Journal articles: '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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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. 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. 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.

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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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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.

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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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Zhang, Kai, and Hang Yu. "Study on the Method of Android System Cloud Monitoring Information Based on SVM." Applied Mechanics and Materials 602-605 (August 2014): 2272–75. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2272.

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In recent years, android system has become a mobile device is especially preferred intelligent mobile phone operating system, like Microsoft window system, Android operating system is also subject to a variety of mobile phone viruses, Trojans and other malicious software attacks, resulting in the leakage of personal information, the harm of spam messages continuously, in order to solve these problems in this paper for the known virus Trojan cloud monitoring and detecting light client burden increase sample detection efficiency; using SVM to unknown trojan virus (support vector machine) short sequences of API function calls the LINUX classification, analysis of the API function of each call with precise improve classification support vector machine the danger coefficient after integration, to distinguish the Trojan virus, in order to achieve information security protection system role.

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Lu, Jiazhong, Xiaolei Liu, Shibin Zhang, and Yan Chang. "Research and Analysis of Electromagnetic Trojan Detection Based on Deep Learning." Security and Communication Networks 2020 (November 25, 2020): 1–13. http://dx.doi.org/10.1155/2020/6641844.

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The electromagnetic Trojan attack can break through the physical isolation to attack, and the leaked channel does not use the system network resources, which makes the traditional firewall and other intrusion detection devices unable to effectively prevent. Based on the existing research results, this paper proposes an electromagnetic Trojan detection method based on deep learning, which makes the work of electromagnetic Trojan analysis more intelligent. First, the electromagnetic wave signal is captured using software-defined radio technology, and then the signal is initially filtered in combination with a white list, a demodulated signal, and a rate of change in intensity. Secondly, the signal in the frequency domain is divided into blocks in a time-window mode, and the electromagnetic signals are represented by features such as time, information amount, and energy. Finally, the serialized signal feature vector is further extracted using the LSTM algorithm to identify the electromagnetic Trojan. This experiment uses the electromagnetic Trojan data published by Gurion University to test. And it can effectively defend electromagnetic Trojans, improve the participation of computers in electromagnetic Trojan detection, and reduce the cost of manual testing.

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Zou, Minhui, Xiaotong Cui, Liang Shi, and Kaijie Wu. "Potential Trigger Detection for Hardware Trojans." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 37, no. 7 (July 2018): 1384–95. http://dx.doi.org/10.1109/tcad.2017.2753201.

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Melosik, M., P. Sniatala, and W. Marszalek. "Hardware Trojans detection in chaos-based cryptography." Bulletin of the Polish Academy of Sciences Technical Sciences 65, no. 5 (October 1, 2017): 725–32. http://dx.doi.org/10.1515/bpasts-2017-0078.

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Abstract The paper deals with the security problems in chaotic-based cryptography. In particular, the 0–1 test for chaos is used to detect hardware Trojans in electronic circuits – generators of chaotic bit sequences. The proposed method of detecting hardware Trojans is based on analyzing the original bit sequences through the 0–1 test yielding a simple result, either a number close to 1, when the examined bit sequence is chaotic, or a number close to 0, when the sequence is non-chaotic. A complementary result is a graph of translation variables qc and pc which form a basis of the 0–1 test. The method does not require any extra corrections and can be applied to relatively short sequences of bits. This makes the method quite attractive as the security problems are dealt with at the chaotic generator level, with no need to apply any extractors of randomness. The method is illustrated by numerical examples of simulated Trojans in chaotic bit generators based on the analog Lindberg circuit as well as a discrete system based on the logistic map.

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Lillo-Box, J., A. Leleu, H. Parviainen, P. Figueira, M. Mallonn, A. C. M. Correia, N. C. Santos, et al. "The TROY project." Astronomy & Astrophysics 618 (October 2018): A42. http://dx.doi.org/10.1051/0004-6361/201833312.

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Context.Co-orbital bodies are the byproduct of planet formation and evolution, as we know from the solar system. Although planet-size co-orbitals do not exists in our planetary system, dynamical studies show that they can remain stable for long periods of time in the gravitational well of massive planets. Should they exist, their detection is feasible with the current instrumentation.Aims.In this paper, we present new ground-based observations searching for these bodies co-orbiting with nine close-in (P< 5 days) planets, using various observing techniques. The combination of all of these techniques allows us to restrict the parameter space of any possible trojan in the system.Methods.We used multi-technique observations, comprised of radial velocity, precision photometry, and transit timing variations, both newly acquired in the context of the TROY project and publicly available, to constrain the presence of planet-size trojans in the Lagrangian points of nine known exoplanets.Results.We find no clear evidence of trojans in these nine systems through any of the techniques used down to the precision of the observations. However, this allows us to constrain the presence of any potential trojan in the system, especially in the trojan mass or radius vs. libration amplitude plane. In particular, we can set upper mass limits in the super-Earth mass regime for six of the studied systems.

