February 26, 2018, 7:11 am
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1 Philippine Peso = 0.0709 UAE Dirham
1 Philippine Peso = 2.0666 Albanian Lek
1 Philippine Peso = 0.03436 Neth Antilles Guilder
1 Philippine Peso = 0.38512 Argentine Peso
1 Philippine Peso = 0.0246 Australian Dollar
1 Philippine Peso = 0.03436 Aruba Florin
1 Philippine Peso = 0.03861 Barbados Dollar
1 Philippine Peso = 1.59981 Bangladesh Taka
1 Philippine Peso = 0.0307 Bulgarian Lev
1 Philippine Peso = 0.00727 Bahraini Dinar
1 Philippine Peso = 33.8027 Burundi Franc
1 Philippine Peso = 0.01931 Bermuda Dollar
1 Philippine Peso = 0.02544 Brunei Dollar
1 Philippine Peso = 0.13243 Bolivian Boliviano
1 Philippine Peso = 0.06249 Brazilian Real
1 Philippine Peso = 0.01931 Bahamian Dollar
1 Philippine Peso = 1.24035 Bhutan Ngultrum
1 Philippine Peso = 0.18341 Botswana Pula
1 Philippine Peso = 386.48649 Belarus Ruble
1 Philippine Peso = 0.03857 Belize Dollar
1 Philippine Peso = 0.02437 Canadian Dollar
1 Philippine Peso = 0.01807 Swiss Franc
1 Philippine Peso = 11.38996 Chilean Peso
1 Philippine Peso = 0.12225 Chinese Yuan
1 Philippine Peso = 54.88417 Colombian Peso
1 Philippine Peso = 10.92317 Costa Rica Colon
1 Philippine Peso = 0.01931 Cuban Peso
1 Philippine Peso = 1.73147 Cape Verde Escudo
1 Philippine Peso = 0.39788 Czech Koruna
1 Philippine Peso = 3.41371 Djibouti Franc
1 Philippine Peso = 0.11689 Danish Krone
1 Philippine Peso = 0.94363 Dominican Peso
1 Philippine Peso = 2.19764 Algerian Dinar
1 Philippine Peso = 0.24563 Estonian Kroon
1 Philippine Peso = 0.34054 Egyptian Pound
1 Philippine Peso = 0.5251 Ethiopian Birr
1 Philippine Peso = 0.0157 Euro
1 Philippine Peso = 0.03853 Fiji Dollar
1 Philippine Peso = 0.01381 Falkland Islands Pound
1 Philippine Peso = 0.01382 British Pound
1 Philippine Peso = 0.08607 Ghanaian Cedi
1 Philippine Peso = 0.90347 Gambian Dalasi
1 Philippine Peso = 173.55213 Guinea Franc
1 Philippine Peso = 0.14162 Guatemala Quetzal
1 Philippine Peso = 3.93494 Guyana Dollar
1 Philippine Peso = 0.15101 Hong Kong Dollar
1 Philippine Peso = 0.45448 Honduras Lempira
1 Philippine Peso = 0.11653 Croatian Kuna
1 Philippine Peso = 1.23243 Haiti Gourde
1 Philippine Peso = 4.90965 Hungarian Forint
1 Philippine Peso = 263.76448 Indonesian Rupiah
1 Philippine Peso = 0.06723 Israeli Shekel
1 Philippine Peso = 1.25268 Indian Rupee
1 Philippine Peso = 22.85714 Iraqi Dinar
1 Philippine Peso = 718.33978 Iran Rial
1 Philippine Peso = 1.93822 Iceland Krona
1 Philippine Peso = 2.4222 Jamaican Dollar
1 Philippine Peso = 0.01364 Jordanian Dinar
1 Philippine Peso = 2.0617 Japanese Yen
1 Philippine Peso = 1.96236 Kenyan Shilling
1 Philippine Peso = 1.311 Kyrgyzstan Som
1 Philippine Peso = 76.94981 Cambodia Riel
1 Philippine Peso = 7.70077 Comoros Franc
1 Philippine Peso = 17.37452 North Korean Won
1 Philippine Peso = 20.76255 Korean Won
1 Philippine Peso = 0.00578 Kuwaiti Dinar
1 Philippine Peso = 0.01583 Cayman Islands Dollar
1 Philippine Peso = 6.1749 Kazakhstan Tenge
1 Philippine Peso = 159.87839 Lao Kip
1 Philippine Peso = 29.06178 Lebanese Pound
1 Philippine Peso = 2.99421 Sri Lanka Rupee
1 Philippine Peso = 2.50386 Liberian Dollar
