Relevant bibliographies by topics / Detection of Trojans

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Detection of Trojans'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Detection of Trojans"

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Hazra, Suvadip, and Mamata Dalui. "CA-Based Detection of Coherence Exploiting Hardware Trojans." Journal of Circuits, Systems and Computers 29, no. 08 (October 18, 2019): 2050120. http://dx.doi.org/10.1142/s0218126620501200.

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Nowadays, Hardware Trojan threats have become inevitable due to the growing complexities of Integrated Circuits (ICs) as well as the current trend of Intellectual Property (IP)-based hardware designs. An adversary can insert a Hardware Trojan during any of its life cycle phases — the design, fabrication or even at manufacturing phase. Once a Trojan is inserted into a system, it can cause an unwanted modification to system functionality which may degrade system performance or sometimes Trojans are implanted with the target to leak secret information. Once Trojans are implanted, they are hard to detect and impossible to remove from the system as they are already fabricated into the chip. In this paper, we propose three stealthy Trojan models which affect the coherence mechanism of Chip Multiprocessors’ (CMPs) cache system by arbitrarily modifying the cache block state which in turn may leave the cache line states as incoherent. We have evaluated the payload of such modeled Trojans and proposed a cellular automaton (CA)-based solution for detection of such Trojans.
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Wang, Lian Hai, and Qiu Liang Xu. "An APT Trojan Detection Method Based on Memory Forensics Techniques." Applied Mechanics and Materials 701-702 (December 2014): 927–34. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.927.

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Advanced Persistent Threat (APT) is currently reported to be one of the most serious threats. It is very important to detect the APT Trojan as early as possible. There are three types of approaches to conduct APT detection: network traffic analysis, change controlling and sandboxing. Unfortunately, all these approaches have limitations in detecting unknown APT Trojans. This paper proposes a novel APT Trojan detection method by utilizing memory forensics techniques. The proposed method first acquires the raw physical memory image from a target running system and then finds the APT’s traces in the memory image based on the ATP’s characteristics and memory forensics techniques. If enough traces are found, we can judge that there must be Trojans in the target system. Experimental results show that the proposed method can effectively detect new APT Trojans.
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Zhao, Meng Meng, and Lian Hai Wang. "Research on Trojan Detection Method of Computer Memory Mirroring." Applied Mechanics and Materials 701-702 (December 2014): 1013–17. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.1013.

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Trojan detection plays an important role in the discovery and treatment of Trojans. Acquisition and analysis of memory mirroring is a new research topic of computer live forensics. Computer forensics often need Trojan detection to determine whether target machine has been controlled. This paper proposed a Trojan detection method based on computer live forensics. Construct probabilistic fuzzy cognitive map(PFCM) through analysis of memory mirroring, use memory mirroring Trojan detection algorithm, calculate the probability of the existence of Trojan. The results showed that this method can effectively determine whether there were Trojan in memory mirroring. Detect Trojans through the analysis of various aspects of memory and numerical computation, proposed method improve the accuracy and reliability of Trojan detection.
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Rooney, Catherine, Amar Seeam, and Xavier Bellekens. "Creation and Detection of Hardware Trojans Using Non-Invasive Off-The-Shelf Technologies." Electronics 7, no. 7 (July 22, 2018): 124. http://dx.doi.org/10.3390/electronics7070124.

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As a result of the globalisation of the semiconductor design and fabrication processes, integrated circuits are becoming increasingly vulnerable to malicious attacks. The most concerning threats are hardware trojans. A hardware trojan is a malicious inclusion or alteration to the existing design of an integrated circuit, with the possible effects ranging from leakage of sensitive information to the complete destruction of the integrated circuit itself. While the majority of existing detection schemes focus on test-time, they all require expensive methodologies to detect hardware trojans. Off-the-shelf approaches have often been overlooked due to limited hardware resources and detection accuracy. With the advances in technologies and the democratisation of open-source hardware, however, these tools enable the detection of hardware trojans at reduced costs during or after production. In this manuscript, a hardware trojan is created and emulated on a consumer FPGA board. The experiments to detect the trojan in a dormant and active state are made using off-the-shelf technologies taking advantage of different techniques such as Power Analysis Reports, Side Channel Analysis and Thermal Measurements. Furthermore, multiple attempts to detect the trojan are demonstrated and benchmarked. Our simulations result in a state-of-the-art methodology to accurately detect the trojan in both dormant and active states using off-the-shelf hardware.
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Prathivi, Rastri, and Vensy Vydia. "ANALISA PENDETEKSIAN WORM dan TROJAN PADA JARINGAN INTERNET UNIVERSITAS SEMARANG MENGGUNAKAN METODE KALSIFIKASI PADA DATA MINING C45 dan BAYESIAN NETWORK." Jurnal Transformatika 14, no. 2 (January 30, 2017): 77. http://dx.doi.org/10.26623/transformatika.v14i2.440.