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Chevychelov, A. V., A. V. Burmistrov, and K. Yu Voyshhev. "Detecting malicious software using machine learning." Issues of radio electronics, no. 11 (November 20, 2019): 42–45. http://dx.doi.org/10.21778/2218-5453-2019-11-42-45.

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Today, most malware detection tools (Trojans): trojans, spyware, adware, worms, viruses, and ransomware are based on a signature approach that is ineffective for detecting polymorphs and malware whose signatures have not been recorded in antivirus database. This article explores methods for detecting opcodes in malware using machine learning algorithms. The study is carried on a Microsoft dataset containing 21653 examples of malicious code. The 20 most informative parameters based on the Fisher criterion are distinguished, methods for selecting parameters and various classifiers (logistic decision tree, random forest, naive Bayesian classifier, random tree) are compared, as a result of which an accuracy close to 100% is achieved.

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吴, 少华. "Study of Trojans Detection and Prevention Technology." Computer Science and Application 05, no. 12 (2015): 429–35. http://dx.doi.org/10.12677/csa.2015.512054.

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Dong, Chen, Yulin Liu, Jinghui Chen, Ximeng Liu, Wenzhong Guo, and Yuzhong Chen. "An Unsupervised Detection Approach for Hardware Trojans." IEEE Access 8 (2020): 158169–83. http://dx.doi.org/10.1109/access.2020.3001239.

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Ali, Liakot, and Farshad. "Analog hardware trojan design and detection in OFDM based wireless cryptographic ICs." PLOS ONE 16, no. 7 (July 29, 2021): e0254903. http://dx.doi.org/10.1371/journal.pone.0254903.

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Due to Hardware Trojan (HT), trustworthiness of Integrated Circuit (IC) supply chain is a burning issue in Semiconductor Industry nowadays. Over the last decade, extensive research has been carried on HT detection methods for digital circuits. However, the HT issue remains largely unexplored in the domain of Analog Mixed Signal (AMS)/ RF circuit where it is now an appealing target for the attackers. The increasing popularity of Orthogonal Frequency Division Multiplexing (OFDM) based wireless cryptographic ICs in modern communication systems makes it a lucrative target for HT-based attacks which could have a devastating impact on data security. This paper presents a trigger-based Hardware Trojan Threat model that exploits the extended cyclic prefix (ECP) property of the OFDM communication scheme to leak the secret encryption key over low noise Additive White Gaussian Channel (AWGN) and developed a Cyclic Prefix (CP) checker based detection mechanism named “SENTRY” to detect such trojans once it is triggered.

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Lillo-Box, J., D. Barrado, P. Figueira, A. Leleu, N. C. Santos, A. C. M. Correia, P. Robutel, and J. P. Faria. "The TROY project: Searching for co-orbital bodies to known planets." Astronomy & Astrophysics 609 (January 2018): A96. http://dx.doi.org/10.1051/0004-6361/201730652.

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Context. The detection of Earth-like planets, exocomets or Kuiper belts show that the different components found in the solar system should also be present in other planetary systems. Trojans are one of these components and can be considered fossils of the first stages in the life of planetary systems. Their detection in extrasolar systems would open a new scientific window to investigate formation and migration processes. Aims. In this context, the main goal of the TROY project is to detect exotrojans for the first time and to measure their occurrence rate (η-Trojan). In this first paper, we describe the goals and methodology of the project. Additionally, we used archival radial velocity data of 46 planetary systems to place upper limits on the mass of possible trojans and investigate the presence of co-orbital planets down to several tens of Earth masses. Methods. We used archival radial velocity data of 46 close-in (P < 5 days) transiting planets (without detected companions) with information from high-precision radial velocity instruments. We took advantage of the time of mid-transit and secondary eclipses (when available) to constrain the possible presence of additional objects co-orbiting the star along with the planet. This, together with a good phase coverage, breaks the degeneracy between a trojan planet signature and signals coming from additional planets or underestimated eccentricity. Results. We identify nine systems for which the archival data provide >1σ evidence for a mass imbalance between L4 and L5. Two of these systems provide >2σ detection, but no significant detection is found among our sample. We also report upper limits to the masses at L4/L5 in all studied systems and discuss the results in the context of previous findings.