1 Philippine Peso = 0.22268 Lesotho Loti
1 Philippine Peso = 0.05886 Lithuanian Lita
1 Philippine Peso = 0.01198 Latvian Lat
1 Philippine Peso = 0.0257 Libyan Dinar
1 Philippine Peso = 0.1777 Moroccan Dirham
1 Philippine Peso = 0.32037 Moldovan Leu
1 Philippine Peso = 0.96332 Macedonian Denar
1 Philippine Peso = 25.79151 Myanmar Kyat
1 Philippine Peso = 46.1583 Mongolian Tugrik
1 Philippine Peso = 0.15547 Macau Pataca
1 Philippine Peso = 6.75676 Mauritania Ougulya
1 Philippine Peso = 0.63514 Mauritius Rupee
1 Philippine Peso = 0.29614 Maldives Rufiyaa
1 Philippine Peso = 13.77317 Malawi Kwacha
1 Philippine Peso = 0.35764 Mexican Peso
1 Philippine Peso = 0.07562 Malaysian Ringgit
1 Philippine Peso = 0.22261 Namibian Dollar
1 Philippine Peso = 6.9112 Nigerian Naira
1 Philippine Peso = 0.59556 Nicaragua Cordoba
1 Philippine Peso = 0.15133 Norwegian Krone
1 Philippine Peso = 1.99853 Nepalese Rupee
1 Philippine Peso = 0.02647 New Zealand Dollar
1 Philippine Peso = 0.00743 Omani Rial
1 Philippine Peso = 0.01931 Panama Balboa
1 Philippine Peso = 0.06266 Peruvian Nuevo Sol
1 Philippine Peso = 0.06071 Papua New Guinea Kina
1 Philippine Peso = 1 Philippine Peso
1 Philippine Peso = 2.13127 Pakistani Rupee
1 Philippine Peso = 0.06552 Polish Zloty
1 Philippine Peso = 107.39382 Paraguayan Guarani
1 Philippine Peso = 0.07027 Qatar Rial
1 Philippine Peso = 0.07302 Romanian New Leu
1 Philippine Peso = 1.08832 Russian Rouble
1 Philippine Peso = 16.23803 Rwanda Franc
1 Philippine Peso = 0.07239 Saudi Arabian Riyal
1 Philippine Peso = 0.14989 Solomon Islands Dollar
1 Philippine Peso = 0.25792 Seychelles Rupee
1 Philippine Peso = 0.34575 Sudanese Pound
1 Philippine Peso = 0.15762 Swedish Krona
1 Philippine Peso = 0.02545 Singapore Dollar
1 Philippine Peso = 0.01382 St Helena Pound
1 Philippine Peso = 0.42869 Slovak Koruna
1 Philippine Peso = 147.2973 Sierra Leone Leone
1 Philippine Peso = 10.84942 Somali Shilling
1 Philippine Peso = 384.74904 Sao Tome Dobra
1 Philippine Peso = 0.16892 El Salvador Colon
1 Philippine Peso = 9.9417 Syrian Pound
1 Philippine Peso = 0.22262 Swaziland Lilageni
1 Philippine Peso = 0.60579 Thai Baht
1 Philippine Peso = 0.04633 Tunisian Dinar
1 Philippine Peso = 0.04271 Tongan paʻanga
1 Philippine Peso = 0.07317 Turkish Lira
1 Philippine Peso = 0.12974 Trinidad Tobago Dollar
1 Philippine Peso = 0.56444 Taiwan Dollar
1 Philippine Peso = 43.35907 Tanzanian Shilling
1 Philippine Peso = 0.52008 Ukraine Hryvnia
1 Philippine Peso = 70.40541 Ugandan Shilling
1 Philippine Peso = 0.01931 United States Dollar
1 Philippine Peso = 0.54923 Uruguayan New Peso
1 Philippine Peso = 157.72201 Uzbekistan Sum
1 Philippine Peso = 558.39769 Venezuelan Bolivar
1 Philippine Peso = 438.97684 Vietnam Dong
1 Philippine Peso = 2.05502 Vanuatu Vatu
1 Philippine Peso = 0.04818 Samoa Tala
1 Philippine Peso = 10.28822 CFA Franc (BEAC)
1 Philippine Peso = 0.05212 East Caribbean Dollar
1 Philippine Peso = 10.28822 CFA Franc (BCEAO)
1 Philippine Peso = 1.87297 Pacific Franc
1 Philippine Peso = 4.82336 Yemen Riyal
1 Philippine Peso = 0.22278 South African Rand
1 Philippine Peso = 100.1834 Zambian Kwacha
1 Philippine Peso = 6.98649 Zimbabwe dollar

