Visualization
From Riots To Blackouts, The World’s Fight For Cybersecurity
摘要： Hyperbole aside, what has been clear for some time is that global digital footprint across all metrics is expanding. However a growing digital landscape has also resulted in greater potential opportunities to digital vulnerabilities and attacks.
隨著人工智慧開始進入智慧型手機開始提高安全性
摘要： 因為越來越多複雜的決策被轉移到機器上，較小的模型越容易使惡意攻擊者欺騙AI系統
大數據安全領域獨角獸：比特信安數據脫敏系統
摘要： 當前大數據、雲計算等新技術層出不窮，大數據離我們的生活越來越近，已成為新一代科技革命的風向標。對現代數據中心而言，雲計算、大數據的興起，必須依據新形勢、新特點適時創造新的安全防護系統和安全體系才能應對數據資產集中化和大數據化所帶來的嚴峻安全挑戰。
安全領域中機器學習的對抗和博弈
摘要： 回顧過去的幾年，機器學習在安全領域有不少應用，但其處境卻一直比較尷尬：一方面，機器學習技術在業內已有不少成功的應用，大量簡單的重複性勞動工作可以很好的由機器學習算法解決。
用大數據解決網絡安全分析
摘要: 一、引言單純的防禦措施無法阻止蓄意的攻擊者，這已經是大家都認同的事實，應對挑戰業界有了諸多方面的探索和實踐，而其中最有趣的就非安全分析莫屬了，圍繞著安全分析展開，我們可以看到大數據、數據挖掘、可視化...
全球有1,800個銀行程式遭銀行木馬鎖定
摘要： 據資安業者Zimperium調查，今年鎖定銀行行動程式的惡意程式數量以及攻擊規模，比去年高了三倍
一場電影比賽，催生估值2億的AI影片生成王者！Pika僅4人憑什麼超前Adobe？
摘要： 剛成立6個月左右的Pika Labs宣布，該公司在最新一輪融資募集到3500萬美元，截至目前的已累計5500萬美元的資金，其估值更突破2億美金。
How to Modernize Legacy Applications
摘要： Legacy applications are the older pieces of software that you use to run your business—these applications have been around for a while, and times have changed since they were created. While some will work fine for a while, they will inevitably need to be reevaluated at some point, which is when it’s time to evolve your legacy apps with confident cloud migration strategies. To help ease your modernization efforts, this article will provide a basic overview of when and how to do so. If you feel it’s time to modernize one of your legacy applications, ensure you either have a well-trained and experienced IT team in place or contact an IT company if needed to help ease this process.
2023 年值得关注的 7 大数据质量趋势
摘要： 要发挥作用，数据必须准确、完整、有效、唯一并且按照标准化格式进行结构化。
Data-Driven? Think again
摘要： The psychological habit most people lack and why you can’t hope to use data to guide your actions effectively without it
Key Issues Associated with Classification Accuracy
摘要： In this blog, we will unfold the key problems associated with classification accuracies, such as imbalanced classes, overfitting, and data bias, and proven ways to address those issues successfully.
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