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Kuznetsov, E., and A. Saurov. "Hardware Trojans. Part3: methods for prevention and detection." Nanoindustry Russia, no. 1 (2017): 30–40. http://dx.doi.org/10.22184/1993-8578.2017.71.1.30.40.

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Cho, Mingi, Jaedong Jang, Yezee Seo, Seyeon Jeong, Soochang Chung, and Taekyoung Kwon. "Towards bidirectional LUT-level detection of hardware Trojans." Computers & Security 104 (May 2021): 102223. http://dx.doi.org/10.1016/j.cose.2021.102223.

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Graf, Jonathan, Whitney Batchelor, Scott Harper, Ryan Marlow, Edward Carlisle, and Peter Athanas. "A practical application of game theory to optimize selection of hardware Trojan detection strategies." Journal of Hardware and Systems Security 4, no. 2 (December 28, 2019): 98–119. http://dx.doi.org/10.1007/s41635-019-00089-3.

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AbstractA wide variety of Hardware Trojan countermeasures have been developed, but less work has been done to determine which are optimal for any given design. To address this, we consider not only metrics related to the performance of the countermeasure, but also the likely action of an adversary given their goals. Trojans are inserted by an adversary to accomplish an end, so these goals must be considered and quantified in order to predict these actions. The model presented here builds upon a security economic approach that models the adversary and defender motives and goals in the context of empirically derived countermeasure efficacy metrics. The approach supports formation of a two-player strategic game to determine optimal strategy selection for both adversary and defender. A game may be played in a variety of contexts, including consideration of the entire design lifecycle or only a step in product development. As a demonstration of the practicality of this approach, we present an experiment that derives efficacy metrics from a set of countermeasures (defender strategies) when tested against a taxonomy of Trojans (adversary strategies). We further present a software framework, GameRunner, that automates not only the solution to the game but also mathematical and graphical exploration of “what if” scenarios in the context of the game. GameRunner can also issue “prescriptions,” a set of commands that allows the defender to automate the application of the optimal defender strategy to their circuit of concern. Finally, we include a discussion of ongoing work to include additional software tools, a more advanced experimental framework, and the application of irrationality models to account for players who make subrational decisions.

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Feng, Wen Feng, Lei Li, and Zhen Li. "A Method of Detecting Hardware Trojans Based on Side-Channel Analysis." Applied Mechanics and Materials 536-537 (April 2014): 558–61. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.558.

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In recent years, integrated circuits subject to hardware Trojans attack in the design and manufacturing process, the security of chip and hardware security was threatened. Some detection methods of have been proposed, the most common of those methods is based on side-channel signal analysis, however, since the effect of process noise, considering only the unilateral information that is difficult to effectively distinguish the noise and Trojans circuit. In this paper, the method still based on side-channel signal, but it is a combination of power and delay which was called the power-delay product (PDP). The idea proposed is verified by the benchmark circuit iscas85, the experimental results show that this method can effectively improve detection probability.

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Wang, Jian, and Ying Li. "RDAMS: An Efficient Run-Time Approach for Memory Fault and Hardware Trojans Detection." Information 12, no. 4 (April 14, 2021): 169. http://dx.doi.org/10.3390/info12040169.

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Ensuring the security of IoT devices and chips at runtime has become an urgent task as they have been widely used in human life. Embedded memories are vital components of SoC (System on Chip) in these devices. If they are attacked or incur faults at runtime, it will bring huge losses. In this paper, we propose a run-time detection architecture for memory security (RDAMS) to detect memory threats (fault and Hardware Trojans attack). The architecture consists of a Security Detection Core (SDC) that controls and enforces the detection procedure as a “security brain”, and a memory wrapper (MEM_wrapper) which interacts with memory to assist the detection. We also design a low latency response mechanism to solve the SoC performance degradation caused by run-time detection. A block-based multi-granularity detection approach is proposed to render the design flexible and reduce the cost in implementation using the FPGA’s dynamic partial reconfigurable (DPR) technology, which enables online detection mode reconfiguration according to the requirements. Experimental results show that RDAMS can correctly detect and identify 10 modeled memory faults and two types of Hardware Trojans (HTs) attacks without leading a great performance degradation to the system.

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Dong, Chen, Yi Xu, Ximeng Liu, Fan Zhang, Guorong He, and Yuzhong Chen. "Hardware Trojans in Chips: A Survey for Detection and Prevention." Sensors 20, no. 18 (September 10, 2020): 5165. http://dx.doi.org/10.3390/s20185165.