CTO reflections: Beyond the appliance

Michael Xie,Founder, President and CTO, Fortinet

FOR anyone reading the news regularly, it’s not hard to grasp that cyber threats are getting more sophisticated and damaging by the day. From a security technology provider’s perspective, I can add that tackling them is a fast mounting challenge for the millions of businesses that come under attack daily.

Modern cybersecurity technologies – assuming you have already put in place the right professionals, policies and processes − are a must but organizations deploying them need to look beyond the boxes that sit on their racks.

What underpins the security appliances is invisible, but plays a pivotal role in ensuring that those boxes block the threats that imperil your business. Threat intelligence − or more specifically, the security appliances’ ability to know the ins-and-outs of the evolving threat landscape and respond to them appropriately – is the fuel that powers your cyber defenses.

Getting timely, accurate and predictive threat intelligence is much tougher than it sounds. It calls for a robust R&D set-up, which comprises a few components:

Divide and conquer − In many aspects of business, large teams equate to large outputs. When trying to outsmart well motivated cybercriminals, however, following conventional wisdom seldom works well. In my experience, an effective threat research organisation should be made up of many small teams, with each team dedicated to a particular type of threat. Creating such research focuses boosts each team’s specialization and competency − leading to faster discovery of threats, and the identification of more threats − while shortening customer response times to incidents.

Stay fleet-footed − Threat research teams must be nimble. The threat landscape is highly dynamic, changing by the day, or even hours and minutes. The teams must be able to adjust their priorities and refocus on the fly. At Fortinet, for instance, based on our projections of how the threat landscape will evolve, research plans are updated. From the new directions identified, researchers with the most appropriate skill sets are selected to join specific task forces to delve into those emerging threats.

Examples of such threats in recent times include IoT, ransomware and autonomous malware.

See the big picture − Researchers must be encouraged to think big and pursue their own interests, even if those interests don’t have a direct link to the company’s products. Research on IoT vulnerabilities, for instance, can deepen an enterprise security provider’s understanding of the threat landscape.

Hone your instincts − Research leaders must train their teams to develop the acumen to identify a threat as important before that fact becomes obvious to all. Good threat researchers, for instance, have been warning for years that IoT vulnerabilities are the next big menace − before the Mirai IoT botnet appeared last September and made it plain to the world. Threats emerge and evolve swiftly. If a security provider is slow to research on them and react, its customers will be slow to get protected.         
Amass data – The more data a threat research team has access to, the greater the potential of its research outcome. Enlightened research organizations share – not hoard – information. At Fortinet, for example, beyond tapping the 3 million sensors we have deployed around the globe, we actively exchange threat intelligence with organizations like INTERPOL, NATO, KISA and other security technology providers through the Cyber Threat Alliance. In recent months, we have also succeeded in bringing on board more government entities and carriers globally. That’s a positive development, as it helps all parties build a bigger threat database to monitor, block and trace malware back to their sources.

Invest in research technology – The days of manually analyzing threat information have long passed us by. Effective research teams need advanced tools to interpret and correlate the reams of data coming through to them every second. While today we have Content Pattern Recognition Languages (CPRLs) to help identify thousands of current and future virus variants with a single signature, the future belongs to technologies like big data analytics and artificial intelligence. Soon, AI in cybersecurity will constantly adapt to the growing attack surface. Today, human beings are performing the relatively complex tasks of connecting the dots, sharing data and applying that data to systems. In future, a mature AI system will be able to automate many of these complex decisions on its own.

No matter how advanced AI becomes, however, full automation – or the passing of 100% of the control to machines to make all the decisions all the time – is not attainable. Human intervention will still be needed. Big data and analytics platforms allow malware progression to be predicted but not malware mutation. Only the human mind could have foreseen that ransomware like Wannacry would embed the National Security Agency’s vulnerability exploits to propagate on unpatched systems.

Malware evolution will intrinsically follow human evolution and how people blend new technologies into their everyday life. If in the coming years, for instance, self-driving cars and wearable IoT find widespread adoption, cybercriminals will – as they have always done – find ways to ride the wave and exploit those cars and devices. Likewise, cryptocurrencies, if they continue to find favor at the rate they gained momentum this year, will attract herds of hackers.

The concept of automation is opening up many new possibilities for cybercriminals, and turning up the heat on organizations. As hackers step up the amount of automation in their malware, attacks will not only come at organizations faster, they will also reduce the time between breach and impact, and learn to avoid detection. Increasingly, firms will need to respond in near real time − in a coordinated fashion across the distributed network ecosystem, from IoT to the cloud. Not many enterprises have the capability to do this today, and that’s something CIOs should start worrying about.
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