Biblio

Browsers are one of the most widely used types of software around the world. This prevalence makes browsers a prime target for cyberattacks. To mitigate these threats, users can practice safe browsing habits and take advantage of the security features available to browsers. These protections, however, could be severely crippled if the browser itself were malicious. Presented in this paper is the concept of the evil-twin browser (ETB), a clone of a legitimate browser that looks and behaves identically to the original browser, but discreetly performs other tasks that harm a user's security. To better understand the concept of the evil-twin browser, a prototype ETB named ChroNe was developed. The creation and installation process of ChroN e is discussed in this paper. This paper also explores the motivation behind creating such a browser, examines existing relevant work, inspects the open-source codebase Chromium that assisted in ChroNe's development, and discusses relevant topics like ways to deliver an ETB, the capabilities of an ETB, and possible ways to defend against ETBs.

Machine learning and Artificial Intelligent techniques are the most used techniques. It gives opportunity to online sharing market where sharing and adopting model is being popular. It gives attackers many new opportunities. Deep neural network is the most used approached for artificial techniques. In this paper we are presenting a Proof of Concept method to detect Trojan attacks on the Deep Neural Network. Deploying trojan models can be dangerous in normal human lives (Application like Automated vehicle). First inverse the neuron network to create general trojan triggers, and then retrain the model with external datasets to inject Trojan trigger to the model. The malicious behaviors are only activated with the trojan trigger Input. In attack, original datasets are not required to train the model. In practice, usually datasets are not shared due to privacy or copyright concerns. We use five different applications to demonstrate the attack, and perform an analysis on the factors that affect the attack. The behavior of a trojan modification can be triggered without affecting the test accuracy for normal input datasets. After generating the trojan trigger and performing an attack. It's applying SHAP as defense against such attacks. SHAP is known for its unique explanation for model predictions.

As an emerging role in on-chip communication, the optical networks-on-chip (ONoCs) can provide ultra-high bandwidth, low latency and low power dissipation for the data transfer. 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 is used to alleviate it. Furthermore, the temperature-sensitive ONoCs are prone to be attacked by the hardware Trojans (HTs) covertly embedded in the integrated circuits (ICs) from the malicious third-party components, leading to performance degradation, denial of service (DoS), or even permanent damages. In this paper, we focus on the tampering attacks on optical sampling during the thermal sensing process in ONoCs. Corresponding approaches are proposed to mitigate the negative impacts from HT attacks. Evaluation results indicate that our approach can significantly enhance the hardware security of thermal sensing for ONoC with trivial overheads of up to 3.06% and 2.6% in average latency and energy consumption, respectively.

Digital microfluidic biochips (DMFBs) have become popular in the healthcare industry recently because of its lowcost, high-throughput, and portability. Users can execute the experiments on biochips with high resolution, and the biochips market therefore grows significantly. However, malicious attackers exploit Intellectual Property (IP) piracy and Trojan attacks to gain illegal profits. The conventional approaches present defense mechanisms that target either IP piracy or Trojan attacks. In practical, DMFBs may suffer from the threat of being attacked by these two attacks at the same time. This paper presents a comprehensive security system to protect DMFBs from IP piracy and Trojan attacks. We propose an authentication mechanism to protect IP and detect errors caused by Trojans with CCD cameras. By our security system, we could generate secret keys for authentication and determine whether the bioassay is under the IP piracy and Trojan attacks. Experimental results demonstrate the efficacy of our security system without overhead of the bioassay completion time.

The privacy of people on internet is getting reduced day by day. Data records of many prestigious organizations are getting corrupted due to computer malwares. Computer viruses are becoming more advanced. Hackers are able penetrate into a network and able to manipulate data. In this paper, describes the types of malwares like Trojans, boot sector virus, polymorphic virus, etc., and some of the hacking techniques which include DOS attack, DDoS attack, brute forcing, man in the middle attack, social engineering, information gathering tools, spoofing, sniffing. Counter measures for cyber attacks include VPN, proxy, tor (browser), firewall, antivirus etc., to understand the need of cyber security.

Jeopardy to cybersecurity threats in electronic systems is persistent and growing. Such threats present in hardware, by means such as Trojans and counterfeits, and in software, by means such as viruses and other malware. Against such threats, we propose a range of embedded instruments that are capable of real-time hardware assurance and online monitoring.

Hardware Trojans, implantable at a myriad of points within the supply chain, are difficult to detect and identify. By emulating systems on programmable hardware, the authors have created a tool from which to create and evaluate Trojan attack signatures and therefore enable better Trojan detection (for in-service systems) and prevention (for in-design systems).

Hackers create different types of Malware such as Trojans which they use to steal user-confidential information (e.g. credit card details) with a few simple commands, recent malware however has been created intelligently and in an uncontrolled size, which puts malware analysis as one of the top important subjects of information security. This paper proposes an efficient dynamic malware-detection method based on API similarity. This proposed method outperform the traditional signature-based detection method. The experiment evaluated 197 malware samples and the proposed method showed promising results of correctly identified malware.

A digital microfluidic biochip (DMFB) is an emerging technology that enables miniaturized analysis systems for point-of-care clinical diagnostics, DNA sequencing, and environmental monitoring. A DMFB reduces the rate of sample and reagent consumption, and automates the analysis of assays. In this paper, we provide the first assessment of the security vulnerabilities of DMFBs. We identify result-manipulation attacks on a DMFB that maliciously alter the assay outcomes. Two practical result-manipulation attacks are shown on a DMFB platform performing enzymatic glucose assay on serum. In the first attack, the attacker adjusts the concentration of the glucose sample and thereby modifies the final result. In the second attack, the attacker tampers with the calibration curve of the assay operation. We then identify denial-of-service attacks, where the attacker can disrupt the assay operation by tampering either with the droplet-routing algorithm or with the actuation sequence. We demonstrate these attacks using a digital microfluidic synthesis simulator. The results show that the attacks are easy to implement and hard to detect. Therefore, this work highlights the need for effective protections against malicious modifications in DMFBs.