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Diverse and wide-range applications of integrated circuits (ICs) and the development of Cyber Physical System (CPS), more and more third-party manufacturers are involved in the manufacturing of ICs. Unfortunately, like software, hardware can also be subjected to malicious attacks. Untrusted outsourced manufacturing tools and intellectual property (IP) cores may bring enormous risks from highly integrated. Attributed to this manufacturing model, the malicious circuits (known as Hardware Trojans, HTs) can be implanted during the most designing and manufacturing stages of the ICs, causing a change of functionality, leakage of information, even a denial of services (DoS), and so on. In this paper, a survey of HTs is presented, which shows the threatens of chips, and the state-of-the-art preventing and detecting techniques. Starting from the introduction of HT structures, the recent researches in the academic community about HTs is compiled and comprehensive classification of HTs is proposed. The state-of-the-art HT protection techniques with their advantages and disadvantages are further analyzed. Finally, the development trends in hardware security are highlighted.

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Haider, Syed Kamran, Chenglu Jin, Masab Ahmad, Devu Manikantan Shila, Omer Khan, and Marten van Dijk. "Advancing the State-of-the-Art in Hardware Trojans Detection." IEEE Transactions on Dependable and Secure Computing 16, no. 1 (January 1, 2019): 18–32. http://dx.doi.org/10.1109/tdsc.2017.2654352.

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Bao, Chongxi, Domenic Forte, and Ankur Srivastava. "Temperature Tracking: Toward Robust Run-Time Detection of Hardware Trojans." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 34, no. 10 (October 2015): 1577–85. http://dx.doi.org/10.1109/tcad.2015.2424929.

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Karabacak, Fatih, Umit Ogras, and Sule Ozev. "Malicious Activity Detection in Lightweight Wearable and IoT Devices Using Signal Stitching." Sensors 21, no. 10 (May 13, 2021): 3408. http://dx.doi.org/10.3390/s21103408.

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The integrated circuit (IC) manufacturing process involves many players, from chip/board design and fabrication to firmware design and installation. In today’s global supply chain, any of these steps are prone to interference from rogue players, creating a security risk. Therefore, manufactured devices need to be verified to perform only their intended operations since it is not economically feasible to control the supply chain and use only trusted facilities. This paper presents a detection technique for malicious activity that can stem from hardware or firmware Trojans. The proposed technique relies on (i) repetitious side-channel sample collection of the active device, (ii) time-domain stitching, and (iii) frequency domain analysis. Since finding a trusted sample is generally impractical, the proposed technique is based on self-referencing to remove the effects of environmental or device-to-device variation in the frequency domain. We first observe that the power spectrum of the Trojan activity is confined to a low-frequency band. Then, we exploit this fact to achieve self-referencing using signal detection theory. The proposed technique’s effectiveness is demonstrated through experiments on a wearable electronics prototype and system-on-chip (SoC) under a variety of practical scenarios. Experimental results show the proposed detection technique enables a high overall detection coverage for malicious activities of varying types with 0.8 s monitoring time overhead, which is negligible.

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Lysenko, Sergiy, and Oleg Savenko. "Software for Computer Systems Trojans Detection as a Safety-Case Tool." Information & Security: An International Journal 28 (2012): 121–32. http://dx.doi.org/10.11610/isij.2810.

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Chen, Zhe, Shize Guo, Jian Wang, Yubai Li, and Zhonghai Lu. "Toward FPGA Security in IoT: A New Detection Technique for Hardware Trojans." IEEE Internet of Things Journal 6, no. 4 (August 2019): 7061–68. http://dx.doi.org/10.1109/jiot.2019.2914079.

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Reece, Trey, and William H. Robinson. "Detection of Hardware Trojans in Third-Party Intellectual Property Using Untrusted Modules." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 35, no. 3 (March 2016): 357–66. http://dx.doi.org/10.1109/tcad.2015.2459038.

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Nguyen, Luong N., Chia-Lin Cheng, Milos Prvulovic, and Alenka Zajic. "Creating a Backscattering Side Channel to Enable Detection of Dormant Hardware Trojans." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 27, no. 7 (July 2019): 1561–74. http://dx.doi.org/10.1109/tvlsi.2019.2906547.

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Danesh, Wafi, Jaya Dofe, and Qiaoyan Yu. "Efficient Hardware Trojan Detection with Differential Cascade Voltage Switch Logic." VLSI Design 2014 (May 11, 2014): 1–11. http://dx.doi.org/10.1155/2014/652187.