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<p>Worm attacks become a dangerous threat and cause damage in the Internet network. If the Internet network worms and trojan attacks the very disruption of traffic data as well as create bandwidth capacity has increased and wasted making the Internet connection is slow. Detecting worms and trojan on the Internet network, especially new variants of worms and trojans and worms and trojans hidden is still a challenging problem. Worm and trojan attacks generally occur in computer networks or the Internet which has a low level of security and vulnerable to infection. The detection and analysis of the worm and trojan attacks in the Internet network can be done by looking at the anomalies in Internet traffic and internet protocol addresses are accessed.<br />This research used experimental research applying C4.5 and Bayesian Network methods to accurately classify anomalies in network traffic internet. Analysis of classification is applied to an internet address, internet protocol and internet bandwidth that allegedly attacked and trojan worm attacks.<br />The results of this research is a result of analysis and classification of internet addresses, internet protocol and internet bandwidth to get the attack worms and trojans.</p>
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Yoshikawa, Masaya, Yusuke Mori, and Takeshi Kumaki. "Implementation Aware Hardware Trojan Trigger." Advanced Materials Research 933 (May 2014): 482–86. http://dx.doi.org/10.4028/www.scientific.net/amr.933.482.

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Recently, the threat of hardware Trojans has garnered attention. Hardware Trojans are malicious circuits that are incorporated into large-scale integrations (LSIs) during the manufacturing process. When predetermined conditions specified by an attacker are satisfied, the hardware Trojan is triggered and performs subversive activities without the LSI users even being aware of these activities. In previous studies, a hardware Trojan was incorporated into a cryptographic circuit to estimate confidential information. However, Trojan triggers have seldom been studied. The present study develops several new Trojan triggers and each of them is embedded in a field-programmable gate array (FPGA). Subsequently, the ease of detection of each trigger is verified from the standpoint of area.
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Amelian, Atieh, and Shahram Etemadi Borujeni. "A Side-Channel Analysis for Hardware Trojan Detection Based on Path Delay Measurement." Journal of Circuits, Systems and Computers 27, no. 09 (April 26, 2018): 1850138. http://dx.doi.org/10.1142/s0218126618501384.

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Hardware Trojan Horses (HTHs) are malicious modifications inserted in Integrated Circuit during fabrication steps. The HTHs are very small and can cause damages in circuit function. They cannot be detected by conventional testing methods. Due to dangerous effects of them, Hardware Trojan Detection has become a major concern in hardware security. In this paper, a new HTH detection method is presented based on side-channel analysis that uses path delay measurement. In this method, we find and observe the paths that Trojans have most effect on them. Most of the previous works add some structures to the circuit and need a large overhead cost. But, in our method, there is no modification in the circuit and we can use it for testing the circuits received after fabrication. The proposed method is evaluated with Xilinx FPGA over a number of test circuits. The results show that measuring the delays on 20 paths with an accuracy of 0.01[Formula: see text]ns can detect more than 80% of Trojans.
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Yin, Khin Swe, and May Aye Khine. "Optimal remote access trojans detection based on network behavior." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (June 1, 2019): 2177. http://dx.doi.org/10.11591/ijece.v9i3.pp2177-2184.

