-defense systems. We utilize the generated fake CTI text to perform a data poisoning attack on a Cybersecurity Knowledge Graph (CKG) and a cybersecurity corpus. The poisoning attack introduced adverse impacts such as returning incorrect reasoning outputs, representation poisoning, and corruption of other dependent AI-based cyber defense systems. We evaluate with traditional approaches and conduct a human evaluation study with cybersecurity professionals and threat hunters. Based on the study, professional threat hunters were equally likely to consider our fake generated CTI as true. 

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