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Offshore fabrication, assembling and packaging challenge chip security, as original chip designs may be tampered by malicious insertions, known as hardware Trojans (HTs). HT detection is imperative to guarantee the chip performance and safety. Existing HT detection methods have limited capability to detect small-scale HTs and are further challenged by the increased process variation. To increase HT detection sensitivity and reduce chip authorization time, we propose to exploit the inherent feature of differential cascade voltage switch logic (DCVSL) to detect HTs at runtime. In normal operation, a system implemented with DCVSL always produces complementary logic values in internal nets and final outputs. Noncomplementary values on inputs and internal nets in DCVSL systems potentially result in abnormal power behavior and even system failures. By examining special power characteristics of DCVSL systems upon HT insertion, we can detect HTs, even if the HT size is small. Simulation results show that the proposed method achieves up to 100% HT detection rate. The evaluation on ISCAS benchmark circuits shows that the proposed method obtains a HT detection rate in the range of 66% to 98%.

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Kanda, Guard, Seungyong Park, and Kwangki Ryoo. "Run-Time Hardware Trojans Detection Using On-Chip Bus for System-on-Chip Design." Journal of the Korea Institute of Information and Communication Engineering 20, no. 2 (February 29, 2016): 343–50. http://dx.doi.org/10.6109/jkiice.2016.20.2.343.

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Tang, Yongkang, and Jianye Wang. "Built-in self-monitor-based finite state machines Trojans detection and self-lock defence." Journal of Engineering 2016, no. 4 (April 1, 2016): 62–63. http://dx.doi.org/10.1049/joe.2016.0012.

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Zhou, Lei, Li-Yong Zhou, Rudolf Dvorak, and Jian Li. "Systematic survey of the dynamics of Uranus Trojans." Astronomy & Astrophysics 633 (January 2020): A153. http://dx.doi.org/10.1051/0004-6361/201936332.

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Context. The discovered Uranus Trojan (UT) 2011 QF99 and several candidate UTs have been reported to be in unstable orbits. This implies that the stability region around the triangular Lagrange points L4 and L5 of Uranus should be very limited. Aims. In this paper, we aim to locate the stability region for UTs and find out the dynamical mechanisms responsible for the structures in the phase space. The null detection of primordial UTs also needs to be explained. Methods. Using the spectral number as the stability indicator, we constructed the dynamical maps on the (a0, i0) plane. The proper frequencies of UTs were determined precisely with a frequency analysis method that allows us to depict the resonance web via a semi-analytical method. We simulated radial migration by introducing an artificial force acting on planets to mimic the capture of UTs. Results. We find two main stability regions: a low-inclination (0° −14°) and a high-inclination regime (32° −59°). There is also an instability strip in each of these regions at 9° and 51°, respectively. These strips are supposed to be related with g − 2g5 + g7 = 0 and ν8 secular resonances. All stability regions are in the tadpole regime and no stable horseshoe orbits exist for UTs. The lack of moderate-inclined UTs is caused by the ν5 and ν7 secular resonances, which could excite the eccentricity of orbits. The fine structures in the dynamical maps are shaped by high-degree secular resonances and secondary resonances. Surprisingly, the libration centre of UTs changes with the initial inclination, and we prove it is related to the quasi 1:2 mean motion resonance (MMR) between Uranus and Neptune. However, this quasi-resonance has an ignorable influence on the long-term stability of UTs in the current planetary configuration. About 36.3% and 0.4% of the pre-formed orbits survive fast and slow migrations with migrating timescales of 1 and 10 Myr, respectively, most of which are in high inclination. Since low-inclined UTs are more likely to survive the age of the solar system, they make up 77% of all such long-life orbits by the end of the migration, making a total fraction up to 4.06 × 10−3 and 9.07 × 10−5 of the original population for fast and slow migrations, respectively. The chaotic capture, just like depletion, results from secondary resonances when Uranus and Neptune cross their mutual MMRs. However, the captured orbits are too hot to survive until today. Conclusions. About 3.81% UTs are able to survive the age of the solar system, among which 95.5% are on low-inclined orbits with i0 < 7.5°. However, the depletion of planetary migration seems to prevent a large fraction of such orbits, especially for the slow migration model. Based on the widely adopted migration models, a swarm of UTs at the beginning of the smooth outward migration is expected and a fast migration is favoured if any primordial UTs are detected.

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Al-Marghilani, A. "Comprehensive Analysis of IoT Malware Evasion Techniques." Engineering, Technology & Applied Science Research 11, no. 4 (August 21, 2021): 7495–500. http://dx.doi.org/10.48084/etasr.4296.