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<p>RAT is one of the most infected malware in the hyper-connected world. Data is being leaked or disclosed every day because new remote access Trojans are emerging and they are used to steal confidential data from target hosts. Network behavior-based detection has been used to provide an effective detection model for Remote Access Trojans. However, there is still short comings: to detect as early as possible, some False Negative Rate and accuracy that may vary depending on ratio of normal and malicious RAT sessions. As typical network contains large amount of normal traffic and small amount of malicious traffic, the detection model was built based on the different ratio of normal and malicious sessions in previous works. At that time false negative rate is less than 2%, and it varies depending on different ratio of normal and malicious instances. An unbalanced dataset will bias the prediction model towards the more common class. In this paper, each RAT is run many times in order to capture variant behavior of a Remote Access Trojan in the early stage, and balanced instances of normal applications and Remote Access Trojans are used for detection model. Our approach achieves 99 % accuracy and 0.3% False Negative Rate by Random Forest Algorithm.</p>
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Sheppard, Scott S., and Chadwick A. Trujillo. "Detection of a Trailing (L5) Neptune Trojan: Fig. 1." Science 329, no. 5997 (August 12, 2010): 1304. http://dx.doi.org/10.1126/science.1189666.

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The orbits of small Solar System bodies record the history of our Solar System. Here, we report the detection of 2008 LC18, which is a Neptune Trojan in the trailing (L5) Lagrangian region of gravitational equilibrium within Neptune’s orbit. We estimate that the leading and trailing Neptune Trojan regions have similarly sized populations and dynamics, with both regions dominated by high-inclination objects. Similar populations and dynamics at both Neptune Lagrangian regions indicate that the Trojans were likely captured by a migrating, eccentric Neptune in a dynamically excited planetesimal population.
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Reddy, Varun, and Nirmala Devi M. "FPGA Realization of Deep Neural Network for Hardware Trojan Detection." International Journal of Engineering & Technology 9, no. 3 (August 30, 2020): 764. http://dx.doi.org/10.14419/ijet.v9i3.30946.

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With the increase in outsourcing design and fabrication, malicious third-party vendors often insert hardware Trojan (HT) in the integrated Circuits(IC). It is difficult to identify these Trojans since the nature and characteristics of each Trojan differ significantly. Any method developed for HT detection is limited by its capacity on dealing with varied types of Trojans. The main purpose of this study is to show using deep learning (DL), this problem can be dealt with some extent and the effect of deep neural network (DNN) when it is realized on field programmable gate array (FPGA). In this paper, we propose a comparison of accuracy in finding faults on ISCAS’85 benchmark circuits between random forest classifier and DNN. Further for the faster processing time and less power consumption, the network is implemented on FPGA. The results show the performance of deep neural network gets better when a large number of nets are used and faster in the execution of the algorithm. Also, the speedup of the neuron is 100x times better when implemented on FPGA with 15.32% of resource utilization and provides less power consumption than GPU.
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Dissertations / Theses on the topic "Detection of Trojans"

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Raju, Akhilesh. "Trojan Detection in Hardware Designs." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504781162418081.

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Dharmadhikari, Pranav Hemant. "Hardware Trojan Detection in Sequential Logic Designs." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1543919236213844.

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Bhamidipati, Harini. "SINGLE TROJAN INJECTION MODEL GENERATION AND DETECTION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1253543191.

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Hoque, Tamzidul. "Ring Oscillator Based Hardware Trojan Detection." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1430413190.

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Banga, Mainak. "Partition based Approaches for the Isolation and Detection of Embedded Trojans in ICs." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/34924.

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This thesis aims towards devising a non-destructive testing methodology for ICs fabricated by a third party manufacturer to ensure the integrity of the chip. With the growing trend of outsourcing, the sanity of the final product has emerged to be a prime concern for the end user. This is especially so if the components are to be used in mission-critical applications such as space-exploration, medical diagnosis and treatment, defense equipments such as missiles etc., where a single failure can lead to a disaster. Thus, any extraneous parts (Trojans) that might have been implanted by the third party manufacturer with a malicious intent during the fabrication process must be diagnosed before the component is put to use.

The inherent stealthy nature of Trojans makes it difficult to detect them at normal IC outputs. More so, with the restriction that one cannot visually inspect the internals of an IC after it has been manufactured. This obviates the use of side-channel signal(s) that acts like a signature of the IC as a means to assess its internal behavior under operational conditions.