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Malware detection in Internet of Things (IoT) devices is a great challenge, as these devices lack certain characteristics such as homogeneity and security. Malware is malicious software that affects a system as it can steal sensitive information, slow its speed, cause frequent hangs, and disrupt operations. The most common malware types are adware, computer viruses, spyware, trojans, worms, rootkits, key loggers, botnets, and ransomware. Malware detection is critical for a system's security. Many security researchers have studied the IoT malware detection domain. Many studies proposed the static or dynamic analysis on IoT malware detection. This paper presents a survey of IoT malware evasion techniques, reviewing and discussing various researches. Malware uses a few common evasion techniques such as user interaction, environmental awareness, stegosploit, domain and IP identification, code obfuscation, code encryption, timing, and code compression. A comparative analysis was conducted pointing various advantages and disadvantages. This study provides guidelines on IoT malware evasion techniques.

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Zhang, Xuehui, Andrew Ferraiuolo, and Mohammad Tehranipoor. "Detection of trojans using a combined ring oscillator network and off-chip transient power analysis." ACM Journal on Emerging Technologies in Computing Systems 9, no. 3 (September 2013): 1–20. http://dx.doi.org/10.1145/2491677.

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Vijayan, Arunkumar, Mehdi B. Tahoori, and Krishnendu Chakrabarty. "Runtime Identification of Hardware Trojans by Feature Analysis on Gate-Level Unstructured Data and Anomaly Detection." ACM Transactions on Design Automation of Electronic Systems 25, no. 4 (September 2, 2020): 1–23. http://dx.doi.org/10.1145/3391890.

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Marchand, Cédric, and Julien Francq. "Low‐level implementation and side‐channel detection of stealthy hardware trojans on field programmable gate arrays." IET Computers & Digital Techniques 8, no. 6 (November 2014): 246–55. http://dx.doi.org/10.1049/iet-cdt.2014.0034.

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Azeez, Nureni Ayofe, Oluwanifise Ebunoluwa Odufuwa, Sanjay Misra, Jonathan Oluranti, and Robertas Damaševičius. "Windows PE Malware Detection Using Ensemble Learning." Informatics 8, no. 1 (February 10, 2021): 10. http://dx.doi.org/10.3390/informatics8010010.

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In this Internet age, there are increasingly many threats to the security and safety of users daily. One of such threats is malicious software otherwise known as malware (ransomware, Trojans, viruses, etc.). The effect of this threat can lead to loss or malicious replacement of important information (such as bank account details, etc.). Malware creators have been able to bypass traditional methods of malware detection, which can be time-consuming and unreliable for unknown malware. This motivates the need for intelligent ways to detect malware, especially new malware which have not been evaluated or studied before. Machine learning provides an intelligent way to detect malware and comprises two stages: feature extraction and classification. This study suggests an ensemble learning-based method for malware detection. The base stage classification is done by a stacked ensemble of fully-connected and one-dimensional convolutional neural networks (CNNs), whereas the end-stage classification is done by a machine learning algorithm. For a meta-learner, we analyzed and compared 15 machine learning classifiers. For comparison, five machine learning algorithms were used: naïve Bayes, decision tree, random forest, gradient boosting, and AdaBoosting. The results of experiments made on the Windows Portable Executable (PE) malware dataset are presented. The best results were obtained by an ensemble of seven neural networks and the ExtraTrees classifier as a final-stage classifier.

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Lamech, Charles, Reza M. Rad, Mohammad Tehranipoor, and Jim Plusquellic. "An Experimental Analysis of Power and Delay Signal-to-Noise Requirements for Detecting Trojans and Methods for Achieving the Required Detection Sensitivities." IEEE Transactions on Information Forensics and Security 6, no. 3 (September 2011): 1170–79. http://dx.doi.org/10.1109/tifs.2011.2136339.

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Khan, Riaz Ullah, Xiaosong Zhang, Rajesh Kumar, Abubakar Sharif, Noorbakhsh Amiri Golilarz, and Mamoun Alazab. "An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers." Applied Sciences 9, no. 11 (June 11, 2019): 2375. http://dx.doi.org/10.3390/app9112375.

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In recent years, the botnets have been the most common threats to network security since it exploits multiple malicious codes like a worm, Trojans, Rootkit, etc. The botnets have been used to carry phishing links, to perform attacks and provide malicious services on the internet. It is challenging to identify Peer-to-peer (P2P) botnets as compared to Internet Relay Chat (IRC), Hypertext Transfer Protocol (HTTP) and other types of botnets because P2P traffic has typical features of the centralization and distribution. To resolve the issues of P2P botnet identification, we propose an effective multi-layer traffic classification method by applying machine learning classifiers on features of network traffic. Our work presents a framework based on decision trees which effectively detects P2P botnets. A decision tree algorithm is applied for feature selection to extract the most relevant features and ignore the irrelevant features. At the first layer, we filter non-P2P packets to reduce the amount of network traffic through well-known ports, Domain Name System (DNS). query, and flow counting. The second layer further characterized the captured network traffic into non-P2P and P2P. At the third layer of our model, we reduced the features which may marginally affect the classification. At the final layer, we successfully detected P2P botnets using decision tree Classifier by extracting network communication features. Furthermore, our experimental evaluations show the significance of the proposed method in P2P botnets detection and demonstrate an average accuracy of 98.7%.

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Zhou, Jun, Mengquan Li, Pengxing Guo, and Weichen Liu. "Attack Mitigation of Hardware Trojans for Thermal Sensing via Micro-ring Resonator in Optical NoCs." ACM Journal on Emerging Technologies in Computing Systems 17, no. 3 (June 25, 2021): 1–23. http://dx.doi.org/10.1145/3433676.

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As an emerging role in new-generation on-chip communication, optical networks-on-chip (ONoCs) provide ultra-high bandwidth, low latency, and low power dissipation for data transfers. However, the thermo-optic effects of the photonic devices have a great impact on the operating performance and reliability of ONoCs, where the thermal-aware control with accurate measurements, e.g., thermal sensing, is typically applied to alleviate it. Besides, the temperature-sensitive ONoCs are prone to be attacked by the hardware Trojans (HTs) covertly embedded in the counterfeit integrated circuits (ICs) from the malicious third-party vendors, leading to performance degradation, denial-of-service (DoS), or even permanent damages. In this article, we focus on the tampering and snooping attacks during the thermal sensing via micro-ring resonator (MR) in ONoCs. Based on the provided workflow and attack model, a new structure of the anti-HT module is proposed to verify and protect the obtained data from the thermal sensor for attacks in its optical sampling and electronic transmission processes. In addition, we present the detection scheme based on the spiking neural networks (SNNs) to implement an accurate classification of the network security statuses for further high-level control. Evaluation results indicate that, with less than 1% extra area of a tile, our approach can significantly enhance the hardware security of thermal sensing for ONoC with trivial costs of up to 8.73%, 5.32%, and 6.14% in average latency, execution time, and energy consumption, respectively.

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Yusof, Muhammad, Madihah Mohd Saudi, and Farida Ridzuan. "Mobile Botnet Classification by using Hybrid Analysis." International Journal of Engineering & Technology 7, no. 4.15 (October 7, 2018): 103. http://dx.doi.org/10.14419/ijet.v7i4.15.21429.

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The popularity and adoption of Android smartphones has attracted malware authors to spread the malware to smartphone users. The malware on smartphone comes in various forms such as Trojans, viruses, worms and mobile botnet. However, mobile botnet or Android botnet are more dangerous since they pose serious threats by stealing user credential information, distributing spam and sending distributed denial of service (DDoS) attacks. Mobile botnet is defined as a collection of compromised mobile smartphones and controlled by a botmaster through a command and control (C&C) channel to serve a malicious purpose. Current research is still lacking in terms of their low detection rate due to their selected features. It is expected that a hybrid analysis could improve the detection rate. Therefore, machine learning methods and hybrid analysis which combines static and dynamic analyses were used to analyse and classify system calls, permission and API calls. The objective of this paper is to leverage machine learning techniques to classify the Android applications (apps) as botnet or benign. The experiment used malware dataset from the Drebin for the training and mobile applications from Google Play Store for testing. The results showed that Random Forest Algorithm achieved the highest accuracy rate of 97.9%. In future, more significant approach by using different feature selection such as intent, string and system component will be further explored for a better detection and accuracy rate.

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Mehbodniya, Abolfazl, Izhar Alam, Sagar Pande, Rahul Neware, Kantilal Pitambar Rane, Mohammad Shabaz, and Mangena Venu Madhavan. "Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques." Security and Communication Networks 2021 (September 9, 2021): 1–8. http://dx.doi.org/10.1155/2021/9293877.