In this work, we have selected power as the side-channel signal to characterize the internal behavior of the ICs. We have used two circuit partitioning based approaches for isolating and enhancing the behavioral difference between parts of a genuine IC and one with a sequence detector Trojan in it. Experimental results reveal that these approaches are effective in exposing anomalous behavior between the targeted ICs. This is reflected as difference in power-profiles of the genuine and maligned ICs that is magnified above the process variation ensuring that the discrepancies are observable.
Master of Science

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Amsaad, Fathi Hassan Mohamed. "A Trusted and Efficient Security Approach for the Detection of Hardware Trojans and Authentication of FPGA-based Systems." University of Toledo / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1512494875469127.

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Weidler, Nathanael R. "Built-In Return-Oriented Programs in Embedded Systems and Deep Learning for Hardware Trojan Detection." DigitalCommons@USU, 2019. https://digitalcommons.usu.edu/etd/7620.

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Microcontrollers and integrated circuits in general have become ubiquitous in the world today. All aspects of our lives depend on them from driving to work, to calling our friends, to checking our bank account balance. People who would do harm to individuals, corporations and nation states are aware of this and for that reason they seek to find or create and exploit vulnerabilities in integrated circuits. This dissertation contains three papers dealing with these types of vulnerabilities. The first paper talks about a vulnerability that was found on a microcontroller, which is a type of integrated circuit. The final two papers deal with hardware trojans. Hardware trojans are purposely added to the design of an integrated circuit in secret so that the manufacturer doesn’t know about it. They are used to damage the integrated circuit, leak confidential information, or in other ways alter the circuit. Hardware trojans are a major concern for anyone using integrated circuits because an attacker can alter a circuit in almost any way if they are successful in inserting one. A known method to prevent hardware trojan insertion is discussed and a type of circuit for which this method does not work is revealed. The discussion of hardware trojans is concluded with a new way to detect them before the integrated circuit is manufactured. Modern deep learning models are used to detect the portions of the hardware trojan called triggers that activate them.
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Harris, Matthew Joshua. "Accelerating Reverse Engineering Image Processing Using FPGA." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright155535529307322.

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Bowman, David C. "Image Stitching and Matching Tool in the Automated Iterative Reverse Engineer (AIRE) Integrated Circuit Analysis Suite." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1533766175549951.

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Hill, Jeremy Michael Olivar. "Detection of Avionics Supply Chain Non-control-flow Malware Using Binary Decompilation and Wavelet Analysis." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1628159084278194.

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Books on the topic "Detection of Trojans"

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Elizabeth, Peters. Trojan Gold. New York: HarperCollins, 2006.

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Elizabeth, Peters. Trojan gold. New York: Avon Books, 2000.

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Peters, Elizabeth. Trojan gold. London: Paitkus, 1987.

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Johnston, Dorothy. The Trojan dog. New York: Thomas Dunne Books, 2005.

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Elizabeth, Peters. Trojan gold: A Vicky Bliss mystery. Thorndike, Me: Thorndike Press, 1987.

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Peters, Elizabeth. Trojan gold: A Vicky Bliss mystery. New York: Atheneum, 1987.

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Peters, Elizabeth. Trojan gold: A Vicky Bliss mystery. Thorndike, Me: Thorndike Press, 1987.

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Elizabeth, Peters, and Elizabeth Peters. Trojan gold: A Vicky Bliss mystery. New York: Atheneum, 1987.

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Salmani, Hassan, Mohammad Tehranipoor, and Xuehui Zhang. Integrated Circuit Authentication: Hardware Trojans and Counterfeit Detection. Springer, 2016.

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Innes, Hammond. Trojan Horse. Bloomsbury Publishing Plc, 2013.

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Book chapters on the topic "Detection of Trojans"

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Salmani, Hassan. "Design Techniques for Hardware Trojans Prevention and Detection at the Gate Level." In Trusted Digital Circuits, 49–67. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-79081-7_5.

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Salmani, Hassan. "Design Techniques for Hardware Trojans Prevention and Detection at the Layout Level." In Trusted Digital Circuits, 93–107. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-79081-7_7.

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Crandall, Jedidiah R., John Brevik, Shaozhi Ye, Gary Wassermann, Daniela A. S. de Oliveira, Zhendong Su, S. Felix Wu, and Frederic T. Chong. "Putting Trojans on the Horns of a Dilemma: Redundancy for Information Theft Detection." In Transactions on Computational Science IV, 244–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01004-0_14.

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Salmani, Hassan. "Design Techniques for Hardware Trojans Prevention and Detection at the Register-Transfer Level." In Trusted Digital Circuits, 31–38. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-79081-7_3.

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Tang, Yongkang, Jianye Wang, Shaoqing Li, Jihua Chen, and Binbin Yang. "Microsecond-Level Temperature Variation of Logic Circuits and Influences of Infrared Cameras’ Parameters on Hardware Trojans Detection." In Communications in Computer and Information Science, 69–80. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3159-5_7.

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Narasimhan, Seetharam, and Swarup Bhunia. "Hardware Trojan Detection." In Introduction to Hardware Security and Trust, 339–64. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8080-9_15.

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Davoodi, Azadeh. "Golden-Free Trojan Detection." In The Hardware Trojan War, 203–15. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68511-3_9.

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Bao, Chongxi, Yang Xie, Yuntao Liu, and Ankur Srivastava. "Reverse Engineering-Based Hardware Trojan Detection." In The Hardware Trojan War, 269–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68511-3_11.

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Govindan, Vidya, and Rajat Subhra Chakraborty. "Logic Testing for Hardware Trojan Detection." In The Hardware Trojan War, 149–82. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68511-3_7.

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Marchette, David J. "Trojan Programs and Covert Channels." In Computer Intrusion Detection and Network Monitoring, 241–55. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3458-4_7.

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Conference papers on the topic "Detection of Trojans"

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Vashistha, Nidish, Hangwei Lu, Qihang Shi, M. Tanjidur Rahman, Haoting Shen, Damon L. Woodard, Navid Asadizanjani, and Mark Tehranipoor. "Trojan Scanner: Detecting Hardware Trojans with Rapid SEM Imaging Combined with Image Processing and Machine Learning." In ISTFA 2018. ASM International, 2018. http://dx.doi.org/10.31399/asm.cp.istfa2018p0256.

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Abstract Hardware Trojans are malicious changes to the design of integrated circuits (ICs) at different stages of the design and fabrication processes. Different approaches have been developed to detect Trojans namely non-destructive (electrical tests like run-time monitoring, functional and structural tests) and destructive (full chip reverse engineering). However, these methods cannot detect all types of Trojans and they suffer from a number of disadvantages such as slow speed of detection and lack of confidence in detecting all types of Trojans. Majority of hardware Trojans implemented in an IC will leave a footprint at the doping (active) layer. In this paper, we introduce a new version of our previously developed “Trojan Scanner” [1] framework for the untrusted foundry threat model, where a trusted GDSII layout (golden layout) is available. Advanced computer vision algorithms in combination with the supervised machine-learning model are used to classify different features of the golden layout and SEM images from an IC under authentication, as a unique descriptor for each type of gates. These descriptors are compared with each other to detect any subtle changes on the active region, which can raise the flag for the existence of a potential hardware Trojan. The descriptors can differentiate variation due to fabrication process, defects, and common SEM image distortions to rule out the possibility of false detection. Our results demonstrate that Trojan Scanner is more reliable than electrical testing and faster than full chip reverse engineering. Trojan Scanner does not rely on the functionality of the circuit rather focuses on the real physical structure to detect malicious changes inserted by the untrusted foundry.
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Zhou, Xinzhe, Wenhao Jiang, Sheng Qi, and Yadong Mu. "Multi-Target Invisibly Trojaned Networks for Visual Recognition and Detection." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/477.