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Healthcare sector is one of the prominent sectors in which a lot of data can be collected not only in terms of health but also in terms of finances. Major frauds happen in the healthcare sector due to the utilization of credit cards as the continuous enhancement of electronic payments, and credit card fraud monitoring has been a challenge in terms of financial condition to the different service providers. Hence, continuous enhancement is necessary for the system for detecting frauds. Various fraud scenarios happen continuously, which has a massive impact on financial losses. Many technologies such as phishing or virus-like Trojans are mostly used to collect sensitive information about credit cards and their owner details. Therefore, efficient technology should be there for identifying the different types of fraudulent conduct in credit cards. In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network are skewed for training the other standard and abnormal features of transactions for detecting the frauds in credit cards. For evaluating the accuracy of the model, publicly available data are used. The different algorithm results visualized the accuracy as 96.1%, 94.8%, 95.89%, 97.58%, and 92.3%, corresponding to various methodologies such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network, respectively. The comparative analysis visualized that the KNN algorithm generates better results than other approaches.

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Priya, Aayushi, Kajol Singh, and Rajeev Tiwari. "A Review on Malware Analysis by using an Approach of Machine Learning Techniques." IJOSTHE 3, no. 5 (January 4, 2019): 5. http://dx.doi.org/10.24113/ojssports.v3i5.86.

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In the Internet age, malware (such as viruses, trojans, ransomware, and bots) has posed serious andevolving security threats to Internet users. To protect legitimate users from these threats, anti-malware softwareproducts from different companies, including Comodo, Kaspersky, Kingsoft, and Symantec, provide the majordefense against malware. Unfortunately, driven by the economic benefits, the number of new malware sampleshas explosively increased: anti-malware vendors are now confronted with millions of potential malware samplesper year. In order to keep on combating the increase in malware samples, there is an urgent need to developintelligent methods for effective and efficient malware detection from the real and large daily sample collection.One of the most common approaches in literature is using machine learning techniques, to automatically learnmodels and patterns behind such complexity, and to develop technologies to keep pace with malware evolution.This survey aims at providing an overview on the way machine learning has been used so far in the context ofmalware analysis in Windows environments. This paper gives an survey on the features related to malware filesor documents and what machine learning techniques they employ (i.e., what algorithm is used to process the inputand produce the output). Different issues and challenges are also discussed.

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Moriam, Sadia, Elke Franz, Paul Walther, Akash Kumar, Thorsten Strufe, and Gerhard Fettweis. "Efficient Communication Protection of Many-Core Systems against Active Attackers." Electronics 10, no. 3 (January 21, 2021): 238. http://dx.doi.org/10.3390/electronics10030238.

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Many-core system-on-chips, together with their established communication infrastructures, Networks-on-Chip (NoC), are growing in complexity, which encourages the integration of third-party components to simplify and accelerate production processes. However, this also adversely exposes the surface for attacks through the injection of hardware Trojans. This work addresses active attacks on NoCs and focuses on the integrity and availability of transmitted data. In particular, we consider the modification and/or dropping of data during transmission as active attacks that might be performed by malicious routers. To mitigate the impact of such active attacks, we propose two lightweight solutions that respect the performance constraints of NoCs. Assuming the presence of symmetric keys, these approaches combine lightweight authentication codes for integrity protection with network coding for increased efficiency and robustness. The proposed solutions prevent undetected modifications and significantly increase availability through a reliable detection of attacks. The efficiency of these solutions is investigated in different scenarios using cycle-accurate simulations and the area overhead is analyzed relative to state-of-the-art many-core system. The results demonstrate that one authentication scheme with network coding protects the integrity of data to a low residual error of 1.36% at 0.2 attack probability with an area overhead of 2.68%. For faster and more flexible evaluation, an analytical approach is developed which is validated against the cycle-accurate simulations. The analytical approach is more than 1000× faster while having a maximum estimation error of 5%. Moreover, the analytical model provides a deeper insight into the system’s behavior. For example, it reveals which factors influence the performance parameters.

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Wang, Ning. "Trojan Detection Simulation Group under the Cloud Computing Environment." Applied Mechanics and Materials 602-605 (August 2014): 1996–99. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1996.

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The accurate method for Trojan group detection under cloud computing environment is studied in this paper. For the problem of Trojan group detection under the cloud computing environment, this paper proposed a Trojan group detection method based on the BP neural network. BP neural network model is constructed and the problem of Trojan group detection is acted on in this model. Experimental results show that the use of this algorithm for Trojan group detection can get the accurate detection results. Thus, it can open the network protection mode to ensure the user's network security and prevent the information loss caused by that the network is intruded.

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Journal articles on the topic 'Detection of Trojans'

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