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Visual backdoor attack is a recently-emerging task which aims to implant trojans in a deep neural model. A trojaned model responds to a trojan-invoking trigger in a fully predictable manner while functioning normally otherwise. As a key motivating fact to this work, most triggers adopted in existing methods, such as a learned patterned block that overlays a benigh image, can be easily noticed by human. In this work, we take image recognition and detection as the demonstration tasks, building trojaned networks that are significantly less human-perceptible and can simultaneously attack multiple targets in an image. The main technical contributions are two-folds: first, under a relaxed attack mode, we formulate trigger embedding as an image steganography-and-steganalysis problem that conceals a secret image in another image in a decipherable and almost invisible way. In specific, a variable number of different triggers can be encoded into a same secret image and fed to an encoder module that does steganography. Secondly, we propose a generic split-and-merge scheme for training a trojaned model. Neurons are split into two sets, trained either for normal image recognition / detection or trojaning the model. To merge them, we novelly propose to hide trojan neurons within the nullspace of the normal ones, such that the two sets do not interfere with each other and the resultant model exhibits similar parameter statistics to a clean model. Comprehensive experiments are conducted on the datasets PASCAL VOC and Microsoft COCO (for detection) and a subset of ImageNet (for recognition). All results clearly demonstrate the effectiveness of our proposed visual trojan method.
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Meade, Travis, Shaojie Zhang, Yier Jin, Zheng Zhao, and David Pan. "Gate-Level Netlist Reverse Engineering Tool Set for Functionality Recovery and Malicious Logic Detection." In ISTFA 2016. ASM International, 2016. http://dx.doi.org/10.31399/asm.cp.istfa2016p0342.

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Abstract Reliance on third-party resources, including thirdparty IP cores and fabrication foundries, as well as wide usage of commercial-off-the-shelf (COTS) components has raised concerns that backdoors and/or hardware Trojans may be inserted into fabricated chips. Defending against hardware backdoors and/or Trojans has primarily focused on detection at various stages in the supply chain. Netlist reverse engineering tools have been investigated as an alternative to existing chip-level reverse engineering methods which can help recover functional netlists from fabricated chips, but fall short of detecting malicious logic or recovering high-level functionality. In this work, we develop a netlist reverse engineering tool-set which recovers high-level functionality from the netlist, thereby aiding malicious logic detection. The tool-set performs state register identification, control logic recovery and datapath tracking, which facilitates validation of encrypted/obfuscated hardware IP cores. Relying on 3-SAT algorithms and topology-based computational methods, we demonstrate that the developed tool-set can handle netlists of various complexities.
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Baktir, Selcuk, Tansal Gucluoglu, Atilla Ozmen, Huseyin Fuat Alsan, and Mustafa Can Macit. "Detection of Trojans in integrated circuits." In 2012 International Symposium on Innovations in Intelligent Systems and Applications (INISTA). IEEE, 2012. http://dx.doi.org/10.1109/inista.2012.6246941.

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Kumar, Nitesh, Vinay Kumar, and Manish Gaur. "Banking Trojans APK Detection using Formal Methods." In 2019 4th International Conference on Information Systems and Computer Networks (ISCON). IEEE, 2019. http://dx.doi.org/10.1109/iscon47742.2019.9036319.

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Rahimifar, Mohammad Mehdi, and Hadi Jahanirad. "Employing Image Processing Techniques for Hardware Trojans Detection." In 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE). IEEE, 2020. http://dx.doi.org/10.1109/iccke50421.2020.9303654.

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Karabacak, Fatih, Richard Welker, Matthew J. Casto, Jennifer N. Kitchen, and Sule Ozev. "RF circuit authentication for detection of process Trojans." In 2018 IEEE 36th VLSI Test Symposium (VTS). IEEE, 2018. http://dx.doi.org/10.1109/vts.2018.8368666.

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McGuire, Matthew, Umit Ogras, and Sule Ozev. "PCB Hardware Trojans: Attack Modes and Detection Strategies." In 2019 IEEE 37th VLSI Test Symposium (VTS). IEEE, 2019. http://dx.doi.org/10.1109/vts.2019.8758643.

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Soll, Oliver, Thomas Korak, Michael Muehlberghuber, and Michael Hutter. "EM-based detection of hardware trojans on FPGAs." In 2014 IEEE International Symposium on Hardware-Oriented Security and Trust (HOST). IEEE, 2014. http://dx.doi.org/10.1109/hst.2014.6855574.

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Zhao, Zhixun, Lin Ni, Shaoqing Li, and Yubo Shi. "A Feature Extraction Method for Hardware Trojans Detection." In 2015 International Conference on Automation, Mechanical Control and Computational Engineering. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/amcce-15.2015.307.

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